Diffusion-MRI in neurodegenerative disorders

    Diffusion-MRI in neurodegenerative disorders Joseph Goveas, Laurence O’Dwyer, Mario Mascalchi, Mirco Cosottini, Stefano Diciotti, Silvia De Santis, Luca Passamonti, Carlo Tessa, Nicola Toschi, Marco Giannelli PII: DOI: Reference:

S0730-725X(15)00104-6 doi: 10.1016/j.mri.2015.04.006 MRI 8356

To appear in:

Magnetic Resonance Imaging

Received date: Revised date: Accepted date:

7 June 2014 18 April 2015 19 April 2015

Please cite this article as: Goveas Joseph, O’Dwyer Laurence, Mascalchi Mario, Cosottini Mirco, Diciotti Stefano, De Santis Silvia, Passamonti Luca, Tessa Carlo, Toschi Nicola, Giannelli Marco, Diffusion-MRI in neurodegenerative disorders, Magnetic Resonance Imaging (2015), doi: 10.1016/j.mri.2015.04.006

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Diffusion-MRI in neurodegenerative disorders Joseph Goveas1, Laurence O’Dwyer2, Mario Mascalchi3,4, Mirco Cosottini5,6, Stefano Diciotti7, Silvia

T

De Santis8, Luca Passamonti9, Carlo Tessa10, Nicola Toschi11,12,13, Marco Giannelli14 1

RI P

Department of Psychiatry and Behavioral Medicine, and Institute for Health and Society, Medical College of

Wisconsin, Milwaukee, WI, U.S.A.

Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt, Germany

3

Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy

4

Quantitative and Functional Neuroradiology Research Program at Meyer Children and Careggi Hospitals of Florence,

SC

2

MA NU

Florence, Italy 5

Deparment of Neurosciences, University of Pisa, Pisa, Italy

6

Unit of Neuroradiology, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy

7

Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna,

ED

Cesena, Italy

Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK

9

Neuroimaging Research Unit, National Research Council, Catanzaro, Italy

PT

8

Division of Radiology, “Versilia” Hospital, AUSL 12 Viareggio, Lido di Camaiore, Italy

11

Department of Biomedicine and Prevention, Medical Physics Section, University of Rome “Tor Vergata”, Rome,

Italy

CE

10

Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, U.S.A.

13

Harvard Medical School, Boston, MA, U.S.A.

14

Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy

AC

12

Corresponding author: Marco Giannelli - Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Via Roma 67, 56126 Pisa, Italy - Phone: 0039 050993359, Fax: 0039 050992513, e-mail: [email protected]

Type of article: Review article

Key words: MRI, diffusion, DTI, brain, neurodegenerative disorders, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, ataxias

ACCEPTED MANUSCRIPT Abstract: The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance

RI P

T

imaging (MRI), “diffusion” (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly

SC

quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which

MA NU

could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various

ED

neurodegenerative disorders including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral

AC

CE

PT

sclerosis, Huntington’s disease and degenerative ataxias.

ACCEPTED MANUSCRIPT 1. Introduction

The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to

T

play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance

RI P

imaging (MRI), “diffusion” (i.e. the random, thermally-induced displacements of water molecules over time) [1] represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords

SC

has proven useful in a vast number of clinical as well as research applications, especially in neuroimaging

MA NU

[2]. Currently, diffusion-MRI is the only technique which allows in vivo, non-invasive characterization of diffusion processes, which are fundamental phenomena in living tissue. As such, the information they provide is closely related to underlying cell physiology as well as tissue microstructure. In particular, the overall diffusivity as well as anisotropic properties of diffusion in the brain are strictly related to a number of tissue properties such as microstructural complexity, membrane permeability and integrity as well as axonal

ED

ordering, axonal diameter, axonal density and myelination [3].

PT

Since diffusion-MRI is a truly quantitative imaging technique [4], the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as

CE

tracking of subtle changes in follow-up examinations and clinical trials. In addition, diffusion-MRI has shown the potential to provide access, through use of fiber tracking techniques [5], to information about

AC

structural brain connectivity [2]. Accordingly, diffusion-MRI has proven useful in obtaining information to better characterize the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, Huntington’s disease and degenerative ataxias.

ACCEPTED MANUSCRIPT 2. Alzheimer’s disease (AD)

Alzheimer’s disease is a progressive neurodegenerative disorder characterized by the presence of significant

RI P

T

irreversible brain damage at the time of clinical manifestation. An estimated 35 million people worldwide are living with this disease. The available treatment options have shown only modest clinical benefit in slowing disease progression, and the potential benefits are short-lived. While AD-modifying agents are currently in

SC

various phases of clinical trials, several have failed to show benefit. If effective treatments are not discovered

MA NU

in a timely fashion, the number of AD cases is anticipated to rise to 113 million by 2050 [6]. This warrants an urgent need to develop early neuroimaging biomarkers of AD neuropathology that can detect and predict the disease before the onset of dementia. These neuroimaging measures may serve as markers that can monitor therapeutic efficacy in halting or, at the very least, slowing down disease progression in the earlier stages of the illness. The earlier stages of AD may be broadly divided into two phases [7, 8]: 1) preclinical

ED

phase, where there are no observable clinical symptoms, and 2) prodromal phase, described as mild cognitive

PT

impairment (MCI), where mild symptoms are present while everyday function is still preserved. While AD is historically described as a disorder of brain gray matter (GM), white matter (WM) injury has

CE

also been identified early in the disease course. Postmortem investigations have revealed significant WM changes in AD, including loss of oligodendrocytes, microglial activation, breakdown of ventricular lining

AC

and loss of myelinated axons in deep WM [9-12]. Moreover, biochemical analyses of WM in AD have demonstrated increased quantities of Aβ40 and Aβ42, and total fatty acid content accompanied by decreased myelin basic protein, myelin proteolipid protein and cholesterol levels [13]. Two putative theories have been hypothesized as potential explanations underlying WM vulnerability in AD. The first theory, termed retrogenesis, postulates that tracts that are late to myelinate in ontogenetic development are among the earliest to be affected by loss of oligodendrocytes and myelin breakdown in AD [14, 15]. Interestingly, myelin breakdown has been related to the promotion of Aβ oligomerization in AD [16]. The second theory, wallerian degeneration, posits that axonal damage mirrors adjacent GM atrophy, and that the progression of WM tissue vulnerability parallels GM pathology [17]. In this regard, diffusion-MRI has provided further insights into the microstructural vulnerabilities associated with preclinical, prodromal and syndromal AD.

ACCEPTED MANUSCRIPT 2.1 Diffusion-MRI in MCI and AD

Diffusion tensor imaging (DTI) is a diffusion-MRI technique which is based on a Gaussian model of

RI P

T

diffusion processes, and fractional anisotropy (FA) and mean diffusivity (MD) are rotationally invariant DTI-derived indices which measure the degree of directionality of diffusion and overall diffusivity in tissue, respectively [18]. Therefore, FA and MD have been used as sensitive measures of water diffusion in the

SC

biological tissue, thereby permitting the in vivo probing of microstructural properties/alterations of WM

MA NU

which is characterized by anisotropic diffusion (i.e. the diffusion along fiber bundles, axial diffusivity (DA), is greater than diffusion orthogonal to fiber bundles, radial diffusivity (DR)). In general, AD has been associated with widespread reductions in FA and increases in MD in several regions, most prominently in the frontal and temporal lobes, and along the cingulum, corpus callosum, uncinate fasciculus (UF), superior longitudinal fasciculus and medial temporal lobe (MTL)-associated tracts [19-22]. In accordance with the

ED

retrogenesis theory, in some DTI studies individuals with AD are reported to have greater WM abnormalities

PT

in the late-myelinating association fiber pathways in the initial stages of the disease, compared with earlymyelinating fibers [23, 24]. On the other hand, some DTI studies have concluded that the patterns of WM

CE

vulnerability seen in AD are reflective of the wallerian degeneration process [25, 26]. MCI is considered a prodromal stage of AD, as individuals suffering from this neuropsychiatric syndrome

AC

are at a higher risk for conversion. FA and MD differences between MCI and healthy controls largely parallel the results seen in AD, but they are not as extensive. A recent meta-analysis showed a widespread decrease of FA in AD, which however excluded the parietal WM and internal capsule in AD. In contrast, the regions spared in MCI were the parietal and occipital lobes. Similarly, in contrast to pervasive MD alterations in AD, in MCI patients frontal and occipital regions were spared [27]. In another recent study [28], the usefulness of FA values in the fornix was comparable to that of hippocampal volume in predicting MCI conversion to AD. Moreover, in MCI patients, increases of MD in the fornix may be a better indicator of change and disease progression when compared to FA [29]. On the other hand, several studies have demonstrated conflicting FA and MD results in MCI and AD. For instance, some studies have revealed no changes in MD values in the frontal and parietal lobes [30], corpus callosum [31-33] and posterior cingulum [33] in patients with AD. Others studies have shown MD increases only in AD (and not in MCI) [30, 34].

ACCEPTED MANUSCRIPT Similarly, while reduced FA in the MTL and cingulum fibers are more consistently reported in AD, others have failed to demonstrate differences between AD, MCI and cognitively healthy controls [35]. Also, results have been equivocal in MCI, with FA results ranging from no significant changes [36, 37] to reductions

RI P

T

isolated to posterior cingulum only [32], to more widespread reductions in FA [38, 39]. In this regard, some have suggested that FA alone may be an insensitive measure for revealing early WM microstructural changes or for assessing clinical severity in AD patients [40, 41]. Since DA and DR may provide valuable

SC

information about the underlying axonal and myelin damage associated with MCI and AD [42, 43], a

MA NU

comprehensive analysis of multiple DTI-derived indices has therefore been proposed to improve our understanding regarding the microstructural WM fiber architecture of MCI and AD. By combining multiple DTI-derived indices, recent tract-based spatial statistics (TBSS) [44] investigations have attempted to distinguish between WM tract changes driven by wallerian degeneration versus the retrogenesis models of MCI and AD [23, 45-47] (Fig. 1). In particular, TBSS analysis in AD

ED

patients demonstrated widespread decreases in WM FA in tracts including the cingulum, UF, inferior and

PT

superior longitudinal fasciculus, corpus callosum, fornix and parahippocampal WM. FA abnormalities in MCI were found to be of intermediate entity between controls and individuals with AD [48]. Increased MD,

CE

DA and DR values predominantly involving the tracts associated with the memory circuit were also identified in AD (relative to healthy controls). Increases in absolute diffusivity measures were more

AC

widespread than FA reductions, and in AD WM disruptions were mostly adjacent to areas with reduced GM densities [40]. Also reduced FA and higher DR, likely reflecting reduced myelination predominantly affecting the late-myelinating association WM fibers, have been demonstrated in AD, supporting the theory of retrogenesis [23]. Another study in AD has suggested wallerian degeneration-related microstructural changes in the posterior subregions of the corpus callosum and retrogenesis associated differences in the anterior corpus callosum [46]. In a longitudinal study by Acosta-Cabronero et al. [49], increased DA and MD in the splenium were the earliest abnormalities to occur in AD. These changes remained static over time, suggesting that these measures are more sensitive in the earlier phases of the disease. On the contrary, FA and DR became increasingly abnormal over time (Fig. 2), suggesting that these are superior measures of disease progression and more advanced WM degeneration. Recent evidence also lends support to the hypothesis that DA is a

ACCEPTED MANUSCRIPT more sensitive marker of early microstructural WM changes in MCI when compared to DR and FA indices. Significantly higher DA and, to a lesser extent, MD in the left prefrontal cortex involving the forceps minor and UF were demonstrated in MCI, whereas widespread changes in multiple diffusion indices, which are

RI P

T

likely related to gross WM tissue loss, were seen in AD with respect to healthy controls [47]. Furthermore, when compared to healthy subjects, patients with MCI showed significantly greater DA in the corticocortical and limbic WM connections, with no changes in FA, MD and DR values.

SC

While correlations between GM atrophy and DTI-derived indices were found in AD, no associations

MA NU

between DTI-derived indices and GM density measures were seen in individuals with MCI [50]. In another study by Alves et al. [51], widespread FA decreases were found in MCI and AD. In particular, the number of regions with decreased FA diminished after adjusting for GM atrophy, although certain areas, especially in the left corpus callosum and UF, remained significant. Others also have reported similar findings in MCI and AD [45]. Taken together, these findings point to the possibility that a primary WM degenerative process is

ED

involved in the early phases of the disease, whereas secondary neurodegeneration plays a more prominent

PT

role in the more advanced stages of AD. In this regard, there is experimental as well as pathological evidence, in both humans and animal models, indicating that neuronal populations affected in AD follow a

CE

dying-back pattern of neuronal degeneration, where substantial reductions in synaptic function and axonal connectivity long precede neuronal cell death [52-56]. This supports the importance of looking at WM

AC

changes as a potentially sensitive approach in the assessment of early AD. Notably, FA and DR differences in the corpus callosum, cingulum and fornix were found to separate individuals with MCI who converted to AD from non-converters [57]. Also, the WM disruptions in the frontal and temporal WM, and in specific tracts including the fornix, cingulum, the genu and splenium of the corpus callosum, have been associated with multiple domain cognitive impairments in MCI and early AD. In a longitudinal study which included healthy controls and subjects with subjective or mild cognitive impairment, DTI was found to be a better predictor of AD-specific MTL atrophy when compared to cerebral spinal fluid (CSF) biomarkers [58]. These novel findings have provided evidence for the potential clinical utility of DTI measures as early biomarkers of AD and its progression. While DTI, which is based on a Gaussian diffusion model [59], is a well-established and commonly employed diffusion-MRI technique in clinical/research neuroimaging studies, it suffers from important

ACCEPTED MANUSCRIPT limitations in a number of pathophysiologically relevant situations like the presence of mixed tissue types, WM fibers which cross within the same voxel, or non-monoexponential signal decay of diffusion-weighted images (DWIs) as a function of b-value [5, 60, 61]. Given that water diffusion in tissue is not free (due to the

T

effect of tissue microstructure – e.g. cell membranes and organelles) which creates diffusion barriers and

RI P

compartments [3], non-Gaussian techniques [62], which employ high b-values (> 2000 s/mm2) in order to probe different diffusion dynamics, may augment the accuracy and hence the diagnostic potential of

SC

diffusion-MRI, allowing a better understanding of neurodegenerative processes underlying AD as well as

MA NU

other neurodegenerative diseases. In particular, diffusional kurtosis imaging (DKI) is a recent and promising diffusion MRI technique, which can be performed within the timeframe of clinical examinations on conventional 1.5T MRI scanners [63, 64] and has proven to be potentially useful in the study of various brain diseases [65-69]. DKI extends conventional DTI by estimating the diffusional kurtosis [70] (DK), which represents the non-Gaussian component of water diffusion (i.e. the deviation of the diffusion displacement

ED

profile from a Gaussian distribution). Based on theoretical models and phantom experiments [70, 71], it can

PT

be shown that a large DK is likely to reflect a high degree of microstructural “complexity”, which in turn is related to the number, orientation and permeability of diffusion barriers (e.g. myelin sheets) as well as to the

CE

presence of various cell types and organelles (e.g. neurons and glial cells, axons, dendrites, neurofilaments, microtubules). Also, DK tends to increase with diffusion “heterogeneity” (i.e. the presence of distinct water

AC

compartments with different diffusion properties – e.g. intra-/extra-axonal compartment), and can be altered by water exchange between diffusion compartments or by diffusion barriers. DKI can provide independent and complementary information to that acquired by employing conventional DTI, with potentially improved sensitivity to pathological changes as well as better characterization of various brain regions [72]. In particular, DKI seems to outperform DTI in the study of relatively isotropic GM regions [63, 73-76], where DTI-derived information about anisotropy is of limited usefulness. In addition, it should be noted that, when combined with proper biophysical models of GM as well as WM [77-80], DKI could provide further insight into microstructural tissue organization as well as into the neurobiological interpretation of its changes. Recently, Falangola et al. [69] have employed both DKI and DTI to investigate changes in brain tissue microstructure in a limited group of patients with MCI and AD as well as in cognitively intact controls. Receiver operating characteristic (ROC) analysis identified mean and radial DK in the anterior corona

ACCEPTED MANUSCRIPT radiata as the best individual discriminators between MCI patients and controls with an area under the ROC curve (AUC) of 0.8 for mean DK and of 0.82 for radial DK. Moreover, DA in the hippocampus was the best overall discriminator of MCI from AD patients, with an AUC of 0.9. Using the DKI method, Gong et al. [81]

RI P

T

have revealed decreased mean/radial DK in the parietal lobe WM and axial DK in occipital lobe GM in AD with respect to MCI. Also, axial DK and mean DK were shown to significantly correlate with mini-mental state examination (MMSE) scores in all frontal, temporal, parietal and occipital lobes of both WM and GM.

SC

Fieremans et al. [82] have shown that DKI-derived metrics specifically related to WM tract microstructure

MA NU

(i.e. axonal water fraction, intra-axonal diffusivity, extra-axonal axial and radial diffusivities) [78] can significantly differentiate healthy controls and patients with amnestic MCI and AD, yield information regarding the underlying mechanisms of WM degeneration and correlate with processing speed. These preliminary studies suggest that non-Gaussian diffusion-MRI may be beneficial in the assessment of microstructural tissue damage in early MCI and may be useful in developing biomarkers for the clinical

ED

staging of AD.

PT

Deterministic as well as probabilistic fiber-tracking techniques [5] have been utilized in MCI and AD [8390] with potential advantages. In particular, diffusion-MRI tractography may improve the spatial localization

CE

of various diffusion indices along the length of the major limbic and cortico-cortical association WM fiber pathways (Fig. 3). In addition, probabilistic tractography may be suitable to adequately evaluate diffusion

AC

changes in areas of crossing-fibers [88], where the DTI model (which is based on a single-fiber population model) is inappropriate.

2.2 Diffusion-MRI in individuals at risk for AD

Increasing age, APOE e4 and parental family history are the major risk factors for late-onset AD. Recent evidence has demonstrated microstructural WM changes associated with these risk factors in middle-aged and older adults. An increase in MD and DR and a decrease in FA with advancing age in selective brain regions have been previously reported [91, 92]. It is increasingly being recognized that genetics plays an important role in WM integrity and age-related decline. Furthermore, APOE e4 allele, which is the major genetic risk factor for sporadic AD, has been associated with decreased FA in the cingulum, corpus callosum

ACCEPTED MANUSCRIPT and the medial temporal lobe WM [93-96]. Bendlin et al. [97] stratified a large group of subjects according to family history and APOE e4, demonstrating a lack of main effects of APOE e4 allele in cognitively healthy middle-aged adults, whereas the parental history of AD was related to decreased FA in brain regions

RI P

T

commonly affected in AD, including the cingulum, corpus callosum, UF and hippocampus WM. In addition, they also found interactive effects between family history and APOE e4. In particular, participants who had a positive family history and were also APOE e4 carriers had the lowest FA relative to other groups. The CSF

SC

AD biomarkers were correlated extensively with absolute diffusivity indices (MD, DR, DA) in several WM

MA NU

regions, and were more prevalent in the frontal, temporal and parietal lobes, in middle-aged individuals with a parental family history of AD [98]. On the other hand, Adluru et al. have found higher regional FA in the genu and superior longitudinal fasciculus in individuals with parental history of late-onset AD, while older age was associated with lower FA in the genu, fornix and cingulum. Also, APOE e4 non-carriers with a parental history of AD showed lower DA in the UF, while individuals with both risk factors for AD had

ED

higher DA in this tract [99]. Moreover, higher FA in multiple brain regions and lower MD in lateral frontal

PT

GM were observed in cognitively healthy individuals with greater amyloid deposition [100]. This suggests that WM microstructural disruption follows a more complex non-linear pattern than previously thought,

CE

which is based on the presence of various risk factors in presymptomatic AD stages. WM pathology associated with AD could be potentially detected several years prior to the onset of cognitive decline,

AC

although further investigations are needed to assess the patterns of microstructural changes that may occur in preclinical stages of late-onset AD.

2.3 Whole-brain structural network disruptions in AD

AD has increasingly been recognized as a disconnection syndrome where loss of cortico-cortical and cortical-limbic WM structural connections occurs, resulting in multidomain cognitive impairment. Structural connectivity in the human brain refers to the interconnections between brain regions formed by neurons by means of WM tracts. There is an increasing body of evidence demonstrating that AD is characterized by large-scale structural disruptions of the human brain. Since the notion of the human connectome has been introduced to define the structural and functional connectivity of the brain, system-level methods have been

ACCEPTED MANUSCRIPT used to study the pathogenesis of prodromal and syndromal AD. Graph theoretical methods [101] have been used in DTI datasets to demonstrate the presence of abnormal topological properties in whole-brain structural brain networks in MCI and AD. In the framework of graph theory, the brain is idealized as a

RI P

T

network (i.e. graph) which is composed of a set of nodes (i.e. brain regions) connected by links (which represent the "connectivity" between regions). In complex networks, there are usually more highly connected nodes (termed hubs) than one would expect to find in a randomly connected graph. In particular, such

SC

complex networks are termed small world networks when most nodes can be reached from every other node

MA NU

through a small number of steps [101]. The graph-theoretical approach allows the analysis of several quantitative measurements which characterize graph topology, for example the amount of small-world-ness and global or local network efficiency [102]. Using these methods, Lo et al. [103] demonstrated increased shortest path length (defined as the path between two nodes with the smallest number of links) and decreased global efficiency (defined as the inverse of the harmonic mean of the shortest path length between each pair

ED

of nodes within the network) in the WM network of patients with AD when compared to controls, implying

PT

abnormal small-world topology and pointing to a loss of efficiency in communication between distinct brain regions. They also identified decreased nodal efficiency (which is defined as the inverse of the harmonic

CE

mean of the shortest path length between a node and all other nodes in the network and quantifies the importance of the node for communication within the network), mainly in several frontal and temporal

AC

regions in AD. Moreover, this study showed how the disruption of such large-scale structural networks may be associated with higher-level cognitive impairments. Interestingly, disrupted global topological organization (relative to control subjects) was also found in MCI-multiple cognitive domain subtype (MCIMD), but not in MCI-single domain (MCI-SD) subtype [104]. Individuals with MCI-MD showed decreased network efficiency relative to MCI-SD, and this finding was most pronounced in the frontal cortex. Disrupted network topology was also associated with cognitive impairment. These findings suggest that global changes in brain network topology is seen in late-MCI whereas more subtle and localized changes in brain network topology are seen in earlier stages of AD or when cognitive impairment in MCI is limited to one domain. Using DTI tractography, Brown et al. [105] identified an increased age-related loss of mean local interconnectivity, and decreased regional local interconnections in the precuneus, medial orbitofrontal and lateral parietal cortices in APOE e4 carriers, relative to non-carriers. More recently, increased amyloid

ACCEPTED MANUSCRIPT burden (measured by using florbetapir PET imaging) was found to be related to change in large-scale cortical network architecture of the brain (measured by using graph theoretical metrics of DTI tractography), even in the preclinical stages of AD [106]. These studies lend additional support to the idea of using novel DTI-

RI P

T

based analytic approaches to further our understanding of the neurobiological mechanisms underlying AD and to better evaluate the utility of this MRI method as a potential biomarker for early identification of the

SC

disease as well as to assess disease progression.

MA NU

2.4 The usefulness of diffusion-MRI for classification of various stages of AD

There is also a growing interest in utilizing diffusion-MRI datasets to classify patients with MCI and AD. Machine learning techniques can be potentially employed in leveraging the information contained in diffusion-MRI indices for automated patient classification and prediction of disease states in

ED

neurodegenerative disorders. In particular, machine learning algorithms have been recently applied to DTI-

PT

derived indices to classify MCI subjects from healthy elderly controls [107]. Also, multimodal classification methods that combine rs-fMRI and DTI datasets seem promising in separating MCI from healthy controls -

CE

more so than what was observed with single-modality approaches [108]. These findings will open potential novel venues for clinical and research applications of DTI for early diagnosis, predicting disease progression

AC

and as an imaging marker in large-scale drug trials of preclinical and prodromal AD. Nevertheless, especially in whole-brain DTI studies, in which more than 100,000 features are usually reduced to approximately 1000, full consideration of the methodological pitfalls of combining supervised feature selection algorithms with classifiers is highly recommended [109].

3. Parkinson’s disease (PD)

Parkinson disease is a common neurodegenerative disorder, affecting approximately 1 in every 50 people over 65 in Europe [110]. It has been estimated that the prevalence of PD will significantly increase as a consequence of the progressive aging of the population [111]. At the pathological level, PD was initially defined on the basis of the characteristic loss of dopaminergic neurons in the substantia nigra (SN) pars

ACCEPTED MANUSCRIPT compacta where typical intracytoplasmatic inclusions known as Lewy bodies can be identified [112]. However, more recent research has demonstrated that PD is also associated with damage of nondopaminergic cells (e.g. cholinergic and noradrenergic neurons) and with diffuse neurodegenerative

RI P

T

processes that extend beyond the SN pars compacta. Given that Lewy bodies can be detected earlier in the medulla and/or olfactory bulb with respect to the SN pars compacta [112], some authors have argued that the SN may not be the first region to be involved in the neurodegenerative processes underlying PD.

SC

At the clinical level, the hallmarks of PD are typical motor symptoms that include resting tremor, balance

MA NU

problems, limb rigidity, bradykinesia and gait abnormalities. However, it is now widely recognized that PD also includes several non-motor symptoms such as mood and sleep disorders, cognitive deficits, sensory and pain disturbances, olfactory impairments, and autonomic nervous system dysfunctions. Furthermore, it has been estimated that the incidence of cognitive deficits in PD ranging from MCI to frank dementia is six-fold compared to that in the general population [113]. The typical neuropsychological pattern of the cognitive

ED

impairments in PD includes dysfunction of executive functions and of a set of cognitive processes such as

(PFC)-striatal circuits [114].

PT

planning, working memory and attention that have been linked to the function of specific prefrontal cortex

CE

At the clinical and neuropathological level, PD is an highly complex and heterogeneous neurodegenerative disorder that poses a great diagnostic and therapeutic challenge. In this regard, advanced

AC

neuroimaging techniques have found clinical application in the differential diagnosis between PD and other parkinsonian disorders such as progressive-supranuclear-palsy (PSP), cortico-basal degeneration and vascular parkinsonisms [115]. There have been considerable efforts to study PD patients in vivo and an important contribution has been provided by MRI. The development of various quantitative MRI measures [116] has allowed a more objective assessment of the tissue alteration and damage that may be present in PD, and has shed new light on the core neurodegenerative mechanisms underlying PD. In particular, diffusionMRI represents a promising noninvasive biomarker for PD that could allow to improve the MR-based differential diagnosis among parkinsonisms as well as to reliably identify PD patients on an individual basis (which is an issue of particular importance in the early stages of the disease). Also, diffusion-MRI could be a useful tool to elucidate the neural correlates of specific symptoms in PD. Furthermore, diffusion-MRI has the

ACCEPTED MANUSCRIPT potential to track the progression of the pathological process and could therefore be used for evaluating treatment response.

RI P

T

3.1 Results and potential of diffusion-MRI in PD

In cross-sectional studies, diffusion-MRI has proven effective in improving the accuracy of MRI in

SC

differential diagnosis of PD and parkinsonism such as progressive PSP [117, 118], multi-system atrophy

MA NU

(MSA) [117, 119, 120] and cortico-basal degeneration (CBD) [121]. In particular, it has been shown that these parkinsonian disorders can be discriminated by DTI-derived indices (MD and FA) in the basal ganglia, cerebellum [117, 118 , 119, 120], SN [122] and corpus callosum [121]. In addition, the joint use of linear, volumetric and DTI measures in a decision-tree approach has been demonstrated to reliably differentiate PD from MSA [123].

ED

Significant differences in DTI-derived metrics between PD patients and healthy individuals have been

PT

reported in several studies. Some authors have focused on areas of a priori relevance to the disease, such as the SN and its projections, and have used a region of interest (ROI) approach. For instance, one study has

CE

reported reduced FA in PD patients, relative to healthy controls, in a brain region corresponding to the ascending nigro-striatal fibers [124], while other studies have found decreased FA values within the SN itself

AC

[125]. Vaillancourt et al. [126] have reported that estimating FA in the substantia nigra allows to distinguish a group of 14 drug naïve de novo PD patients from 14 healthy controls with sensitivity and specificity of 100%. Furthermore, in a study with a relatively small number of subjects (10 PD patients and 10 healthy subjects), Menke et al. [127] have suggested that combining SN volumetry and the DTI-derived connectivity profile of SN with the thalamus can improve the sensitivity (100%) and specificity (80%) in classifying patients with PD (as compared to SN volumetry and connectivity profile with thalamus alone). Given the role played by iron accumulation in the pathophysiological mechanisms underlying PD [128-130], some MRI studies have combined R2* relaxation rate [116] (which provides an indirect measure of iron content) [131] and DTI data in order to improve the discrimination between PD patients and healthy controls. In particular, Peran et al. [132] have found that PD patients showed increased R2* values in the SN, lower FA values in the SN and increased MD values in the striatum. This combination of relaxometry and diffusion

ACCEPTED MANUSCRIPT MRI indices enabled a global accuracy of 95% (area under the receiver operating characteristic curve) in distinguishing PD patients (30 subjects) from healthy controls (22 subjects). The advantages of combining R2* relaxation rate and FA values in the SN to discriminate PD patients from healthy controls was

RI P

T

confirmed in another MRI study by Du et al. [133]. In addition, the same group have reported that FA in the caudal SN is significantly decreased in PD patients at all disease stages relative to healthy controls, whereas R2* values of SN were significantly increased only in middle and late but not early disease-stages [134].

SC

Therefore, the authors suggest that FA changes may be an early marker of PD pathological changes, whereas

MA NU

the changes in R2* may be more closely related to the neurodegenerative processes underlying the clinical progression of the disease. However, recent studies have raised concerns regarding the potential use of nigral DTI-derived indices as diagnostic markers of PD. In particular, Aquino et al. reported that early PD patients, late PD patients and healthy controls differed in SN area and volume, while no significant differences were detected in MD, FA or R2* values [135]. Also, Schwartz et al. [136] found only small PD-

ED

related nigral MD changes in ROI analysis, while voxel-based analysis of MD and both ROI and voxel-based

PT

analyses of FA provided negative findings.

Several DTI studies have shown that microstructural damage in PD extends beyond the SN and its

CE

projection, as reported by pathological studies that have suggested a widespread nature of degenerative processes in PD [112]. In line with the hypothesis that diffuse and subtle changes are already present in the

AC

early stages of the disease, in a study of de novo PD patients, whole-brain histogram analysis revealed a widespread increase of FA values, which is more pronounced in patients suffering from the akinetic-rigid disease type when compared to the than tremor-dominant disease type [137]. Voxel-wise DTI studies in PD patients have described altered FA and/or MD values (Fig. 4) in a number of brain regions including frontal WM and GM [138-140], parietal WM [139, 140], corpus callosum and superior longitudinal fasciculus [138], cerebellar and orbitofrontal cortex [141], olfactory tract [141-144], internal and external capsules [140] and the cingulum [138, 145]. Although the precise neural correlates of altered diffusivity in widespread GM and WM structures are not completely known, in addition to cell loss the changes in DTI metrics may tentatively be interpreted to reflect the microstructural damage caused by the widespread deposition of inclusion bodies [145, 146].

ACCEPTED MANUSCRIPT More recently, DTI studies in PD have focused on the correlations between specific symptoms, including non-motor manifestations, and distinct brain areas. Some studies have identified significant correlations between motor scores and FA [140, 147] or MD [148] in the SN. Hattori et al. [146] have found significantly

T

decreased FA values in several major tracts in PD patients with mild cognitive impairment (PD-MCI) as well

RI P

as with dementia (PDD), but not in PD patients with “normal cognition”, as compared with control subjects. Notably, in patients with PD as a whole, significant positive correlations were identified between FA values

SC

and MMSE scores in corpus callosum, superior and inferior longitudinal fasciculus, inferior fronto-occipital

MA NU

fasciculus, uncinate fasciculus and cingulum (Fig. 5). In a PD study by Melzer et al.[149], alterations of WM DTI-derived indices were associated with cognitive disfunction and correlated with impairment in executive function, attention, learning and memory. Agosta et al. [150] have confirmed that, when compared to healthy controls and PD without cognitive impairment, PD-MCI patients showed a distributed pattern of WM abnormalities with reduced FA values in the corona radiata, in corpus callosum, and in anterior inferior

ED

fronto-occipital, uncinate, and superior longitudinal fascicule. However, no correlations between DTI-

PT

derived indices and cognitive tests were found in this study. Kamagata et al. [145] have employed diffusion tensor tractography to investigate the FA of the cingulate fiber tracts in PD patients, and found a positive

CE

correlation between MMSE scores and FA values in the anterior cingulum in patients with PD as well as PDD. In addition, Carlesimo et al. [151] have found that even PD patients without cognitive impairments

AC

showed higher hippocampal MD values relative to healthy controls. In this study, a negative correlation between hippocampal MD values and the scores on memory test was revealed. In a recent study [139], early to mid-stage non demented PD patients had widespread areas of reduced FA and increased MD in cerebral WM. In particular, FA and MD in frontal WM correlated positively and negatively (respectively) with phonemic fluency score (an index of executive function), while FA in the right splenium of the corpus callosum negatively correlated with motor scores (UPDRS-III subscale). Other studies have described a positive correlation between FA values in the frontal WM (frontal portion of the right inferior frontooccipital fasciculus) and the scores in a facial emotion recognition test (identification of sadness) [152], and a negative correlation between MD and DR values in posterior WM structures and capability in color discrimination [153].

ACCEPTED MANUSCRIPT Depression is a frequent, non-motor symptom in PD that has been recently investigated with morphometric MRI. In particular, changes in frontal and limbic brain regions have been hypothesized to underlie depression in PD [154, 155]. In one study [156] that combined morphometry and DTI tractography,

RI P

T

depressed PD patients had smaller amygdala volumes compared to healthy controls, but no abnormalities of limbic connectivity (e.g. uncinate fasciculus). Given the growing interest in applications of non-Gaussian diffusion-MRI techniques, a pilot study in PD patients [157] has suggested that DKI measures in the basal

SC

ganglia and SN have higher sensitivity and specificity than conventional DTI-derived metrics in detecting

MA NU

differences between PD patients and healthy controls. In particular, the mean DK of the ipsilateral substantia nigra showed the best diagnostic performance for the diagnosis of PD. Recently, Kamagata et al. [158] compared DTI and DKI measures in the anterior and posterior cingulum (which were segmented by means of diffusion tensor tractography) and found reduced FA as well as mean DK values in cingulate fiber tracts in PD patients relative to healthy controls, with mean DK showing better diagnostic performance. In a

ED

subsequent study by the same group [159], the authors conducted a whole-brain WM analysis of DTI and

PT

DKI data by using TBSS. FA changes in PD patients with respect to healthy controls were confined in the frontal WM, while reductions in mean DK were more widespread and encompassed frontal, parietal,

CE

occipital and right temporal WM. These preliminary results suggest a better sensitivity of DKI with respect to DTI in the assessment of the microstructural changes in PD, although further studies are needed to better

AC

characterize and asses the potentialities of this non-Gaussian technique in PD [160].

4. Amyotrophic lateral sclerosis (ALS)

Amyotrophic lateral sclerosis - the most common motor neuron disease - is a progressive disorder that involves degeneration of both upper and lower motor neurons [161]. Patients experience progressive weakness, muscle atrophy and fasciculations. In addition to these lower motor neuron manifestations, upper motor neuron (UMN) findings are evident upon clinical examination and include increased tone, exaggerated deep tendon reflexes, and pathological reflexes such as a Babinski sign [162]. The typical disease course ranges from one to five years [163]. Besides motor symptoms, a subset of patients develop cognitive disturbances or even frontotemporal dementia (FTD), indicating that ALS can involve extra-motor brain

ACCEPTED MANUSCRIPT regions. Clinical [164], immuno-histochemical [165] and pathological [166] evidences support the link between ALS and FTD as a clinically pathological spectrum described as TDP-34 protheinopathies [167]. From a pathological point of view, UMN pathology in ALS is characterized by depopulation of the Betz

RI P

T

cells in the motor cortex and by axonal loss within the descending motor pathway associated with myelin pallor and gliosis of the corticospinal tracts (CST) [168, 169]. Lower motor neuron pathology primarily affects the motor neurons within the ventral horn of the spinal cord and brainstem. Extra motor pathology is

SC

found in regions such as the frontotemporal cortex, the hippocampus and thalamus [170].

MA NU

In patients with ALS, conventional MRI is used to rule out “ALS-mimics” disorders, however it has limited value in enhancing the likelihood of a correct diagnosis [171, 172]. Advanced MRI techniques had a tumultuous development in ALS, both establishing themselves as an alternative diagnostic tool (especially in the earliest clinical stages) and serving as quantitative surrogate markers for monitoring disease progression in clinical trials [173]. In particular, in ALS the neuroimaging approach evaluates the UMN dysfunction and

ED

can be divided into two main lines of research. The first one investigates UMN impairment at the cortical

PT

level and includes conventional morphological studies [174, 175], quantitative measurement of atrophy by means of VBM [176, 177] or cortical thickness measurement through surface-based morphometry (SBM)

CE

[178]. Moreover, functional MRI [179-181] and magnetization transfer imaging [182] have been employed in order to explore the motor function/reorganization and tissue alterations at a cortical level, respectively.

AC

The second line of research is focused on the involvement of WM fiber bundles in ALS. In this field of application, diffusion-MRI constitutes an invaluable method of investigation.

4.1 Diffusion-MRI in ALS

DTI has been applied in ALS to assess CST impairment, revealing a significant increase of MD and a significant reduction of FA [183-189]. In this regard, studying the three eigenvalues of the diffusion tensor, which measure diffusion along (maximum eigenvalue, λ1) and orthogonal (medium, λ2, and minimum, λ3, eigenvalue) to the fiber bundles, can allow further insights into pathological processes underlying ALS. In particular, modifications of DTI indexes in the CST of patients with ALS are due to increases in λ2 and λ3 without changes in λ1 [184, 185]. This pattern of variation in eigenvalues resembles that reported in wallerian

ACCEPTED MANUSCRIPT degeneration both in humans and in animal models [189-191]. In ALS, axonal degeneration of motor pathways in conjunction with possible extracellular environment expansion and the proliferation of glial cells could justify the observed DTI changes.

RI P

T

Decreased FA and increased MD values along the CST were found to be related to disease severity [183, 185, 187, 192] and disease duration [183, 185], respectively. Although these findings were not confirmed in other studies [184, 187, 188, 193], DTI-based tractography demonstrated a reduction in FA in the CST of

SC

patients with ALS with a more rapid disease progression [194].

MA NU

Voxel-based DTI studies of the whole brain reported that patients with ALS display a decrease of FA values not only in the CST but also in locations beyond the motor network [176, 194, 195], such as in the corpus callosum, in the pre-motor WM, in the pre-frontal WM and in the temporal WM, and the involvement of the corpus callosum deserves particular attention in ALS. Indeed, the largest FA changes are observed in the postero-central portion of the corpus callosum, which is known to contain fibers connecting the two

ED

motor cortices [196, 197]. A concomitant increase in DR in the corpus callosum can be argued to involve

PT

wallerian degeneration of fibers rising from involved neurons of the primary motor cortex. A meta-analysis of voxel-wise DTI studies confirms the idea that ALS is a multisystem disease beyond motor dysfunction

CE

[198]. Like voxel-based morphometry studies of the cerebral cortex [199, 200], DTI studies with voxel-based analysis in patients with ALS also revealed the involvement of extra-motor areas related to cognitive

AC

functions (Fig. 6) with decreased FA and increased MD in prefrontal and temporal WM [195, 201, 202]. Interestingly, DTI can detect ALS-related WM microstructural changes independent of atrophy, suggesting that most microstructural alterations reflect intrinsic tissue damage and not simply a volume loss [203]. In a VBM and TBSS study, subgroups of patients with FTD and ALS-FTD showed widespread GM and WM changes involving frontal and temporal lobes, corroborating evidence that ALS and FTD lie on a clinical, pathological and genetic continuum [204]. Correlations between neuropsychological impairment and DTI changes in ALS and ALS-FTD will need to be further explored. However, DTI changes in the cingulum, uncinate, inferior longitudinal and inferior fronto occipital fasciculi have been demonstrated to correlate with attention and executive functions [205], as well as with impairments in socio-emotional processing [206]. Although in ALS patients a FA reduction within the cervical cord has been reported to correlate with disability and to worsen with time [207, 208],

ACCEPTED MANUSCRIPT few studies have focused on the spinal cord as a pathological hallmark of ALS. A recent paper which evaluates DTI within the lateral corticospinal tracts of the spinal cord at cervical level did not reveal a correlation of DTI-derived indices with clinical parameters [209]

RI P

T

DTI has also been tested in the differential diagnosis of other motor neuron diseases. In patients with progressive muscular atrophy (PMA), DTI gives conflicting results. Reduced FA in CST has been reported in some studies [187] but not others [185]. This reflects the uncertain nosological definition of PMA. Indeed,

SC

as it occurs for pathological CST alterations [168], the detection of diffusion changes also depends on how

MA NU

stringent the criteria for the diagnosis of PMA are. Notably, a voxel-based DTI study of patients with PMA that later developed ALS showed decreased FA values along the CST, suggesting that DTI may be a marker of early and clinically silent UMN involvement [210]. The potential role of DTI in revealing subtle UMN involvement is supported by the observation that in a group of subjects at risk of developing ALS due to SOD1 mutation, FA changes in CST are detectable before the clinical manifestation [211]. DTI studies

ED

investigating patients with primary lateral sclerosis (PLS) showed that patients with PLS had lower FA

PT

values than that of patients with ALS in the body of the corpus callosum and in the WM adjacent to the right primary motor cortex [194, 212-214] (Fig. 7).

CE

DTI has also been applied to the longitudinal evaluation of ALS patients. Some studies reported a progression of brain damage which correlates with worsening of disability [195] whereas others did not [193,

AC

215]. FA within the posterior limb of the internal capsule seems to have a prognostic value in ALS, since it was able to identify cases with more rapid progression [216]. Indeed, FA along CST is associated with progression rate as measured by the ALS functional rating scale and more pronounced DTI abnormalities along CST predict a poorer long-term clinical outcome. The survival at 3 years was reported to be 42% and 90% in patients with a FA value under and over the cutoff of 0.56, respectively [217]. Disease duration, ALS functional rating scale and a subscore for physical and executive function correlate with progressively decreased FA and increased MD predominantly in the motor system (along CST after 6 months) as well as in the cerebellum and the limbic system [218]. The involvement of motor as well as extra-motor brain regions in ALS could support the hypothesis of ALS as a system failure, with a dysfunction of the motor network and the way it is embedded in the overall brain network underlying the disease [213]. In this regard, the structural topological organization of the brain

ACCEPTED MANUSCRIPT in ALS has been preliminarily assessed by using DTI and graph theoretical analysis, albeit further studies are needed [219, 220]. In a study [219] which compared brain connectivity between patients and controls by using complexity theory without selecting regions of interest a priori, patients with ALS showed an impaired

RI P

T

sub-network of regions with reduced WM connectivity. This impaired sub-network was strongly centered around primary motor regions (bilateral precentral gyrus and right paracentral lobule) and also included secondary motor regions (bilateral caudal middle frontal gyrus and pallidum) as well as high-order hub

SC

regions (right posterior cingulate and precuneus). In contrast to the theory of ALS solely and progressively

MA NU

affecting a fixed set of primary motor connections, recently Verstraete et al.[220] have found an impaired connectivity which expands over time involving more and more connections and regions. When summarizing the clinical impact of diffusion-MRI in the diagnosis of ALS, no firm conclusion can be drawn. A recent meta-analysis indicates that using individual patient CST data, the diagnostic accuracy of DTI is modest (76%) and currently lacks sufficient discriminatory power to be used in the clinical setting

ED

[221]. Moreover, the variable results concerning the diagnostic accuracy of DTI for diagnosis of ALS could

PT

arise from several sources. One of the main limitations is related to patient selection. The clinical spectrum of ALS includes different phenotypes [222] and prognosis. Presently, it is unclear whether these clinical

CE

subtypes of ALS have different pathological substrates and result in different DTI changes. It appears however that the two main forms of ALS (bulbar form and limb onset form) may result in different DTI-

AC

related features, since FA alterations along the CST are more pronounced in the bulbar form [223].

5. Huntington’s disease (HD)

Huntington disease is due to the expansion of a CAG triplet in a gene encoding for a protein of unknown function named huntingtin, is inherited as an autosomal dominant trait and belongs to the group of polyglutamine (Poly-Q) diseases comprising 8 other conditions sharing expansion of a polyglutamine in the abnormal protein [224]. HD is the most frequent Poly-Q disease and one of the most common inherited neurodegenerative diseases for which a molecular genetic test enables a reliable diagnosis in vivo before the onset of clinical symptoms. These two key factors have justified the considerable interest and efforts in searching for MRI descriptors and markers of disease physiopathology and progression in HD, both from a

ACCEPTED MANUSCRIPT structural and functional point of view [225-227]. In particular, while the role of volumetry based on T1weighted (T1WI) MR images in demonstrating in vivo the ordinate progression of regional and global atrophy of the brain GM and WM in HD is now established, there is increasing focus on the capability of

SC

and symptomatic [230, 231, 234, 236-245] HD gene carriers.

RI P

T

quantitative diffusion-MRI to map the microstructural damage of GM and WM in pre-manifest [228-239]

MA NU

5.1 Applications of diffusion-MRI in HD

Several cross sectional diffusion-MRI studies revealed overall increased apparent diffusion coefficient (ADC) or MD and decreased FA in the subcortical GM nuclei (putamen, caudate, pallidum and thalamus) and cerebral WM including the corpus callosum, internal capsule and striato-pallidal tracts in symptomatic or

ED

presymptomatic HD gene carriers as compared with age matched controls [230, 231, 237, 239, 241, 242].

PT

Moreover, increased ADC or MD in subcortical GM or in WM correlated on one hand with clinical stages, global, motor and oculomotor functional impairment, cognitive performance, probability of disease onset in

CE

the next few years of MRI examination and on the other hand with the number of abnormal CAG triplets and estimates of disease burden score [230, 231, 237, 239, 241, 242]. Notably, in a multimodal MRI study of the

AC

basal ganglia including pre-manifest and symptomatic HD gene carriers and healthy controls, MD in the caudate, putamen, and thalamus was the most powerful predictor, explaining more than 50% of the variability in HD development and exceeding the predictive capability of volumetry [237]. Similar results were obtained in a DTI study of the basal ganglia in presymptomatic HD gene carriers. However, the highest discriminative accuracy was achieved in a multi-modality approach and when including all available measures: motor/neurocognitive scores and DTI/volumetry measures from the basal ganglia, accumbens and thalamus [246]. In a single study an unexpected decrease of MD was observed in the caudate nucleus, but not in the putamen, of presymptomatic HD gene carriers as compared to healthy controls. This was interpreted to potentially reflect the combined effect of increased oligodendroglial population, putatively a developmental abnormality, and incipient neurodegeneration [232]. Some DTI studies specifically addressed the damage of WM tracts in HD by using a segmentation/parcellation approach [234] or TBSS [235, 236,

ACCEPTED MANUSCRIPT 243], demonstrating that WM tract damage in the cerebral hemispheres and corpus callosum is observed in both pre-manifest and symptomatic HD gene carriers, and is correlated with estimated years to clinical onset, severity of cognitive and motor dysfunction and disease duration (Fig 8). Notably, by using probabilistic

RI P

T

tractography Kloppel et al. [228] documented selective damage of the fronto-striatal WM tracts in presymptomatic HD gene carriers which correlated with impairment of voluntary-guided saccades, reflecting early and selective damage of the eye fields of the frontal cortex. Furthermore, a selective degeneration of

SC

fiber tracts in subcortical regions of symptomatic HD patients was supported by a relatively atypical increase

MA NU

of FA in subcortical GM structures involved in the cortico-striato-thalamo-cortical loops [240]. Two recent studies [244, 245] focused on the diffusion properties of the corticospinal tract and cerebellar WM, two structures to which limited interest has been devoted in previous neuropathological and MRI studies. Phillips et al. [245] documented decreased FA and increased DA/DR in the corticospinal tracts of symptomatic HD carriers, which were correlated with CAG repeat length, age and clinical variables. Rees et

ED

al. [244] performed a combined morphometry and DTI study in early stage HD gene carriers and found

PT

reduced paravermal volume and decreased FA, as well as increased MD/DA/DR in both cerebellar GM and WM in HD. Abnormal cerebellar diffusion was associated with increasing motor deficit. Also, Poudel et al.

CE

[247] have used network-based statistics in order to show aberrant structural connectivity in WM networks in HD. Interestingly, preliminary animal studies [68, 248] have shown that non-Gaussian diffusion-MRI

AC

techniques could improve the characterization of tissue microstructure, showing a promising potential for diagnostic imaging in HD.

So far, few longitudinal studies addressed the capability of diffusion-MRI to track progression of neurodegeneration in HD [238, 249-251]. In a preliminary study, Weaver et al. [249] used DTI and TBSS to investigate the progression of WM changes in 7 HD gene carriers and 7 healthy controls examined at baseline and at 1 year follow-up. They reported a significant reduction of FA between baseline and follow-up that was evident throughout the brain in HD gene carriers but not in controls, and was combined with a decrease of DA and increases in DR. In a multi-modal longitudinal study [238], significant volume and diffusion (FA, MD) changes over an 18 month period in both presymptomatic and early symptomatic HD patients, as compared to controls, were detected at both the brain-wide level (whole brain, GM, WM) and in the caudate/putamen regions. Nevertheless, longitudinal volume change in the caudate was the only measure

ACCEPTED MANUSCRIPT that discriminated between groups across all stages of disease: far from diagnosis (> 15 years), close to diagnosis (< 15 years) and after diagnosis. On the other hand, two cohort studies failed to demonstrate significant changes of mean ADC and MD in the striatum and corpus callosum of symptomatic HD gene

RI P

T

carriers examined twice 1 year apart [250, 251]. Recently, TBSS analysis of DTI-derived indices was incorporated in a phase II trial investigating the effects of oral creatine supplementation in subjects at risk of HD [252]. However, further longitudinal investigations of quantitative diffusion-MRI in HD gene carriers

MA NU

SC

are needed to better establish its role as biomarker of disease progression.

6. Degenerative ataxias

Ataxia is defined as an inability to coordinate voluntary muscle movements that cannot be attributed to weakness or involuntary muscle activity. Several inherited or sporadic diseases, in which progressive ataxia

ED

with onset from childhood to elderly is the main clinical feature, are grouped under the term of degenerative

PT

ataxias (Table 1).

Conventional MRI allows an optimal visualization of the cerebellum, brainstem and spinal cord, which

CE

are the elective sites of damage in degenerative ataxias. By showing the variable combination and distribution of loss of bulk and signal intensity changes of the brainstem, cerebellum, spinal cord, and, in

AC

some instances of the basal ganglia and cerebral cortical GM, T1WI and T2-weighted (T2WI) MR images enable identification of three main atrophy pattern associated with degenerative ataxias, namely spinal atrophy (SA), cortico-cerebellar atrophy (CCA) and pontocerebellar atrophy (PCA) [253]. In particular, SA is characterized by thinning of the cervical spinal cord and medulla with symmetrically increased signal in the lateral and posterior columns of the spinal cord in T2WI, without apparent abnormalities of the cerebellum and cerebral hemispheres. In CCA, a marked thinning of the cerebellar folia is observed with normal volume of the brainstem and spinal cord and no signal change in T2WI. Finally, in PCA the volume of the brainstem, notably of the pons, middle cerebellar peduncles and cerebellum is markedly decreased and there is a diffuse mild hyperintensity in T2WI of the pons and WM of cerebellar hemispheres with sparing of the cortico-spinal tract featuring a typical “cross sign”. In sporadic forms of PCA loss of bulk and signal changes in the basal ganglia are common. The classification of degenerative ataxias based on the distribution

ACCEPTED MANUSCRIPT of atrophy shown by MRI is related with the aetiology of degenerative ataxias and can be followed to summarize the results obtained so far using quantitative diffusion-MRI for mapping the microstructural damage of the remaining WM and GM - generally in terms of increased ADC/MD and decreased FA [254].

RI P

T

A variety of approaches have been utilized to analyze ADC or DTI-derived metrics in cross-sectional studies of patients with degenerative ataxias including ROI- [255-259], histogram- [255, 259, 260], voxel- [260-265]

SC

as well as tractography-based methods [266, 267].

MA NU

6.1 Spinal atrophy (SA)

Friedreich’s ataxia (FRDA) is the most common inherited ataxia and shows a typical SA pattern in conventional MRI. The pathological hallmark of FRDA is neuronal loss and shrinkage in the spinal ganglion cells and in the Clarke column of the spinal cord, with degeneration of the peripheral nerves and of the

ED

spinocerebellar and gracilis and cuneatus tracts of the spinal cord [268]. The cerebellar cortex is spared,

PT

whereas cell loss is present in the dentate nuclei and gliosis of the cerebellar WM. Cerebral cortical GM abnormalities are restricted to the primary motor cortex, but visual system damage is common.

CE

Two cross sectional diffusion-MRI studies that evaluated alterations in tissue microstructure by using both ROI and histogram analyses showed selective damage in the medulla, inferior cerebellar peduncles,

AC

cerebellum and optic radiations in FRDA patients as compared to healthy controls [255, 259], and a strong correlation between ADC and disease duration/severity was reported in the larger study [259]. DTI-derived mapping of WM fiber tracts damage in FRDA revealed predominant damage of the superior cerebellar peduncles, which contain the main efferent fibers of the dentate nuclei, and was closely correlated with the severity of the neurological deficit [261, 262, 269-271]. Other brain WM tracts showing microstructural damage in FRDA include inferior cerebellar peduncles, corticospinal and thalamo cortical tracts [261, 269-271]. Interestingly, demonstration of microstructural damage of multiple WM tracts anatomically linking cerebellar and brainstem structures with cerebral cortical and subcortical regions points to disrupted cerebello-cortical connectivity in FRDA. This might explain some non-ataxia symptoms and signs in FRDA [269]. In addition, analysis of the diffusion tensor eigenvalues showed that the decrease of the FA in the superior cerebellar peduncles was associated with an increased axial diffusivity and a

ACCEPTED MANUSCRIPT concomitant greater increase of DR [272]. This pattern resembles that observed in association with a decrease of FA in degenerating WM tracts in other neurodegenerative diseases such as Alzheimer’s disease

RI P

T

[40, 273] and HD [40, 234, 243, 273].

6.2 Cortical cerebellar atrophy (CCA)

SC

Loss of Purkinjie cells in the cerebellar cortex is the most prominent neuropathological feature of CCA

MA NU

[274]. So far, quantitative diffusion-MRI data in patients with degenerative ataxia exhibiting a CCA pattern have been obtained in Ataxia Telangectasia (AT) [264, 275], an inherited recessive ataxia with childhood onset, and in Idiopathic Late Onset Cerebellar Ataxia (ILOCA) with pure cerebellar syndrome [255, 267], a sporadic condition. In particular, by using a ROI-based approach, an increase in ADC was found in the cerebellar WM and cortex but not in the cerebral hemispheres, brainstem and middle cerebellar peduncles of

ED

6 patients with AT as compared to 9 healthy controls [275]. In a combined morphometry and DTI study, a

PT

reduction of GM volume in both cerebellar hemispheres and in the precentral-postcentral gyrus was accompanied by a significant reduction in FA in the cerebellar hemispheres, anterior/posterior horns of the

CE

medulla, cerebral peduncles, and internal capsule WM and corona radiata in 11 AT patients when compared to 11 healthy controls [264]. Also, significant MD differences were observed within the left cerebellar

AC

hemisphere and the WM of the superior lobule of the right cerebella hemisphere. The ADC of the entire cerebellum and brainstem, but not of the medulla, pons, middle cerebellar peduncles and peridentate WM, was increased in 4 patients with ILOCA as compared to 26 healthy controls, possibly indicating a mild diffuse structural damage of the cerebellar cortical GM and of the cerebello-petal and cerebello-fugal WM tracts [255]. Also, in a DTI study that measured MD and FA in the cerebellar peduncles and evaluated the potential of tractography in patients with degenerative ataxias in 4 patients with ILOCA as compared to 8 healthy controls, no microstructural abnormalities were observed in any of the 3 pairs of cerebellar peduncles [267] that are typically spared at the neuropathological examination in patients with CCA [274].

ACCEPTED MANUSCRIPT 6.3 Ponto-cerebellar atrophy (PCA)

Neuropathology shows some heterogeneity in cases of inherited PCA, including spinocerebellar atrophy

RI P

T

(SCA) type 1-3, 7, 17 and dentate-rubro-pallido-luysian atrophy (DRPLA), and of sporadic PCA which essentially corresponds to multi-system atrophy, cerebellar type (MSA-C) [274, 276]. Neuronal loss in several brainstem GM nuclei is common to all these conditions, while the occurrence of neuronal damage in

SC

the dentate nucleus and cerebellar cortex as well as in the basal ganglia and cerebral cortex is much more

MA NU

variable. Conversely, demyelination, axonal degeneration and gliosis are invariably present in the WM of the brainstem, cerebellum and middle cerebellar peduncles.

Quantitative diffusion-MRI studies to-date were performed in cases of SCA1, SCA2, SCA3, SCA 7, DRPLA and MSA-C [255-258, 260, 263, 266, 267, 277-280]. Using a combined ROI and histogram analysis, an increase of ADC was observed in the medulla, middle cerebellar peduncles, and peridentate WM

ED

in patients with SCA1, SCA2 and MSA-C who also had significantly increased values of the 25th and 50th

PT

percentiles in the brainstem and cerebellum ADC histogram [255]. The latter correlated with the severity of the clinical deficit in patients with SCA1 and SCA2. Similar findings were reported in two cross-sectional

CE

studies that used manually drawn ROIs to explore diffusion-MRI in patients with SCA1 and SCA2 [256] as well as in patients with MSA [257]. In particular, MD was significantly elevated also in the corticospinal

AC

tract in SCA2, and the severity of clinical deficit correlated with the increase in MD in several regions in both SCA1 and SCA2 [256]. In MSA patients, an increased mean ADC was also observed in the putamen and the increase in mean ADC in the cerebellum and middle cerebellar peduncles correlated with disease duration [257]. DTI studies demonstrated that microstructural changes in terms of decreased FA involved the inferior, middle and superior cerebellar peduncles in DRPLA and SCA 7, whereas in patients with MSA-C the middle cerebellar peduncles were selectively involved [263, 267]. Della Nave et al. [260] explored the distribution of microstructural damage of the main WM fiber tracts in patients with SCA1 and with SCA2 as compared to healthy control subjects by using DTI and TBSS. In SCA1 and SCA2, decreased FA and increased MD and DR were observed in the inferior, middle and superior cerebellar peduncles, pontine transverse fibers, medial and lateral lemnisci, spinothalamic tracts, corticospinal tracts and corpus callosum (Fig. 9).

ACCEPTED MANUSCRIPT Notably, axial diffusivity was increased in the majority of the same tracts, but it was decreased in the medial and lateral lemnisci and the spinothalamic tracts. The extent of tract changes was greater in SCA2 patients who also showed decreased FA in the short intracerebellar tracts. In both diseases,

RI P

T

TBSS results correlated with clinical severity. Two recent DTI studies report a widespread microstructural damage of the brain including cerebellum, brainstem, thalamus, frontal lobes and temporal lobes in SCA3, which correlated with clinical deficits [278, 280]. A decreased FA in the three cerebellar peduncles which

SC

correlated with the severity of the clinical deficit was reported in a DTI study that included patients

MA NU

with a variety of degenerative ataxias (MSA, SCA1 and SCA2) predominantly exhibiting a PCA pattern [266]. Also, by applying graph theory and diffusion tensor tractography, Lu et al. [279] demonstrated that disrupted cerebellar structural connectivity reduces whole-brain network efficiency in MSA. So far longitudinal diffusion weighted studies in degenerative ataxias were obtained in MSA and SCA 2

ED

[258, 265, 277]. In MSA patients examined twice 1 year apart, Pellecchia et al. [258] demonstrated progression of the microstructural changes in terms of increased mean ADC in the putamen, pons, cerebellar

PT

WM, thalamus and frontal WM which were not correlated with clinical variations. In another study, MSA patients examined twice 2 years apart showed increased ADC of the middle cerebellar peduncles which was

CE

correlated with motor function decline [277]. In SCA2 patients examined twice 3.6 years apart, longitudinal TBSS analysis revealed significantly increased DA in WM tracts of the right cerebral hemisphere and the

AC

corpus callosum as compared to healthy controls [265].

7. Challenges and future research

Given that diffusion-MRI is a truly quantitative technique which is inherently sensitive to microstructural properties of tissue [281], it has proven useful in providing unique insight into changes and abnormalities that underpin various diseases besides neurodegenerative disorders. Diffusion-MRI has become an extremely important tool for studying the morphology and structural connectivity of living brain tissue [2, 282]. In particular, advances in diffusion-MRI along with improvement in network theory [101, 283] could provide further insights into the characterization of the “connectome” (i.e. a brain property which refers to a combination of several neural characteristics that describe the ability of the brain to transfer information

ACCEPTED MANUSCRIPT between different areas at different length and time scales [284]). In this regard, the Human Connectome Project (HCP) is a recent ambitious effort to chart human brain connectivity in a large population of 1200 healthy adults using cutting-edge neuroimaging methods (which include diffusion-MRI, resting state fMRI,

RI P

T

task-evoked fMRI, T1- and T2-weighted MRI, as well as combined magnetoencephalography and electroencephalography [285]), and to enable detailed comparisons between brain circuits, behavior as well as genetics at single-subject level [286, 287]. In the field of diffusion-MRI, the HCP also includes major

SC

efforts towards improving MR scanner instrumentation and image acquisition/processing methods [288-293].

MA NU

So far, clinical as well as research studies have commonly carried out measurements of conventional DTI-derived indices (FA, MD, DR, DA) [294], which can be computed from the eigenvalues of the diffusion tensor (i.e. they convey information about the shape of the diffusion tensor [295]). In this regard, brain tissue FA and MD depend on several factors [3] which include but are not limited to degree of fiber myelination, axon density, axon diameter, permeability of cell membranes as well as the way in which the axons are laid

ED

out within the voxel under examination. Therefore, any physiological interpretation of changes of FA and

PT

MD (which are not mathematically orthogonal measures of diffusive processes [295]) should be performed with caution [296, 297]. The use of independent additional indices, which can be derived from the diffusion

CE

tensor by exploiting directional information [295], as well as sophisticated differential operators [298], could improve the potentialities of DTI. On the other hand, given that high b-value measurements have shown that

AC

diffusional dynamics in tissue is not Gaussian and that this circumstance can become of crucial importance in regions containing crossing fibers [60, 61, 299], diffusion-MRI techniques based on multi-compartment models, the stretched exponential model [62, 300-302], DKI [64, 71] and q–space methods [303-305] could be suitable for characterizing diffusion processes in greater detail. While currently the relatively long acquisition times (when compared to typical DTI protocols) as well as the limited strength of diffusion gradients in clinical MR scanner systems are limiting factors, technological developments can be expected to allow more widespread use of such techniques in the near future. While conventional DTI is commonly optimized and applied mostly for the study and characterization of the main WM fiber bundles, it is not as useful in inferring microstructural information in region such as GM, where the macroscopic organization of fibers is isotropic (i.e. there is no directional coherence of fibers at the scale of a voxel). Nevertheless, it has been shown that, when compared to DTI, DKI affords substantially

ACCEPTED MANUSCRIPT higher sensitivity to microstructural organization in isotropic tissues such as GM [70]. Also, the use of higher diffusion gradient strength, ultra-high fields [306], fast diffusion imaging acquisitions/methods [307-309] and double pulsed field gradient sequences [310, 311] may represent a way toward a deeper investigation of

RI P

T

GM cyto-architecture [77, 312, 313].

Multimodal studies which combine advanced diffusion-MRI techniques with the potential of functional MRI (e.g. task-related/resting-state) [179, 314-322] as well as volumetric MRI (e.g. voxel-

SC

based/deformation-based/surface-based morphometry) [20, 261, 323-329] represent a focus of future

MA NU

investigations in neurodegenerative disorders [19]. Moreover, given that the new "omics" techniques are powerful tools in generating further insights into the patho-etiology of disorders, combining neuroimaging data with genomic, proteomic as well as metabolic information could pave the way for novel, multi-scale interpretation of disease physiology [22, 330-333]. While multimodal MRI techniques can unlock a wealth of complementary structural and functional information and allow articulate studies of group differences

ED

(e.g. patients versus controls), their standalone employment cannot access the synergistic benefits of

PT

multimodal data collection or allocate a single subject to a particular group, hence limiting the overall diagnostic/prognostic potential in a clinical setting. In order to overcome these issues, machine learning

CE

techniques (e.g. support vector machines [334]) have recently been identified as promising tools in neuroimaging data analysis [335-337]. Such methods, when employed rigorously [109], are able to manage

AC

simultaneously different and multiple types of data feature (e.g. extracted by structural/functional neuroimaging, neurophysiological, genetic) which can be integrated into the classification procedure. As discussed in this review, sometimes the findings on diffusion-MRI in neurodegenerative disorders can appear contradictory. However, this can be due to different diagnostic criteria for the enrolled subjects in different studies. Moreover, different methods of data acquisition, processing and analysis can introduce significant bias in accuracy and precision of estimated diffusion-MRI indices [4, 338-343]. Therefore, a standardization of optimized diffusion-MRI methods is needed [344]. Diffusion-MRI studies are usually characterized by a limited number of enrolled subjects, often resulting in low statistical power (a factor which can contribute in explaining the variability in findings reported by different diffusion MRI studies on neurodegenerative disorders). Therefore, especially in rare pathologies, the integration of multicenter data would greatly improve the sensitivity of diffusion-MRI [21, 87] as well as

ACCEPTED MANUSCRIPT of other MRI methods [345-349]. Previous investigations have specifically evaluated the inter-scanner reproducibility of measurements of different DTI-derived indices in the human brain [350-357] showing, on the whole, a relatively small variability. Nonetheless, during the planning of a multicenter study, the

RI P

T

accuracy of quantitative diffusion-MRI measurements should be carefully assessed in every participating center. Additionally, in longitudinal studies, periodic monitoring of the accuracy of measured diffusion indices is highly recommended. Indeed, the signal-to-noise ratio as well as the overall degree of calibration

SC

of the high strength diffusion gradients system can directly and systematically bias the measurement of

MA NU

diffusion indices [4, 358-362]. Accordingly, a number of studies have emphasized the importance of implementing specific diffusion-MRI related quality control protocols as well as correction methods [363375], which should be put into practice in addition to standard quality assurance routines in order to

AC

CE

PT

ED

guarantee the reliability of quantitative diffusion-MRI measurements.

ACCEPTED MANUSCRIPT References

[4] [5] [6]

[7]

[11]

[12]

[13]

[14]

[15]

[16] [17] [18]

T

CE

[10]

AC

[9]

PT

ED

[8]

RI P

[3]

SC

[2]

Einstein A, Fürth R. Investigations on the theory of Brownian movement. 1956, New York, N.Y.: Dover Publications, doi: Johansen-Berg H, Behrens TEJ. Diffusion MRI: From quantitative measurement to in-vivo neuroanatomy. 2009: Elsevier Science, doi: Beaulieu C. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed, 2002; 15(7-8):435-55, doi: 10.1002/nbm.782. Jones DK. Diffusion MRI : Theory, Methods, and Applications. 2010: Oxford University Press, USA, doi: 10.1093/med/9780195369779.001.0001. Tournier JD, Mori S, Leemans A. Diffusion tensor imaging and beyond. Magn Reson Med, 2011; 65(6):1532-56, doi: 10.1002/mrm.22924. Brodaty H, Breteler MM, Dekosky ST, Dorenlot P, Fratiglioni L, Hock C, Kenigsberg PA, Scheltens P, De Strooper B. The world of dementia beyond 2020. J Am Geriatr Soc, 2011; 59(5):923-7, doi: 10.1111/j.1532-5415.2011.03365.x. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 2011; 7(3):270-9, doi: 10.1016/j.jalz.2011.03.008. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR, Jr., Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y, Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 2011; 7(3):280-92, doi: 10.1016/j.jalz.2011.03.003. Brun A, Englund E. A white matter disorder in dementia of the Alzheimer type: a pathoanatomical study. Ann Neurol, 1986; 19(3):253-62, doi: 10.1002/ana.410190306. Gouw AA, Seewann A, Vrenken H, van der Flier WM, Rozemuller JM, Barkhof F, Scheltens P, Geurts JJ. Heterogeneity of white matter hyperintensities in Alzheimer's disease: post-mortem quantitative MRI and neuropathology. Brain, 2008; 131(Pt 12):3286-98, doi: 10.1093/brain/awn265. Scheltens P, Barkhof F, Leys D, Wolters EC, Ravid R, Kamphorst W. Histopathologic correlates of white matter changes on MRI in Alzheimer's disease and normal aging. Neurology, 1995; 45(5):8838, doi: 10.1212/WNL.45.5.883. Sjobeck M, Haglund M, Englund E. White matter mapping in Alzheimer's disease: A neuropathological study. Neurobiol Aging, 2006; 27(5):673-80, doi: 10.1016/j.neurobiolaging.2005.03.007. Roher AE, Weiss N, Kokjohn TA, Kuo YM, Kalback W, Anthony J, Watson D, Luehrs DC, Sue L, Walker D, Emmerling M, Goux W, Beach T. Increased A beta peptides and reduced cholesterol and myelin proteins characterize white matter degeneration in Alzheimer's disease. Biochemistry, 2002; 41(37):11080-90, doi: 10.1021/bi026173d. Bartzokis G. Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer's disease. Neurobiol Aging, 2004; 25(1):5-18; author reply 49-62, doi: 10.1016/j.neurobiolaging.2003.03.001. Reisberg B, Franssen EH, Souren LE, Auer SR, Akram I, Kenowsky S. Evidence and mechanisms of retrogenesis in Alzheimer's and other dementias: management and treatment import. Am J Alzheimers Dis Other Demen, 2002; 17(4):202-12, doi: 10.1177/153331750201700411. Bartzokis G, Lu PH, Mintz J. Human brain myelination and amyloid beta deposition in Alzheimer's disease. Alzheimers Dement, 2007; 3(2):122-5, doi: 10.1016/j.jalz.2007.01.019. Coleman M. Axon degeneration mechanisms: commonality amid diversity. Nat Rev Neurosci, 2005; 6(11):889-98, doi: 10.1038/nrn1788. Mori S. Introduction to Diffusion Tensor Imaging. 2007: Elsevier Science.

MA NU

[1]

ACCEPTED MANUSCRIPT [19]

[20]

[27]

[28]

[29]

[30]

[31]

[32]

[33]

[34]

[35] [36]

SC

MA NU

[26]

ED

[25]

PT

[24]

CE

[23]

AC

[22]

RI P

T

[21]

Teipel SJ, Grothe M, Lista S, Toschi N, Garaci FG, Hampel H. Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease. Med Clin North Am, 2013; 97(3):399-424, doi: 10.1016/j.mcna.2012.12.013. Teipel SJ, Meindl T, Grinberg L, Heinsen H, Hampel H. Novel MRI techniques in the assessment of dementia. Eur J Nucl Med Mol Imaging, 2008; 35 Suppl 1:S58-69, doi: 10.1007/s00259-007-0703-z. Teipel SJ, Wegrzyn M, Meindl T, Frisoni G, Bokde AL, Fellgiebel A, Filippi M, Hampel H, Kloppel S, Hauenstein K, Ewers M, group Es. Anatomical MRI and DTI in the diagnosis of Alzheimer's disease: a European multicenter study. J Alzheimers Dis, 2012; 31 Suppl 3:S33-47, doi: 10.3233/JAD-2012-112118. Lista S, Garaci FG, Toschi N, Hampel H. Imaging epigenetics in Alzheimer's disease. Curr Pharm Des, 2013; 19(36):6393-415, doi: 10.2174/13816128113199990370. Stricker NH, Schweinsburg BC, Delano-Wood L, Wierenga CE, Bangen KJ, Haaland KY, Frank LR, Salmon DP, Bondi MW. Decreased white matter integrity in late-myelinating fiber pathways in Alzheimer's disease supports retrogenesis. Neuroimage, 2009; 45(1):10-6, doi: 10.1016/j.neuroimage.2008.11.027. Choi SJ, Lim KO, Monteiro I, Reisberg B. Diffusion tensor imaging of frontal white matter microstructure in early Alzheimer's disease: a preliminary study. J Geriatr Psychiatry Neurol, 2005; 18(1):12-9, doi: 10.1177/0891988704271763. Huang J, Friedland RP, Auchus AP. Diffusion tensor imaging of normal-appearing white matter in mild cognitive impairment and early Alzheimer disease: preliminary evidence of axonal degeneration in the temporal lobe. AJNR Am J Neuroradiol, 2007; 28(10):1943-8, doi: 10.3174/ajnr.A0700. Bozzali M, Falini A, Franceschi M, Cercignani M, Zuffi M, Scotti G, Comi G, Filippi M. White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry, 2002; 72(6):742-6, doi: 10.1136/jnnp.72.6.742. Sexton CE, Kalu UG, Filippini N, Mackay CE, Ebmeier KP. A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease. Neurobiol Aging, 2011; 32(12):2322 e5-18, doi: 10.1016/j.neurobiolaging.2010.05.019. Mielke MM, Okonkwo OC, Oishi K, Mori S, Tighe S, Miller MI, Ceritoglu C, Brown T, Albert M, Lyketsos CG. Fornix integrity and hippocampal volume predict memory decline and progression to Alzheimer's disease. Alzheimers Dement, 2012; 8(2):105-13, doi: 10.1016/j.jalz.2011.05.2416. Nowrangi MA, Lyketsos CG, Leoutsakos JM, Oishi K, Albert M, Mori S, Mielke MM. Longitudinal, region-specific course of diffusion tensor imaging measures in mild cognitive impairment and Alzheimer's disease. Alzheimers Dement, 2012, doi: 10.1016/j.jalz.2012.05.2186. Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF, Schoenberg SO. White matter damage in Alzheimer disease and mild cognitive impairment: assessment with diffusion-tensor MR imaging and parallel imaging techniques. Radiology, 2007; 243(2):483-92, doi: 10.1148/radiol.2432051714. Head D, Buckner RL, Shimony JS, Williams LE, Akbudak E, Conturo TE, McAvoy M, Morris JC, Snyder AZ. Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cereb Cortex, 2004; 14(4):410-23, doi: 10.1093/cercor/bhh003. Zhang Y, Schuff N, Jahng GH, Bayne W, Mori S, Schad L, Mueller S, Du AT, Kramer JH, Yaffe K, Chui H, Jagust WJ, Miller BL, Weiner MW. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology, 2007; 68(1):13-9, doi: 10.1212/01.wnl.0000250326.77323.01. Kavcic V, Ni H, Zhu T, Zhong J, Duffy CJ. White matter integrity linked to functional impairments in aging and early Alzheimer's disease. Alzheimers Dement, 2008; 4(6):381-9, doi: 10.1016/j.jalz.2008.07.001. Fellgiebel A, Schermuly I, Gerhard A, Keller I, Albrecht J, Weibrich C, Muller MJ, Stoeter P. Functional relevant loss of long association fibre tracts integrity in early Alzheimer's disease. Neuropsychologia, 2008; 46(6):1698-706, doi: 10.1016/j.neuropsychologia.2007.12.010. Stebbins GT, Murphy CM. Diffusion tensor imaging in Alzheimer's disease and mild cognitive impairment. Behav Neurol, 2009; 21(1):39-49, doi: 10.3233/BEN-2009-0234. Damoiseaux JS, Smith SM, Witter MP, Sanz-Arigita EJ, Barkhof F, Scheltens P, Stam CJ, Zarei M, Rombouts SA. White matter tract integrity in aging and Alzheimer's disease. Hum Brain Mapp, 2009; 30(4):1051-9, doi: 10.1002/hbm.20563.

ACCEPTED MANUSCRIPT [37]

[44]

[45]

[46]

[47]

[48]

[49]

[50]

[51]

[52]

SC

MA NU

[43]

ED

[42]

PT

[41]

CE

[40]

AC

[39]

RI P

T

[38]

Mielke MM, Kozauer NA, Chan KC, George M, Toroney J, Zerrate M, Bandeen-Roche K, Wang MC, Vanzijl P, Pekar JJ, Mori S, Lyketsos CG, Albert M. Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease. Neuroimage, 2009; 46(1):47-55, doi: 10.1016/j.neuroimage.2009.01.054. Medina D, DeToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M, Fleischman D, Bennett DA, Leurgans S, Turner DA, Stebbins GT. White matter changes in mild cognitive impairment and AD: A diffusion tensor imaging study. Neurobiol Aging, 2006; 27(5):663-72, doi: 10.1016/j.neurobiolaging.2005.03.026. Rose SE, McMahon KL, Janke AL, O'Dowd B, de Zubicaray G, Strudwick MW, Chalk JB. Diffusion indices on magnetic resonance imaging and neuropsychological performance in amnestic mild cognitive impairment. J Neurol Neurosurg Psychiatry, 2006; 77(10):1122-8, doi: 10.1136/jnnp.2005.074336. Acosta-Cabronero J, Williams GB, Pengas G, Nestor PJ. Absolute diffusivities define the landscape of white matter degeneration in Alzheimer's disease. Brain, 2010; 133(Pt 2):529-39, doi: 10.1093/brain/awp257. Fellgiebel A, Yakushev I. Diffusion tensor imaging of the hippocampus in MCI and early Alzheimer's disease. J Alzheimers Dis, 2011; 26 Suppl 3:257-62, doi: 10.3233/JAD-2011-0001. Budde MD, Xie M, Cross AH, Song SK. Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci, 2009; 29(9):2805-13, doi: 10.1523/JNEUROSCI.4605-08.2009. Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage, 2003; 20(3):1714-22, doi: 10.1016/j.neuroimage.2003.07.005. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage, 2006; 31(4):1487-505, doi: 10.1016/j.neuroimage.2006.02.024. Bosch B, Arenaza-Urquijo EM, Rami L, Sala-Llonch R, Junque C, Sole-Padulles C, Pena-Gomez C, Bargallo N, Molinuevo JL, Bartres-Faz D. Multiple DTI index analysis in normal aging, amnestic MCI and AD. Relationship with neuropsychological performance. Neurobiol Aging, 2012; 33(1):6174, doi: 10.1016/j.neurobiolaging.2010.02.004. Di Paola M, Di Iulio F, Cherubini A, Blundo C, Casini AR, Sancesario G, Passafiume D, Caltagirone C, Spalletta G. When, where, and how the corpus callosum changes in MCI and AD: a multimodal MRI study. Neurology, 2010; 74(14):1136-42, doi: 10.1212/WNL.0b013e3181d7d8cb. O'Dwyer L, Lamberton F, Bokde AL, Ewers M, Faluyi YO, Tanner C, Mazoyer B, O'Neill D, Bartley M, Collins DR, Coughlan T, Prvulovic D, Hampel H. Multiple indices of diffusion identifies white matter damage in mild cognitive impairment and Alzheimer's disease. PLoS One, 2011; 6(6):e21745, doi: 10.1371/journal.pone.0021745. Liu Y, Spulber G, Lehtimaki KK, Kononen M, Hallikainen I, Grohn H, Kivipelto M, Hallikainen M, Vanninen R, Soininen H. Diffusion tensor imaging and tract-based spatial statistics in Alzheimer's disease and mild cognitive impairment. Neurobiol Aging, 2011; 32(9):1558-71, doi: 10.1016/j.neurobiolaging.2009.10.006. Acosta-Cabronero J, Alley S, Williams GB, Pengas G, Nestor PJ. Diffusion tensor metrics as biomarkers in Alzheimer's disease. PLoS One, 2012; 7(11):e49072, doi: 10.1371/journal.pone.0049072. Agosta F, Pievani M, Sala S, Geroldi C, Galluzzi S, Frisoni GB, Filippi M. White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology, 2011; 258(3):853-63, doi: 10.1148/radiol.10101284. Alves GS, O'Dwyer L, Jurcoane A, Oertel-Knochel V, Knochel C, Prvulovic D, Sudo F, Alves CE, Valente L, Moreira D, Fubetaer F, Karakaya T, Pantel J, Engelhardt E, Laks J. Different patterns of white matter degeneration using multiple diffusion indices and volumetric data in mild cognitive impairment and Alzheimer patients. PLoS One, 2012; 7(12):e52859, doi: 10.1371/journal.pone.0052859. Song SK, Kim JH, Lin SJ, Brendza RP, Holtzman DM. Diffusion tensor imaging detects agedependent white matter changes in a transgenic mouse model with amyloid deposition. Neurobiol Dis, 2004; 15(3):640-7, doi: 10.1016/j.nbd.2003.12.003.

ACCEPTED MANUSCRIPT

[58]

[59] [60]

[61]

[62] [63]

[64] [65]

[66]

[67]

[68]

[69]

[70]

T

RI P

SC

MA NU

[57]

ED

[56]

PT

[55]

CE

[54]

Ghoshal N, Garcia-Sierra F, Wuu J, Leurgans S, Bennett DA, Berry RW, Binder LI. Tau conformational changes correspond to impairments of episodic memory in mild cognitive impairment and Alzheimer's disease. Exp Neurol, 2002; 177(2):475-93. Kalus P, Slotboom J, Gallinat J, Mahlberg R, Cattapan-Ludewig K, Wiest R, Nyffeler T, Buri C, Federspiel A, Kunz D, Schroth G, Kiefer C. Examining the gateway to the limbic system with diffusion tensor imaging: the perforant pathway in dementia. Neuroimage, 2006; 30(3):713-20, doi: 10.1016/j.neuroimage.2005.10.035. Kanaan NM, Pigino GF, Brady ST, Lazarov O, Binder LI, Morfini GA. Axonal degeneration in Alzheimer's disease: when signaling abnormalities meet the axonal transport system. Exp Neurol, 2013; 246:44-53, doi: 10.1016/j.expneurol.2012.06.003. Vana L, Kanaan NM, Ugwu IC, Wuu J, Mufson EJ, Binder LI. Progression of tau pathology in cholinergic Basal forebrain neurons in mild cognitive impairment and Alzheimer's disease. Am J Pathol, 2011; 179(5):2533-50, doi: 10.1016/j.ajpath.2011.07.044. van Bruggen T, Stieltjes B, Thomann PA, Parzer P, Meinzer HP, Fritzsche KH. Do Alzheimerspecific microstructural changes in mild cognitive impairment predict conversion? Psychiatry Res, 2012; 203(2-3):184-93, doi: 10.1016/j.pscychresns.2011.12.003. Selnes P, Aarsland D, Bjornerud A, Gjerstad L, Wallin A, Hessen E, Reinvang I, Grambaite R, Auning E, Kjaervik VK, Due-Tonnessen P, Stenset V, Fladby T. Diffusion tensor imaging surpasses cerebrospinal fluid as predictor of cognitive decline and medial temporal lobe atrophy in subjective cognitive impairment and mild cognitive impairment. J Alzheimers Dis, 2013; 33(3):723-36, doi: 10.3233/JAD-2012-121603. Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR Biomed, 2002; 15(7-8):456-67, doi: 10.1002/nbm.783. Grinberg F, Farrher E, Kaffanke J, Oros-Peusquens AM, Shah NJ. Non-Gaussian diffusion in human brain tissue at high b-factors as examined by a combined diffusion kurtosis and biexponential diffusion tensor analysis. Neuroimage, 2011; 57(3):1087-102, doi: 10.1016/j.neuroimage.2011.04.050. Clark CA, Le Bihan D. Water diffusion compartmentation and anisotropy at high b values in the human brain. Magn Reson Med, 2000; 44(6):852-9, doi: 10.1002/15222594(200012)44:6<852::AID-MRM5>3.0.CO;2-A. De Santis S, Gabrielli A, Palombo M, Maraviglia B, Capuani S. Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging, 2011; 29(10):1410-6, doi: 10.1016/j.mri.2011.04.006. Bester M, Jensen J, Babb J, Tabesh A, Miles L, Herbert J, Grossman R, Inglese M. Non-Gaussian diffusion MRI of gray matter is associated with cognitive impairment in multiple sclerosis. Mult Scler, 2014, doi: 10.1177/1352458514556295. Wu EX, Cheung MM. MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed, 2010; 23(7):836-48, doi: 10.1002/nbm.1506. Van Cauter S, Veraart J, Sijbers J, Peeters RR, Himmelreich U, De Keyzer F, Van Gool SW, Van Calenbergh F, De Vleeschouwer S, Van Hecke W, Sunaert S. Gliomas: diffusion kurtosis MR imaging in grading. Radiology, 2012; 263(2):492-501, doi: 10.1148/radiol.12110927. Lee CY, Tabesh A, Benitez A, Helpern JA, Jensen JH, Bonilha L. Microstructural integrity of earlyversus late-myelinating white matter tracts in medial temporal lobe epilepsy. Epilepsia, 2013; 54(10):1801-9, doi: 10.1111/epi.12353. Hui ES, Fieremans E, Jensen JH, Tabesh A, Feng W, Bonilha L, Spampinato MV, Adams R, Helpern JA. Stroke assessment with diffusional kurtosis imaging. Stroke, 2012; 43(11):2968-73, doi: 10.1161/STROKEAHA.112.657742. Blockx I, De Groof G, Verhoye M, Van Audekerke J, Raber K, Poot D, Sijbers J, Osmand AP, Von Horsten S, Van der Linden A. Microstructural changes observed with DKI in a transgenic Huntington rat model: evidence for abnormal neurodevelopment. Neuroimage, 2012; 59(2):957-67, doi: 10.1016/j.neuroimage.2011.08.062. Falangola MF, Jensen JH, Tabesh A, Hu C, Deardorff RL, Babb JS, Ferris S, Helpern JA. NonGaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer's disease. Magn Reson Imaging, 2013; 31(6):840-6, doi: 10.1016/j.mri.2013.02.008. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med, 2005; 53(6):1432-40, doi: 10.1002/mrm.20508.

AC

[53]

ACCEPTED MANUSCRIPT [71] [72]

[79] [80]

[81]

[82]

[83]

[84]

[85]

[86]

[87]

[88]

SC

MA NU

[78]

ED

[77]

PT

[76]

CE

[75]

AC

[74]

RI P

T

[73]

Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed, 2010; 23(7):698-710, doi: 10.1002/nbm.1518. Steven AJ, Zhuo J, Melhem ER. Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain. AJR Am J Roentgenol, 2014; 202(1):W26-33, doi: 10.2214/AJR.13.11365. Gong NJ, Wong CS, Chan CC, Leung LM, Chu YC. Aging in deep gray matter and white matter revealed by diffusional kurtosis imaging. Neurobiol Aging, 2014; 35(10):2203-16, doi: 10.1016/j.neurobiolaging.2014.03.011. Gao J, Feng ST, Wu B, Gong N, Lu M, Wu PM, Wang H, He X, Huang B. Microstructural brain abnormalities of children of idiopathic generalized epilepsy with generalized tonic-clonic seizure: A voxel-based diffusional kurtosis imaging study. J Magn Reson Imaging, 2015; 41(4):1088-95, doi: 10.1002/jmri.24647. Bonilha L, Lee CY, Jensen JH, Tabesh A, Spampinato MV, Edwards JC, Breedlove J, Helpern JA. Altered microstructure in temporal lobe epilepsy: a diffusional kurtosis imaging study. AJNR Am J Neuroradiol, 2015; 36(4):719-24, doi: 10.3174/ajnr.A4185. Gao Y, Zhang Y, Wong CS, Wu PM, Zhang Z, Gao J, Qiu D, Huang B. Diffusion abnormalities in temporal lobes of children with temporal lobe epilepsy: a preliminary diffusional kurtosis imaging study and comparison with diffusion tensor imaging. NMR Biomed, 2012; 25(12):1369-77, doi: 10.1002/nbm.2809. Jespersen SN, Leigland LA, Cornea A, Kroenke CD. Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging. IEEE Trans Med Imaging, 2012; 31(1):16-32, doi: 10.1109/TMI.2011.2162099. Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage, 2011; 58(1):177-88, doi: 10.1016/j.neuroimage.2011.06.006. Hui ES, Russell Glenn G, Helpern JA, Jensen JH. Kurtosis analysis of neural diffusion organization. Neuroimage, 2015; 106:391-403, doi: 10.1016/j.neuroimage.2014.11.015. Jelescu IO, Veraart J, Adisetiyo V, Milla SS, Novikov DS, Fieremans E. One diffusion acquisition and different white matter models: how does microstructure change in human early development based on WMTI and NODDI? Neuroimage, 2015; 107:242-56, doi: 10.1016/j.neuroimage.2014.12.009. Gong NJ, Wong CS, Chan CC, Leung LM, Chu YC. Correlations between microstructural alterations and severity of cognitive deficiency in Alzheimer's disease and mild cognitive impairment: a diffusional kurtosis imaging study. Magn Reson Imaging, 2013; 31(5):688-94, doi: 10.1016/j.mri.2012.10.027. Fieremans E, Benitez A, Jensen JH, Falangola MF, Tabesh A, Deardorff RL, Spampinato MV, Babb JS, Novikov DS, Ferris SH, Helpern JA. Novel white matter tract integrity metrics sensitive to Alzheimer disease progression. AJNR Am J Neuroradiol, 2013, doi: 10.3174/ajnr.A3553. Zarei M, Damoiseaux JS, Morgese C, Beckmann CF, Smith SM, Matthews PM, Scheltens P, Rombouts SA, Barkhof F. Regional white matter integrity differentiates between vascular dementia and Alzheimer disease. Stroke, 2009; 40(3):773-9, doi: 10.1161/STROKEAHA.108.530832. Kiuchi K, Morikawa M, Taoka T, Nagashima T, Yamauchi T, Makinodan M, Norimoto K, Hashimoto K, Kosaka J, Inoue Y, Inoue M, Kichikawa K, Kishimoto T. Abnormalities of the uncinate fasciculus and posterior cingulate fasciculus in mild cognitive impairment and early Alzheimer's disease: a diffusion tensor tractography study. Brain Res, 2009; 1287:184-91, doi: 10.1016/j.brainres.2009.06.052. Bozzali M, Parker GJ, Serra L, Embleton K, Gili T, Perri R, Caltagirone C, Cercignani M. Anatomical connectivity mapping: a new tool to assess brain disconnection in Alzheimer's disease. Neuroimage, 2011; 54(3):2045-51, doi: 10.1016/j.neuroimage.2010.08.069. Bozzali M, Giulietti G, Basile B, Serra L, Spano B, Perri R, Giubilei F, Marra C, Caltagirone C, Cercignani M. Damage to the cingulum contributes to Alzheimer's disease pathophysiology by deafferentation mechanism. Hum Brain Mapp, 2012; 33(6):1295-308, doi: 10.1002/hbm.21287. Fischer FU, Scheurich A, Wegrzyn M, Schermuly I, Bokde AL, Kloppel S, Pouwels PJ, Teipel S, Yakushev I, Fellgiebel A. Automated tractography of the cingulate bundle in Alzheimer's disease: a multicenter DTI study. J Magn Reson Imaging, 2012; 36(1):84-91, doi: 10.1002/jmri.23621. Douaud G, Jbabdi S, Behrens TE, Menke RA, Gass A, Monsch AU, Rao A, Whitcher B, Kindlmann G, Matthews PM, Smith S. DTI measures in crossing-fibre areas: increased diffusion anisotropy

ACCEPTED MANUSCRIPT

[89]

[96] [97]

[98]

[99]

[100]

[101] [102] [103]

[104]

[105]

[106]

SC

MA NU

[95]

ED

[94]

PT

[93]

CE

[92]

AC

[91]

RI P

T

[90]

reveals early white matter alteration in MCI and mild Alzheimer's disease. Neuroimage, 2011; 55(3):880-90, doi: 10.1016/j.neuroimage.2010.12.008. Pievani M, Agosta F, Pagani E, Canu E, Sala S, Absinta M, Geroldi C, Ganzola R, Frisoni GB, Filippi M. Assessment of white matter tract damage in mild cognitive impairment and Alzheimer's disease. Hum Brain Mapp, 2010; 31(12):1862-75, doi: 10.1002/hbm.20978. Nezamzadeh M, Wedeen VJ, Wang R, Zhang Y, Zhan W, Young K, Meyerhoff DJ, Weiner MW, Schuff N. In-vivo investigation of the human cingulum bundle using the optimization of MR diffusion spectrum imaging. Eur J Radiol, 2010; 75(1):e29-36, doi: 10.1016/j.ejrad.2009.06.019. Pfefferbaum A, Adalsteinsson E, Sullivan EV. Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging. Neuroimage, 2005; 26(3):891-9, doi: 10.1016/j.neuroimage.2005.02.034. Sullivan EV, Pfefferbaum A. Diffusion tensor imaging and aging. Neurosci Biobehav Rev, 2006; 30(6):749-61, doi: 10.1016/j.neubiorev.2006.06.002. Honea RA, Vidoni E, Harsha A, Burns JM. Impact of APOE on the healthy aging brain: a voxelbased MRI and DTI study. J Alzheimers Dis, 2009; 18(3):553-64, doi: 10.3233/JAD-2009-1163. Persson J, Lind J, Larsson A, Ingvar M, Cruts M, Van Broeckhoven C, Adolfsson R, Nilsson LG, Nyberg L. Altered brain white matter integrity in healthy carriers of the APOE epsilon4 allele: a risk for AD? Neurology, 2006; 66(7):1029-33, doi: 10.1212/01.wnl.0000204180.25361.48. Ryan L, Walther K, Bendlin BB, Lue LF, Walker DG, Glisky EL. Age-related differences in white matter integrity and cognitive function are related to APOE status. Neuroimage, 2011; 54(2):156577, doi: 10.1016/j.neuroimage.2010.08.052. Heise V, Filippini N, Ebmeier KP, Mackay CE. The APOE varepsilon4 allele modulates brain white matter integrity in healthy adults. Mol Psychiatry, 2011; 16(9):908-16, doi: 10.1038/mp.2010.90. Bendlin BB, Ries ML, Canu E, Sodhi A, Lazar M, Alexander AL, Carlsson CM, Sager MA, Asthana S, Johnson SC. White matter is altered with parental family history of Alzheimer's disease. Alzheimers Dement, 2010; 6(5):394-403, doi: 10.1016/j.jalz.2009.11.003. Bendlin BB, Carlsson CM, Johnson SC, Zetterberg H, Blennow K, Willette AA, Okonkwo OC, Sodhi A, Ries ML, Birdsill AC, Alexander AL, Rowley HA, Puglielli L, Asthana S, Sager MA. CSF T-Tau/Abeta42 predicts white matter microstructure in healthy adults at risk for Alzheimer's disease. PLoS One, 2012; 7(6):e37720, doi: 10.1371/journal.pone.0037720. Adluru N, Destiche DJ, Lu SY, Doran ST, Birdsill AC, Melah KE, Okonkwo OC, Alexander AL, Dowling NM, Johnson SC, Sager MA, Bendlin BB. White matter microstructure in late middle-age: Effects of apolipoprotein E4 and parental family history of Alzheimer's disease. Neuroimage Clin, 2014; 4:730-42, doi: 10.1016/j.nicl.2014.04.008. Racine AM, Adluru N, Alexander AL, Christian BT, Okonkwo OC, Oh J, Cleary CA, Birdsill A, Hillmer AT, Murali D, Barnhart TE, Gallagher CL, Carlsson CM, Rowley HA, Dowling NM, Asthana S, Sager MA, Bendlin BB, Johnson SC. Associations between white matter microstructure and amyloid burden in preclinical Alzheimer's disease: A multimodal imaging investigation. Neuroimage Clin, 2014; 4:604-14, doi: 10.1016/j.nicl.2014.02.001. Sporns O. Networks of the Brain. 2010: MIT Press, doi: Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 2010; 52(3):1059-69, doi: 10.1016/j.neuroimage.2009.10.003. Lo CY, Wang PN, Chou KH, Wang J, He Y, Lin CP. Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease. J Neurosci, 2010; 30(50):16876-85, doi: 10.1523/JNEUROSCI.4136-10.2010. Shu N, Liang Y, Li H, Zhang J, Li X, Wang L, He Y, Wang Y, Zhang Z. Disrupted topological organization in white matter structural networks in amnestic mild cognitive impairment: relationship to subtype. Radiology, 2012; 265(2):518-27, doi: 10.1148/radiol.12112361. Brown JA, Terashima KH, Burggren AC, Ercoli LM, Miller KJ, Small GW, Bookheimer SY. Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc Natl Acad Sci U S A, 2011; 108(51):20760-5, doi: 10.1073/pnas.1109038108. Prescott JW, Guidon A, Doraiswamy PM, Roy Choudhury K, Liu C, Petrella JR, Alzheimer's Disease Neuroimaging I. The Alzheimer structural connectome: changes in cortical network topology with increased amyloid plaque burden. Radiology, 2014; 273(1):175-84, doi: 10.1148/radiol.14132593.

ACCEPTED MANUSCRIPT

[112]

[113]

[114]

[115] [116] [117]

[118]

[119]

[120]

[121]

[122]

[123]

T

RI P

SC

MA NU

[111]

ED

[110]

PT

[109]

CE

[108]

O'Dwyer L, Lamberton F, Bokde AL, Ewers M, Faluyi YO, Tanner C, Mazoyer B, O'Neill D, Bartley M, Collins DR, Coughlan T, Prvulovic D, Hampel H. Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS One, 2012; 7(2):e32441, doi: 10.1371/journal.pone.0032441. Wee CY, Yap PT, Zhang D, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D. Identification of MCI individuals using structural and functional connectivity networks. Neuroimage, 2012; 59(3):2045-56, doi: 10.1016/j.neuroimage.2011.10.015. Diciotti S, Ciulli S, Mascalchi M, Giannelli M, Toschi N. The "peeking" effect in supervised feature selection on diffusion tensor imaging data. AJNR Am J Neuroradiol, 2013; 34(9):E107, doi: 10.3174/ajnr.A3685. de Rijk MC, Launer LJ, Berger K, Breteler MM, Dartigues JF, Baldereschi M, Fratiglioni L, Lobo A, Martinez-Lage J, Trenkwalder C, Hofman A. Prevalence of Parkinson's disease in Europe: A collaborative study of population-based cohorts. Neurologic Diseases in the Elderly Research Group. Neurology, 2000; 54(11 Suppl 5):S21-3, doi: 10.1212/WNL.54.11.21A. Lilienfeld DE, Perl DP. Projected neurodegenerative disease mortality in the United States, 19902040. Neuroepidemiology, 1993; 12(4):219-28, doi: 10.1159/000110320. Braak H, Ghebremedhin E, Rub U, Bratzke H, Del Tredici K. Stages in the development of Parkinson's disease-related pathology. Cell Tissue Res, 2004; 318(1):121-34, doi: 10.1007/s00441004-0956-9. Aarsland D, Andersen K, Larsen JP, Lolk A, Nielsen H, Kragh-Sorensen P. Risk of dementia in Parkinson's disease: a community-based, prospective study. Neurology, 2001; 56(6):730-6, doi: 10.1212/WNL.56.6.730. Williams-Gray CH, Foltynie T, Brayne CE, Robbins TW, Barker RA. Evolution of cognitive dysfunction in an incident Parkinson's disease cohort. Brain, 2007; 130(Pt 7):1787-98, doi: 10.1093/brain/awm111. Mascalchi M, Vella A, Ceravolo R. Movement disorders: role of imaging in diagnosis. J Magn Reson Imaging, 2012; 35(2):239-56, doi: 10.1002/jmri.22825. Tofts P. Quantitative MRI of the Brain: Measuring Changes Caused by Disease. 2003: Wiley, doi: 10.1002/0470869526. Nicoletti G, Lodi R, Condino F, Tonon C, Fera F, Malucelli E, Manners D, Zappia M, Morgante L, Barone P, Barbiroli B, Quattrone A. Apparent diffusion coefficient measurements of the middle cerebellar peduncle differentiate the Parkinson variant of MSA from Parkinson's disease and progressive supranuclear palsy. Brain, 2006; 129(Pt 10):2679-87, doi: 10.1093/brain/awl166. Seppi K, Schocke MF, Esterhammer R, Kremser C, Brenneis C, Mueller J, Boesch S, Jaschke W, Poewe W, Wenning GK. Diffusion-weighted imaging discriminates progressive supranuclear palsy from PD, but not from the parkinson variant of multiple system atrophy. Neurology, 2003; 60(6):922-7, doi: 10.1212/01.WNL.0000049911.91657.9D. Schocke MF, Seppi K, Esterhammer R, Kremser C, Mair KJ, Czermak BV, Jaschke W, Poewe W, Wenning GK. Trace of diffusion tensor differentiates the Parkinson variant of multiple system atrophy and Parkinson's disease. Neuroimage, 2004; 21(4):1443-51, doi: 10.1016/j.neuroimage.2003.12.005. Wang PS, Wu HM, Lin CP, Soong BW. Use of diffusion tensor imaging to identify similarities and differences between cerebellar and Parkinsonism forms of multiple system atrophy. Neuroradiology, 2011; 53(7):471-81, doi: 10.1007/s00234-010-0757-7. Boelmans K, Bodammer NC, Suchorska B, Kaufmann J, Ebersbach G, Heinze HJ, Niehaus L. Diffusion tensor imaging of the corpus callosum differentiates corticobasal syndrome from Parkinson's disease. Parkinsonism Relat Disord, 2010; 16(8):498-502, doi: 10.1016/j.parkreldis.2010.05.006. Prodoehl J, Li H, Planetta PJ, Goetz CG, Shannon KM, Tangonan R, Comella CL, Simuni T, Zhou XJ, Leurgans S, Corcos DM, Vaillancourt DE. Diffusion tensor imaging of Parkinson's disease, atypical parkinsonism, and essential tremor. Mov Disord, 2013; 28(13):1816-22, doi: 10.1002/mds.25491. Nair SR, Tan LK, Mohd Ramli N, Lim SY, Rahmat K, Mohd Nor H. A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging. Eur Radiol, 2013; 23(6):1459-66, doi: 10.1007/s00330-012-2759-9.

AC

[107]

ACCEPTED MANUSCRIPT

[130]

[131]

[132]

[133]

[134]

[135]

[136]

[137]

[138] [139]

[140]

[141]

T

RI P

SC

[129]

MA NU

[128]

ED

[127]

PT

[126]

CE

[125]

Yoshikawa K, Nakata Y, Yamada K, Nakagawa M. Early pathological changes in the parkinsonian brain demonstrated by diffusion tensor MRI. J Neurol Neurosurg Psychiatry, 2004; 75(3):481-4, doi: 10.1136/jnnp.2003.021873. Chan LL, Rumpel H, Yap K, Lee E, Loo HV, Ho GL, Fook-Chong S, Yuen Y, Tan EK. Case control study of diffusion tensor imaging in Parkinson's disease. J Neurol Neurosurg Psychiatry, 2007; 78(12):1383-6, doi: 10.1136/jnnp.2007.121525. Vaillancourt DE, Spraker MB, Prodoehl J, Abraham I, Corcos DM, Zhou XJ, Comella CL, Little DM. High-resolution diffusion tensor imaging in the substantia nigra of de novo Parkinson disease. Neurology, 2009; 72(16):1378-84, doi: 10.1212/01.wnl.0000340982.01727.6e. Menke RA, Scholz J, Miller KL, Deoni S, Jbabdi S, Matthews PM, Zarei M. MRI characteristics of the substantia nigra in Parkinson's disease: a combined quantitative T1 and DTI study. Neuroimage, 2009; 47(2):435-41, doi: 10.1016/j.neuroimage.2009.05.017. Dexter DT, Wells FR, Lees AJ, Agid F, Agid Y, Jenner P, Marsden CD. Increased nigral iron content and alterations in other metal ions occurring in brain in Parkinson's disease. J Neurochem, 1989; 52(6):1830-6. Earle KM. Studies on Parkinson's disease including x-ray fluorescent spectroscopy of formalin fixed brain tissue. J Neuropathol Exp Neurol, 1968; 27(1):1-14, doi: 10.1097/00005072-19680100000001. Sofic E, Riederer P, Heinsen H, Beckmann H, Reynolds GP, Hebenstreit G, Youdim MB. Increased iron (III) and total iron content in post mortem substantia nigra of parkinsonian brain. J Neural Transm, 1988; 74(3):199-205, doi: 10.1007/BF01244786. Haacke EM, Cheng NY, House MJ, Liu Q, Neelavalli J, Ogg RJ, Khan A, Ayaz M, Kirsch W, Obenaus A. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging, 2005; 23(1):1-25, doi: 10.1016/j.mri.2004.10.001. Peran P, Cherubini A, Assogna F, Piras F, Quattrocchi C, Peppe A, Celsis P, Rascol O, Demonet JF, Stefani A, Pierantozzi M, Pontieri FE, Caltagirone C, Spalletta G, Sabatini U. Magnetic resonance imaging markers of Parkinson's disease nigrostriatal signature. Brain, 2010; 133(11):3423-33, doi: 10.1093/brain/awq212. Du G, Lewis MM, Styner M, Shaffer ML, Sen S, Yang QX, Huang X. Combined R2* and diffusion tensor imaging changes in the substantia nigra in Parkinson's disease. Mov Disord, 2011; 26(9):1627-32, doi: 10.1002/mds.23643. Du G, Lewis MM, Sen S, Wang J, Shaffer ML, Styner M, Yang QX, Huang X. Imaging nigral pathology and clinical progression in Parkinson's disease. Mov Disord, 2012; 27(13):1636-43, doi: 10.1002/mds.25182. Aquino D, Contarino V, Albanese A, Minati L, Farina L, Grisoli M, Elia A, Bruzzone MG, Chiapparini L. Substantia nigra in Parkinson's disease: a multimodal MRI comparison between early and advanced stages of the disease. Neurol Sci, 2014; 35(5):753-8, doi: 10.1007/s10072-013-1595-2. Schwarz ST, Abaei M, Gontu V, Morgan PS, Bajaj N, Auer DP. Diffusion tensor imaging of nigral degeneration in Parkinson's disease: A region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis. Neuroimage Clin, 2013; 3:481-8, doi: 10.1016/j.nicl.2013.10.006. Tessa C, Giannelli M, Della Nave R, Lucetti C, Berti C, Ginestroni A, Bonuccelli U, Mascalchi M. A whole-brain analysis in de novo Parkinson disease. AJNR Am J Neuroradiol, 2008; 29(4):674-80, doi: 10.3174/ajnr.A0900. Karagulle Kendi AT, Lehericy S, Luciana M, Ugurbil K, Tuite P. Altered diffusion in the frontal lobe in Parkinson disease. AJNR Am J Neuroradiol, 2008; 29(3):501-5, doi: 10.3174/ajnr.A0850. Rae CL, Correia MM, Altena E, Hughes LE, Barker RA, Rowe JB. White matter pathology in Parkinson's disease: the effect of imaging protocol differences and relevance to executive function. Neuroimage, 2012; 62(3):1675-84, doi: 10.1016/j.neuroimage.2012.06.012. Zhan W, Kang GA, Glass GA, Zhang Y, Shirley C, Millin R, Possin KL, Nezamzadeh M, Weiner MW, Marks WJ, Jr., Schuff N. Regional alterations of brain microstructure in Parkinson's disease using diffusion tensor imaging. Mov Disord, 2012; 27(1):90-7, doi: 10.1002/mds.23917. Zhang K, Yu C, Zhang Y, Wu X, Zhu C, Chan P, Li K. Voxel-based analysis of diffusion tensor indices in the brain in patients with Parkinson's disease. Eur J Radiol, 2011; 77(2):269-73, doi: 10.1016/j.ejrad.2009.07.032.

AC

[124]

ACCEPTED MANUSCRIPT

[147]

[148]

[149]

[150]

[151]

[152]

[153]

[154]

[155]

[156]

[157]

[158]

T

RI P

SC

MA NU

[146]

ED

[145]

PT

[144]

CE

[143]

Ibarretxe-Bilbao N, Junque C, Marti MJ, Valldeoriola F, Vendrell P, Bargallo N, Zarei M, Tolosa E. Olfactory impairment in Parkinson's disease and white matter abnormalities in central olfactory areas: A voxel-based diffusion tensor imaging study. Mov Disord, 2010; 25(12):1888-94, doi: 10.1002/mds.23208. Rolheiser TM, Fulton HG, Good KP, Fisk JD, McKelvey JR, Scherfler C, Khan NM, Leslie RA, Robertson HA. Diffusion tensor imaging and olfactory identification testing in early-stage Parkinson's disease. J Neurol, 2011; 258(7):1254-60, doi: 10.1007/s00415-011-5915-2. Scherfler C, Schocke MF, Seppi K, Esterhammer R, Brenneis C, Jaschke W, Wenning GK, Poewe W. Voxel-wise analysis of diffusion weighted imaging reveals disruption of the olfactory tract in Parkinson's disease. Brain, 2006; 129(Pt 2):538-42, doi: 10.1093/brain/awh674. Kamagata K, Motoi Y, Abe O, Shimoji K, Hori M, Nakanishi A, Sano T, Kuwatsuru R, Aoki S, Hattori N. White matter alteration of the cingulum in Parkinson disease with and without dementia: evaluation by diffusion tensor tract-specific analysis. AJNR Am J Neuroradiol, 2012; 33(5):890-5, doi: 10.3174/ajnr.A2860. Hattori T, Orimo S, Aoki S, Ito K, Abe O, Amano A, Sato R, Sakai K, Mizusawa H. Cognitive status correlates with white matter alteration in Parkinson's disease. Hum Brain Mapp, 2012; 33(3):727-39, doi: 10.1002/hbm.21245. Modrego PJ, Fayed N, Artal J, Olmos S. Correlation of findings in advanced MRI techniques with global severity scales in patients with Parkinson disease. Acad Radiol, 2011; 18(2):235-41, doi: 10.1016/j.acra.2010.09.022. Scherfler C, Esterhammer R, Nocker M, Mahlknecht P, Stockner H, Warwitz B, Spielberger S, Pinter B, Donnemiller E, Decristoforo C, Virgolini I, Schocke M, Poewe W, Seppi K. Correlation of dopaminergic terminal dysfunction and microstructural abnormalities of the basal ganglia and the olfactory tract in Parkinson's disease. Brain, 2013; 136(Pt 10):3028-37, doi: 10.1093/brain/awt234. Melzer TR, Watts R, MacAskill MR, Pitcher TL, Livingston L, Keenan RJ, Dalrymple-Alford JC, Anderson TJ. White matter microstructure deteriorates across cognitive stages in Parkinson disease. Neurology, 2013; 80(20):1841-9, doi: 10.1212/WNL.0b013e3182929f62. Agosta F, Canu E, Stefanova E, Sarro L, Tomic A, Spica V, Comi G, Kostic VS, Filippi M. Mild cognitive impairment in Parkinson's disease is associated with a distributed pattern of brain white matter damage. Hum Brain Mapp, 2014; 35(5):1921-9, doi: 10.1002/hbm.22302. Carlesimo GA, Piras F, Assogna F, Pontieri FE, Caltagirone C, Spalletta G. Hippocampal abnormalities and memory deficits in Parkinson disease: a multimodal imaging study. Neurology, 2012; 78(24):1939-45, doi: 10.1212/WNL.0b013e318259e1c5. Baggio HC, Segura B, Ibarretxe-Bilbao N, Valldeoriola F, Marti MJ, Compta Y, Tolosa E, Junque C. Structural correlates of facial emotion recognition deficits in Parkinson's disease patients. Neuropsychologia, 2012; 50(8):2121-8, doi: 10.1016/j.neuropsychologia.2012.05.020. Bertrand JA, Bedetti C, Postuma RB, Monchi O, Genier Marchand D, Jubault T, Gagnon JF. Color discrimination deficits in Parkinson's disease are related to cognitive impairment and white-matter alterations. Mov Disord, 2012; 27(14):1781-8, doi: 10.1002/mds.25272. Feldmann A, Illes Z, Kosztolanyi P, Illes E, Mike A, Kover F, Balas I, Kovacs N, Nagy F. Morphometric changes of gray matter in Parkinson's disease with depression: a voxel-based morphometry study. Mov Disord, 2008; 23(1):42-6, doi: 10.1002/mds.21765. Kostic VS, Agosta F, Petrovic I, Galantucci S, Spica V, Jecmenica-Lukic M, Filippi M. Regional patterns of brain tissue loss associated with depression in Parkinson disease. Neurology, 2010; 75(10):857-63, doi: 10.1212/WNL.0b013e3181f11c1d. Surdhar I, Gee M, Bouchard T, Coupland N, Malykhin N, Camicioli R. Intact limbic-prefrontal connections and reduced amygdala volumes in Parkinson's disease with mild depressive symptoms. Parkinsonism Relat Disord, 2012; 18(7):809-13, doi: 10.1016/j.parkreldis.2012.03.008. Wang JJ, Lin WY, Lu CS, Weng YH, Ng SH, Wang CH, Liu HL, Hsieh RH, Wan YL, Wai YY. Parkinson disease: diagnostic utility of diffusion kurtosis imaging. Radiology, 2011; 261(1):210-7, doi: 10.1148/radiol.11102277. Kamagata K, Tomiyama H, Motoi Y, Kano M, Abe O, Ito K, Shimoji K, Suzuki M, Hori M, Nakanishi A, Kuwatsuru R, Sasai K, Aoki S, Hattori N. Diffusional kurtosis imaging of cingulate fibers in Parkinson disease: comparison with conventional diffusion tensor imaging. Magn Reson Imaging, 2013; 31(9):1501-6, doi: 10.1016/j.mri.2013.06.009.

AC

[142]

ACCEPTED MANUSCRIPT

[166] [167] [168]

[169]

[170] [171]

[172]

[173] [174]

[175]

[176]

[177]

[178]

T

RI P

SC

[165]

MA NU

[164]

ED

[162] [163]

PT

[161]

CE

[160]

Kamagata K, Tomiyama H, Hatano T, Motoi Y, Abe O, Shimoji K, Kamiya K, Suzuki M, Hori M, Yoshida M, Hattori N, Aoki S. A preliminary diffusional kurtosis imaging study of Parkinson disease: comparison with conventional diffusion tensor imaging. Neuroradiology, 2014; 56(3):251-8, doi: 10.1007/s00234-014-1327-1. Giannelli M, Toschi N, Passamonti L, Mascalchi M, Diciotti S, Tessa C. Diffusion kurtosis and diffusion-tensor MR imaging in Parkinson disease. Radiology, 2012; 265(2):645-6, doi: 10.1148/radiol.12121036. Strong M, Rosenfeld J. Amyotrophic lateral sclerosis: a review of current concepts. Amyotroph Lateral Scler Other Motor Neuron Disord, 2003; 4(3):136-43, doi: 10.1080/14660820310011250. Charcot JM. Des amyotrophies spinales chroniques. Progres Medical, 1874; 2:573–574. Musaro A. State of the art and the dark side of amyotrophic lateral sclerosis. World J Biol Chem, 2010; 1(5):62-8, doi: 10.4331/wjbc.v1.i5.62. Phukan J, Pender NP, Hardiman O. Cognitive impairment in amyotrophic lateral sclerosis. Lancet Neurol, 2007; 6(11):994-1003, doi: 10.1016/S1474-4422(07)70265-X. Geser F, Brandmeir NJ, Kwong LK, Martinez-Lage M, Elman L, McCluskey L, Xie SX, Lee VM, Trojanowski JQ. Evidence of multisystem disorder in whole-brain map of pathological TDP-43 in amyotrophic lateral sclerosis. Arch Neurol, 2008; 65(5):636-41, doi: 10.1001/archneur.65.5.636. Nakano I. Frontotemporal dementia with motor neuron disease (amyotrophic lateral sclerosis with dementia). Neuropathology, 2000; 20(1):68-75. Neumann M. Molecular neuropathology of TDP-43 proteinopathies. Int J Mol Sci, 2009; 10(1):23246, doi: 10.3390/ijms10010232. Ince PG, Evans J, Knopp M, Forster G, Hamdalla HH, Wharton SB, Shaw PJ. Corticospinal tract degeneration in the progressive muscular atrophy variant of ALS. Neurology, 2003; 60(8):1252-8, doi: 10.1212/01.WNL.0000058901.75728.4E. Ince PG, Lowe J, Shaw PJ. Amyotrophic lateral sclerosis: current issues in classification, pathogenesis and molecular pathology. Neuropathol Appl Neurobiol, 1998; 24(2):104-17, doi: 10.1046/j.1365-2990.1998.00108.x. Brownell B, Oppenheimer DR, Hughes JT. The central nervous system in motor neurone disease. J Neurol Neurosurg Psychiatry, 1970; 33(3):338-57, doi: 10.1136/jnnp.33.3.338. Chan S, Kaufmann P, Shungu DC, Mitsumoto H. Amyotrophic lateral sclerosis and primary lateral sclerosis: evidence-based diagnostic evaluation of the upper motor neuron. Neuroimaging Clin N Am, 2003; 13(2):307-26, doi: 10.1016/S1052-5149(03)00018-2. Filippi M, Agosta F, Abrahams S, Fazekas F, Grosskreutz J, Kalra S, Kassubek J, Silani V, Turner MR, Masdeu JC, European Federation of Neurological S. EFNS guidelines on the use of neuroimaging in the management of motor neuron diseases. Eur J Neurol, 2010; 17(4):526-e20, doi: 10.1111/j.1468-1331.2010.02951.x. Turner MR. MRI as a frontrunner in the search for amyotrophic lateral sclerosis biomarkers? Biomark Med, 2011; 5(1):79-81, doi: 10.2217/bmm.10.120. Cheung G, Gawel MJ, Cooper PW, Farb RI, Ang LC, Gawal MJ. Amyotrophic lateral sclerosis: correlation of clinical and MR imaging findings. Radiology, 1995; 194(1):263-70, doi: 10.1148/radiology.194.1.7997565. Hecht MJ, Fellner F, Fellner C, Hilz MJ, Neundorfer B, Heuss D. Hyperintense and hypointense MRI signals of the precentral gyrus and corticospinal tract in ALS: a follow-up examination including FLAIR images. J Neurol Sci, 2002; 199(1-2):59-65, doi: 10.1016/S0022-510X(02)001041. Agosta F, Pagani E, Rocca MA, Caputo D, Perini M, Salvi F, Prelle A, Filippi M. Voxel-based morphometry study of brain volumetry and diffusivity in amyotrophic lateral sclerosis patients with mild disability. Hum Brain Mapp, 2007; 28(12):1430-8, doi: 10.1002/hbm.20364. Mezzapesa DM, Ceccarelli A, Dicuonzo F, Carella A, De Caro MF, Lopez M, Samarelli V, Livrea P, Simone IL. Whole-brain and regional brain atrophy in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol, 2007; 28(2):255-9. Agosta F, Valsasina P, Riva N, Copetti M, Messina MJ, Prelle A, Comi G, Filippi M. The cortical signature of amyotrophic lateral sclerosis. PLoS One, 2012; 7(8):e42816, doi: 10.1371/journal.pone.0042816.

AC

[159]

ACCEPTED MANUSCRIPT

[184]

[185]

[186]

[187]

[188]

[189]

[190]

[191] [192]

[193]

[194]

[195]

T

RI P

SC

MA NU

[183]

ED

[182]

PT

[181]

CE

[180]

Cosottini M, Pesaresi I, Piazza S, Diciotti S, Cecchi P, Fabbri S, Carlesi C, Mascalchi M, Siciliano G. Structural and functional evaluation of cortical motor areas in Amyotrophic Lateral Sclerosis. Exp Neurol, 2012; 234(1):169-80, doi: 10.1016/j.expneurol.2011.12.024. Konrad C, Jansen A, Henningsen H, Sommer J, Turski PA, Brooks BR, Knecht S. Subcortical reorganization in amyotrophic lateral sclerosis. Exp Brain Res, 2006; 172(3):361-9, doi: 10.1007/s00221-006-0352-7. Stanton BR, Williams VC, Leigh PN, Williams SC, Blain CR, Jarosz JM, Simmons A. Altered cortical activation during a motor task in ALS. Evidence for involvement of central pathways. J Neurol, 2007; 254(9):1260-7, doi: 10.1007/s00415-006-0513-4. Cosottini M, Pesaresi I, Piazza S, Diciotti S, Belmonte G, Battaglini M, Ginestroni A, Siciliano G, De Stefano N, Mascalchi M. Magnetization transfer imaging demonstrates a distributed pattern of microstructural changes of the cerebral cortex in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol, 2011; 32(4):704-8, doi: 10.3174/ajnr.A2356. Ellis CM, Simmons A, Jones DK, Bland J, Dawson JM, Horsfield MA, Williams SC, Leigh PN. Diffusion tensor MRI assesses corticospinal tract damage in ALS. Neurology, 1999; 53(5):1051-8, doi: 10.1212/WNL.53.5.1051. Toosy AT, Werring DJ, Orrell RW, Howard RS, King MD, Barker GJ, Miller DH, Thompson AJ. Diffusion tensor imaging detects corticospinal tract involvement at multiple levels in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry, 2003; 74(9):1250-7, doi: 10.1136/jnnp.74.9.1250. Cosottini M, Giannelli M, Siciliano G, Lazzarotti G, Michelassi MC, Del Corona A, Bartolozzi C, Murri L. Diffusion-tensor MR imaging of corticospinal tract in amyotrophic lateral sclerosis and progressive muscular atrophy. Radiology, 2005; 237(1):258-64, doi: 10.1148/radiol.2371041506. Charil A, Corbo M, Filippi M, Kesavadas C, Agosta F, Munerati E, Gambini A, Comi G, Scotti G, Falini A. Structural and metabolic changes in the brain of patients with upper motor neuron disorders: a multiparametric MRI study. Amyotroph Lateral Scler, 2009; 10(5-6):269-79, doi: 10.3109/17482960902777339. Graham JM, Papadakis N, Evans J, Widjaja E, Romanowski CA, Paley MN, Wallis LI, Wilkinson ID, Shaw PJ, Griffiths PD. Diffusion tensor imaging for the assessment of upper motor neuron integrity in ALS. Neurology, 2004; 63(11):2111-9, doi: 10.1212/01.WNL.0000145766.03057.E7. Schimrigk SK, Bellenberg B, Schluter M, Stieltjes B, Drescher R, Rexilius J, Lukas C, Hahn HK, Przuntek H, Koster O. Diffusion tensor imaging-based fractional anisotropy quantification in the corticospinal tract of patients with amyotrophic lateral sclerosis using a probabilistic mixture model. AJNR Am J Neuroradiol, 2007; 28(4):724-30. Iwata NK, Aoki S, Okabe S, Arai N, Terao Y, Kwak S, Abe O, Kanazawa I, Tsuji S, Ugawa Y. Evaluation of corticospinal tracts in ALS with diffusion tensor MRI and brainstem stimulation. Neurology, 2008; 70(7):528-32, doi: 10.1212/01.wnl.0000299186.72374.19. Pierpaoli C, Barnett A, Pajevic S, Chen R, Penix LR, Virta A, Basser P. Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. Neuroimage, 2001; 13(6 Pt 1):1174-85, doi: 10.1006/nimg.2001.0765. Beaulieu C, Does MD, Snyder RE, Allen PS. Changes in water diffusion due to Wallerian degeneration in peripheral nerve. Magn Reson Med, 1996; 36(4):627-31. Wang S, Poptani H, Woo JH, Desiderio LM, Elman LB, McCluskey LF, Krejza J, Melhem ER. Amyotrophic lateral sclerosis: diffusion-tensor and chemical shift MR imaging at 3.0 T. Radiology, 2006; 239(3):831-8, doi: 10.1148/radiol.2393050573. Mitsumoto H, Ulug AM, Pullman SL, Gooch CL, Chan S, Tang MX, Mao X, Hays AP, Floyd AG, Battista V, Montes J, Hayes S, Dashnaw S, Kaufmann P, Gordon PH, Hirsch J, Levin B, Rowland LP, Shungu DC. Quantitative objective markers for upper and lower motor neuron dysfunction in ALS. Neurology, 2007; 68(17):1402-10, doi: 10.1212/01.wnl.0000260065.57832.87. Ciccarelli O, Behrens TE, Altmann DR, Orrell RW, Howard RS, Johansen-Berg H, Miller DH, Matthews PM, Thompson AJ. Probabilistic diffusion tractography: a potential tool to assess the rate of disease progression in amyotrophic lateral sclerosis. Brain, 2006; 129(Pt 7):1859-71, doi: 10.1093/brain/awl100. Sage CA, Peeters RR, Gorner A, Robberecht W, Sunaert S. Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis. Neuroimage, 2007; 34(2):486-99, doi: 10.1016/j.neuroimage.2006.09.025.

AC

[179]

ACCEPTED MANUSCRIPT

[201]

[202]

[203]

[204]

[205]

[206]

[207]

[208]

[209]

[210]

[211]

[212]

T

RI P

SC

MA NU

[200]

ED

[199]

PT

[198]

CE

[197]

Filippini N, Douaud G, Mackay CE, Knight S, Talbot K, Turner MR. Corpus callosum involvement is a consistent feature of amyotrophic lateral sclerosis. Neurology, 2010; 75(18):1645-52, doi: 10.1212/WNL.0b013e3181fb84d1. Verstraete E, van den Heuvel MP, Veldink JH, Blanken N, Mandl RC, Hulshoff Pol HE, van den Berg LH. Motor network degeneration in amyotrophic lateral sclerosis: a structural and functional connectivity study. PLoS One, 2010; 5(10):e13664, doi: 10.1371/journal.pone.0013664. Li J, Pan P, Song W, Huang R, Chen K, Shang H. A meta-analysis of diffusion tensor imaging studies in amyotrophic lateral sclerosis. Neurobiol Aging, 2012; 33(8):1833-8, doi: 10.1016/j.neurobiolaging.2011.04.007. Cosottini M, Cecchi P, Piazza S, Pesaresi I, Fabbri S, Diciotti S, Mascalchi M, Siciliano G, Bonuccelli U. Mapping cortical degeneration in ALS with magnetization transfer ratio and voxelbased morphometry. PLoS One, 2013; 8(7):e68279, doi: 10.1371/journal.pone.0068279. Kassubek J, Unrath A, Huppertz HJ, Lule D, Ethofer T, Sperfeld AD, Ludolph AC. Global brain atrophy and corticospinal tract alterations in ALS, as investigated by voxel-based morphometry of 3D MRI. Amyotroph Lateral Scler Other Motor Neuron Disord, 2005; 6(4):213-20, doi: 10.1080/14660820510038538. Sage CA, Van Hecke W, Peeters R, Sijbers J, Robberecht W, Parizel P, Marchal G, Leemans A, Sunaert S. Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited. Hum Brain Mapp, 2009; 30(11):3657-75, doi: 10.1002/hbm.20794. van der Graaff MM, Sage CA, Caan MW, Akkerman EM, Lavini C, Majoie CB, Nederveen AJ, Zwinderman AH, Vos F, Brugman F, van den Berg LH, de Rijk MC, van Doorn PA, Van Hecke W, Peeters RR, Robberecht W, Sunaert S, de Visser M. Upper and extra-motoneuron involvement in early motoneuron disease: a diffusion tensor imaging study. Brain, 2011; 134(Pt 4):1211-28, doi: 10.1093/brain/awr016. Canu E, Agosta F, Riva N, Sala S, Prelle A, Caputo D, Perini M, Comi G, Filippi M. The topography of brain microstructural damage in amyotrophic lateral sclerosis assessed using diffusion tensor MR imaging. AJNR Am J Neuroradiol, 2011; 32(7):1307-14, doi: 10.3174/ajnr.A2469. Lillo P, Mioshi E, Burrell JR, Kiernan MC, Hodges JR, Hornberger M. Grey and white matter changes across the amyotrophic lateral sclerosis-frontotemporal dementia continuum. PLoS One, 2012; 7(8):e43993, doi: 10.1371/journal.pone.0043993. Sarro L, Agosta F, Canu E, Riva N, Prelle A, Copetti M, Riccitelli G, Comi G, Filippi M. Cognitive functions and white matter tract damage in amyotrophic lateral sclerosis: a diffusion tensor tractography study. AJNR Am J Neuroradiol, 2011; 32(10):1866-72, doi: 10.3174/ajnr.A2658. Crespi C, Cerami C, Dodich A, Canessa N, Arpone M, Iannaccone S, Corbo M, Lunetta C, Scola E, Falini A, Cappa SF. Microstructural white matter correlates of emotion recognition impairment in Amyotrophic Lateral Sclerosis. Cortex, 2014; 53:1-8, doi: 10.1016/j.cortex.2014.01.002. Agosta F, Rocca MA, Valsasina P, Sala S, Caputo D, Perini M, Salvi F, Prelle A, Filippi M. A longitudinal diffusion tensor MRI study of the cervical cord and brain in amyotrophic lateral sclerosis patients. J Neurol Neurosurg Psychiatry, 2009; 80(1):53-5, doi: 10.1136/jnnp.2008.154252. Valsasina P, Agosta F, Benedetti B, Caputo D, Perini M, Salvi F, Prelle A, Filippi M. Diffusion anisotropy of the cervical cord is strictly associated with disability in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry, 2007; 78(5):480-4, doi: 10.1136/jnnp.2006.100032. Wang Y, Liu L, Ma L, Huang X, Lou X, Wang Y, Wu N, Liu T, Guo X. Preliminary Study on Cervical Spinal Cord in Patients with Amyotrophic Lateral Sclerosis Using MR Diffusion Tensor Imaging. Acad Radiol, 2014; 21(5):590-6, doi: 10.1016/j.acra.2014.01.014. Sach M, Winkler G, Glauche V, Liepert J, Heimbach B, Koch MA, Buchel C, Weiller C. Diffusion tensor MRI of early upper motor neuron involvement in amyotrophic lateral sclerosis. Brain, 2004; 127(Pt 2):340-50, doi: 10.1093/brain/awh041. Ng MC, Ho JT, Ho SL, Lee R, Li G, Cheng TS, Song YQ, Ho PW, Fong GC, Mak W, Chan KH, Li LS, Luk KD, Hu Y, Ramsden DB, Leong LL. Abnormal diffusion tensor in nonsymptomatic familial amyotrophic lateral sclerosis with a causative superoxide dismutase 1 mutation. J Magn Reson Imaging, 2008; 27(1):8-13, doi: 10.1002/jmri.21217. Iwata NK, Kwan JY, Danielian LE, Butman JA, Tovar-Moll F, Bayat E, Floeter MK. White matter alterations differ in primary lateral sclerosis and amyotrophic lateral sclerosis. Brain, 2011; 134(Pt 9):2642-55, doi: 10.1093/brain/awr178.

AC

[196]

ACCEPTED MANUSCRIPT

[219]

[220]

[221]

[222]

[223]

[224] [225]

[226]

[227]

[228]

[229]

T

RI P

SC

[218]

MA NU

[217]

ED

[216]

PT

[215]

CE

[214]

Filippi M, van den Heuvel MP, Fornito A, He Y, Hulshoff Pol HE, Agosta F, Comi G, Rocca MA. Assessment of system dysfunction in the brain through MRI-based connectomics. Lancet Neurol, 2013; 12(12):1189-99, doi: 10.1016/S1474-4422(13)70144-3. Agosta F, Galantucci S, Riva N, Chio A, Messina S, Iannaccone S, Calvo A, Silani V, Copetti M, Falini A, Comi G, Filippi M. Intrahemispheric and interhemispheric structural network abnormalities in PLS and ALS. Hum Brain Mapp, 2014; 35(4):1710-22, doi: 10.1002/hbm.22286. Blain CR, Williams VC, Johnston C, Stanton BR, Ganesalingam J, Jarosz JM, Jones DK, Barker GJ, Williams SC, Leigh NP, Simmons A. A longitudinal study of diffusion tensor MRI in ALS. Amyotroph Lateral Scler, 2007; 8(6):348-55, doi: 10.1080/17482960701548139. Menke RA, Abraham I, Thiel CS, Filippini N, Knight S, Talbot K, Turner MR. Fractional anisotropy in the posterior limb of the internal capsule and prognosis in amyotrophic lateral sclerosis. Arch Neurol, 2012; 69(11):1493-9, doi: 10.1001/archneurol.2012.1122. Agosta F, Pagani E, Petrolini M, Sormani MP, Caputo D, Perini M, Prelle A, Salvi F, Filippi M. MRI predictors of long-term evolution in amyotrophic lateral sclerosis. Eur J Neurosci, 2010; 32(9):1490-6, doi: 10.1111/j.1460-9568.2010.07445.x. Keil C, Prell T, Peschel T, Hartung V, Dengler R, Grosskreutz J. Longitudinal diffusion tensor imaging in amyotrophic lateral sclerosis. BMC Neurosci, 2012; 13:141, doi: 10.1186/1471-2202-13141. Verstraete E, Veldink JH, Mandl RC, van den Berg LH, van den Heuvel MP. Impaired structural motor connectome in amyotrophic lateral sclerosis. PLoS One, 2011; 6(9):e24239, doi: 10.1371/journal.pone.0024239. Verstraete E, Veldink JH, van den Berg LH, van den Heuvel MP. Structural brain network imaging shows expanding disconnection of the motor system in amyotrophic lateral sclerosis. Hum Brain Mapp, 2014; 35(4):1351-61, doi: 10.1002/hbm.22258. Foerster BR, Dwamena BA, Petrou M, Carlos RC, Callaghan BC, Pomper MG. Diagnostic accuracy using diffusion tensor imaging in the diagnosis of ALS: a meta-analysis. Acad Radiol, 2012; 19(9):1075-86, doi: 10.1016/j.acra.2012.04.012. Chio A, Calvo A, Moglia C, Mazzini L, Mora G, group Ps. Phenotypic heterogeneity of amyotrophic lateral sclerosis: a population based study. J Neurol Neurosurg Psychiatry, 2011; 82(7):740-6, doi: 10.1136/jnnp.2010.235952. Prell T, Peschel T, Hartung V, Kaufmann J, Klauschies R, Bodammer N, Kollewe K, Dengler R, Grosskreutz J. Diffusion tensor imaging patterns differ in bulbar and limb onset amyotrophic lateral sclerosis. Clin Neurol Neurosurg, 2013; 115(8):1281-7, doi: 10.1016/j.clineuro.2012.11.031. Takahashi T, Katada S, Onodera O. Polyglutamine diseases: where does toxicity come from? what is toxicity? where are we going? J Mol Cell Biol, 2010; 2(4):180-91, doi: 10.1093/jmcb/mjq005. Bohanna I, Georgiou-Karistianis N, Hannan AJ, Egan GF. Magnetic resonance imaging as an approach towards identifying neuropathological biomarkers for Huntington's disease. Brain Res Rev, 2008; 58(1):209-25, doi: 10.1016/j.brainresrev.2008.04.001. Kloppel S, Chu C, Tan GC, Draganski B, Johnson H, Paulsen JS, Kienzle W, Tabrizi SJ, Ashburner J, Frackowiak RS, Group P-HIotHS. Automatic detection of preclinical neurodegeneration: presymptomatic Huntington disease. Neurology, 2009; 72(5):426-31, doi: 10.1212/01.wnl.0000341768.28646.b6. Tabrizi SJ, Langbehn DR, Leavitt BR, Roos RA, Durr A, Craufurd D, Kennard C, Hicks SL, Fox NC, Scahill RI, Borowsky B, Tobin AJ, Rosas HD, Johnson H, Reilmann R, Landwehrmeyer B, Stout JC, investigators T-H. Biological and clinical manifestations of Huntington's disease in the longitudinal TRACK-HD study: cross-sectional analysis of baseline data. Lancet Neurol, 2009; 8(9):791-801, doi: 10.1016/S1474-4422(09)70170-X. Kloppel S, Draganski B, Golding CV, Chu C, Nagy Z, Cook PA, Hicks SL, Kennard C, Alexander DC, Parker GJ, Tabrizi SJ, Frackowiak RS. White matter connections reflect changes in voluntaryguided saccades in pre-symptomatic Huntington's disease. Brain, 2008; 131(Pt 1):196-204, doi: 10.1093/brain/awm275. Reading SA, Yassa MA, Bakker A, Dziorny AC, Gourley LM, Yallapragada V, Rosenblatt A, Margolis RL, Aylward EH, Brandt J, Mori S, van Zijl P, Bassett SS, Ross CA. Regional white matter change in pre-symptomatic Huntington's disease: a diffusion tensor imaging study. Psychiatry Res, 2005; 140(1):55-62, doi: 10.1016/j.pscychresns.2005.05.011.

AC

[213]

ACCEPTED MANUSCRIPT

[235]

[236]

[237]

[238]

[239]

[240]

[241]

[242]

[243]

[244]

[245]

T

RI P

SC

MA NU

[234]

ED

[233]

PT

[232]

CE

[231]

Rosas HD, Tuch DS, Hevelone ND, Zaleta AK, Vangel M, Hersch SM, Salat DH. Diffusion tensor imaging in presymptomatic and early Huntington's disease: Selective white matter pathology and its relationship to clinical measures. Mov Disord, 2006; 21(9):1317-25, doi: 10.1002/mds.20979. Dumas EM, van den Bogaard SJ, Ruber ME, Reilman RR, Stout JC, Craufurd D, Hicks SL, Kennard C, Tabrizi SJ, van Buchem MA, van der Grond J, Roos RA. Early changes in white matter pathways of the sensorimotor cortex in premanifest Huntington's disease. Hum Brain Mapp, 2012; 33(1):20312, doi: 10.1002/hbm.21205. Mandelli ML, Savoiardo M, Minati L, Mariotti C, Aquino D, Erbetta A, Genitrini S, Di Donato S, Bruzzone MG, Grisoli M. Decreased diffusivity in the caudate nucleus of presymptomatic huntington disease gene carriers: which explanation? AJNR Am J Neuroradiol, 2010; 31(4):706-10, doi: 10.3174/ajnr.A1891. Rizk-Jackson A, Stoffers D, Sheldon S, Kuperman J, Dale A, Goldstein J, Corey-Bloom J, Poldrack RA, Aron AR. Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington's disease using machine learning techniques. Neuroimage, 2011; 56(2):788-96, doi: 10.1016/j.neuroimage.2010.04.273. Rosas HD, Lee SY, Bender AC, Zaleta AK, Vangel M, Yu P, Fischl B, Pappu V, Onorato C, Cha JH, Salat DH, Hersch SM. Altered white matter microstructure in the corpus callosum in Huntington's disease: implications for cortical "disconnection". Neuroimage, 2010; 49(4):2995-3004, doi: 10.1016/j.neuroimage.2009.10.015. Stoffers D, Sheldon S, Kuperman JM, Goldstein J, Corey-Bloom J, Aron AR. Contrasting gray and white matter changes in preclinical Huntington disease: an MRI study. Neurology, 2010; 74(15):1208-16, doi: 10.1212/WNL.0b013e3181d8c20a. Di Paola M, Luders E, Cherubini A, Sanchez-Castaneda C, Thompson PM, Toga AW, Caltagirone C, Orobello S, Elifani F, Squitieri F, Sabatini U. Multimodal MRI analysis of the corpus callosum reveals white matter differences in presymptomatic and early Huntington's disease. Cereb Cortex, 2012; 22(12):2858-66, doi: 10.1093/cercor/bhr360. Sanchez-Castaneda C, Cherubini A, Elifani F, Peran P, Orobello S, Capelli G, Sabatini U, Squitieri F. Seeking Huntington disease biomarkers by multimodal, cross-sectional basal ganglia imaging. Hum Brain Mapp, 2013; 34(7):1625-35, doi: 10.1002/hbm.22019. Dominguez DJ, Egan GF, Gray MA, Poudel GR, Churchyard A, Chua P, Stout JC, GeorgiouKaristianis N. Multi-modal neuroimaging in premanifest and early Huntington's disease: 18 month longitudinal data from the IMAGE-HD study. PLoS One, 2013; 8(9):e74131, doi: 10.1371/journal.pone.0074131. Novak MJ, Seunarine KK, Gibbard CR, Hobbs NZ, Scahill RI, Clark CA, Tabrizi SJ. White matter integrity in premanifest and early Huntington's disease is related to caudate loss and disease progression. Cortex, 2014; 52:98-112, doi: 10.1016/j.cortex.2013.11.009. Douaud G, Behrens TE, Poupon C, Cointepas Y, Jbabdi S, Gaura V, Golestani N, Krystkowiak P, Verny C, Damier P, Bachoud-Levi AC, Hantraye P, Remy P. In vivo evidence for the selective subcortical degeneration in Huntington's disease. Neuroimage, 2009; 46(4):958-66, doi: 10.1016/j.neuroimage.2009.03.044. Mascalchi M, Lolli F, Della Nave R, Tessa C, Petralli R, Gavazzi C, Politi LS, Macucci M, Filippi M, Piacentini S. Huntington disease: volumetric, diffusion-weighted, and magnetization transfer MR imaging of brain. Radiology, 2004; 232(3):867-73, doi: 10.1148/radiol.2322030820. Seppi K, Schocke MF, Mair KJ, Esterhammer R, Weirich-Schwaiger H, Utermann B, Egger K, Brenneis C, Granata R, Boesch S, Poewe W, Wenning GK. Diffusion-weighted imaging in Huntington's disease. Mov Disord, 2006; 21(7):1043-7, doi: 10.1002/mds.20868. Della Nave R, Ginestroni A, Tessa C, Giannelli M, Piacentini S, Filippi M, Mascalchi M. Regional distribution and clinical correlates of white matter structural damage in Huntington disease: a tractbased spatial statistics study. AJNR Am J Neuroradiol, 2010; 31(9):1675-81, doi: 10.3174/ajnr.A2128. Rees EM, Farmer R, Cole JH, Haider S, Durr A, Landwehrmeyer B, Scahill RI, Tabrizi SJ, Hobbs NZ. Cerebellar abnormalities in Huntington's disease: a role in motor and psychiatric impairment? Mov Disord, 2014; 29(13):1648-54, doi: 10.1002/mds.25984. Phillips O, Squitieri F, Sanchez-Castaneda C, Elifani F, Griguoli A, Maglione V, Caltagirone C, Sabatini U, Di Paola M. The corticospinal tract in Huntington's disease. Cereb Cortex, 2014, doi: 10.1093/cercor/bhu065.

AC

[230]

ACCEPTED MANUSCRIPT

[251]

[252]

[253] [254] [255]

[256]

[257]

[258]

[259]

[260]

[261]

[262]

T

RI P

SC

MA NU

[250]

ED

[249]

PT

[248]

CE

[247]

Georgiou-Karistianis N, Gray MA, Dominguez DJ, Dymowski AR, Bohanna I, Johnston LA, Churchyard A, Chua P, Stout JC, Egan GF. Automated differentiation of pre-diagnosis Huntington's disease from healthy control individuals based on quadratic discriminant analysis of the basal ganglia: the IMAGE-HD study. Neurobiol Dis, 2013; 51:82-92, doi: 10.1016/j.nbd.2012.10.001. Poudel GR, Stout JC, Dominguez DJ, Salmon L, Churchyard A, Chua P, Georgiou-Karistianis N, Egan GF. White matter connectivity reflects clinical and cognitive status in Huntington's disease. Neurobiol Dis, 2014; 65:180-7, doi: 10.1016/j.nbd.2014.01.013. Blockx I, Verhoye M, Van Audekerke J, Bergwerf I, Kane JX, Delgado YPR, Veraart J, Jeurissen B, Raber K, von Horsten S, Ponsaerts P, Sijbers J, Leergaard TB, Van der Linden A. Identification and characterization of Huntington related pathology: an in vivo DKI imaging study. Neuroimage, 2012; 63(2):653-62, doi: 10.1016/j.neuroimage.2012.06.032. Weaver KE, Richards TL, Liang O, Laurino MY, Samii A, Aylward EH. Longitudinal diffusion tensor imaging in Huntington's Disease. Exp Neurol, 2009; 216(2):525-9, doi: 10.1016/j.expneurol.2008.12.026. Sritharan A, Egan GF, Johnston L, Horne M, Bradshaw JL, Bohanna I, Asadi H, Cunnington R, Churchyard AJ, Chua P, Farrow M, Georgiou-Karistianis N. A longitudinal diffusion tensor imaging study in symptomatic Huntington's disease. J Neurol Neurosurg Psychiatry, 2010; 81(3):257-62, doi: 10.1136/jnnp.2007.142786. Vandenberghe W, Demaerel P, Dom R, Maes F. Diffusion-weighted versus volumetric imaging of the striatum in early symptomatic Huntington disease. J Neurol, 2009; 256(1):109-14, doi: 10.1007/s00415-009-0086-0. Rosas HD, Doros G, Gevorkian S, Malarick K, Reuter M, Coutu JP, Triggs TD, Wilkens PJ, Matson W, Salat DH, Hersch SM. PRECREST: A phase II prevention and biomarker trial of creatine in atrisk Huntington disease. Neurology, 2014; 82(10):850-7, doi: 10.1212/WNL.0000000000000187. Mascalchi M, Vella A. Magnetic resonance and nuclear medicine imaging in ataxias. Handb Clin Neurol, 2012; 103:85-110, doi: 10.1016/B978-0-444-51892-7.00004-8. Mascalchi M, Filippi M, Floris R, Fonda C, Gasparotti R, Villari N. Diffusion-weighted MR of the brain: methodology and clinical application. Radiol Med, 2005; 109(3):155-97, doi: Della Nave R, Foresti S, Tessa C, Moretti M, Ginestroni A, Gavazzi C, Guerrini L, Salvi F, Piacentini S, Mascalchi M. ADC mapping of neurodegeneration in the brainstem and cerebellum of patients with progressive ataxias. Neuroimage, 2004; 22(2):698-705, doi: 10.1016/j.neuroimage.2004.01.035. Mandelli ML, De Simone T, Minati L, Bruzzone MG, Mariotti C, Fancellu R, Savoiardo M, Grisoli M. Diffusion tensor imaging of spinocerebellar ataxias types 1 and 2. AJNR Am J Neuroradiol, 2007; 28(10):1996-2000, doi: 10.3174/ajnr.A0716. Pellecchia MT, Barone P, Mollica C, Salvatore E, Ianniciello M, Longo K, Varrone A, Vicidomini C, Picillo M, De Michele G, Filla A, Salvatore M, Pappata S. Diffusion-weighted imaging in multiple system atrophy: a comparison between clinical subtypes. Mov Disord, 2009; 24(5):689-96, doi: 10.1002/mds.22440. Pellecchia MT, Barone P, Vicidomini C, Mollica C, Salvatore E, Ianniciello M, Liuzzi R, Longo K, Picillo M, De Michele G, Filla A, Brunetti A, Salvatore M, Pappata S. Progression of striatal and extrastriatal degeneration in multiple system atrophy: a longitudinal diffusion-weighted MR study. Mov Disord, 2011; 26(7):1303-9, doi: 10.1002/mds.23601. Rizzo G, Tonon C, Valentino ML, Manners D, Fortuna F, Gellera C, Pini A, Ghezzo A, Baruzzi A, Testa C, Malucelli E, Barbiroli B, Carelli V, Lodi R. Brain diffusion-weighted imaging in Friedreich's ataxia. Mov Disord, 2011; 26(4):705-12, doi: 10.1002/mds.23518. Della Nave R, Ginestroni A, Tessa C, Salvatore E, De Grandis D, Plasmati R, Salvi F, De Michele G, Dotti MT, Piacentini S, Mascalchi M. Brain white matter damage in SCA1 and SCA2. An in vivo study using voxel-based morphometry, histogram analysis of mean diffusivity and tract-based spatial statistics. Neuroimage, 2008; 43(1):10-9, doi: 10.1016/j.neuroimage.2008.06.036. Della Nave R, Ginestroni A, Giannelli M, Tessa C, Salvatore E, Salvi F, Dotti MT, De Michele G, Piacentini S, Mascalchi M. Brain structural damage in Friedreich's ataxia. J Neurol Neurosurg Psychiatry, 2008; 79(1):82-5, doi: 10.1136/jnnp.2007.124297. Pagani E, Ginestroni A, Della Nave R, Agosta F, Salvi F, De Michele G, Piacentini S, Filippi M, Mascalchi M. Assessment of brain white matter fiber bundle atrophy in patients with Friedreich ataxia. Radiology, 2010; 255(3):882-9, doi: 10.1148/radiol.10091742.

AC

[246]

ACCEPTED MANUSCRIPT [263]

[264]

[271]

[272]

[273]

[274] [275]

[276] [277]

[278]

[279]

[280] [281]

SC

MA NU

[270]

ED

[269]

PT

[268]

CE

[267]

AC

[266]

RI P

T

[265]

Alcauter S, Barrios FA, Diaz R, Fernandez-Ruiz J. Gray and white matter alterations in spinocerebellar ataxia type 7: an in vivo DTI and VBM study. Neuroimage, 2011; 55(1):1-7, doi: 10.1016/j.neuroimage.2010.12.014. Sahama I, Sinclair K, Fiori S, Pannek K, Lavin M, Rose S. Altered corticomotor-cerebellar integrity in young ataxia telangiectasia patients. Mov Disord, 2014; 29(10):1289-98, doi: 10.1002/mds.25970. Mascalchi M, Toschi N, Giannelli M, Ginestroni A, Della Nave R, Nicolai E, Bianchi A, Tessa C, Salvatore E, Aiello M, Soricelli A, Diciotti S. Progression of microstructural damage in spinocerebellar ataxia type 2. A longitudinal diffusion tensor study. AJNR Am J Neuroradiol 2015, doi: 10.3174/ajnr.A4343. Prakash N, Hageman N, Hua X, Toga AW, Perlman SL, Salamon N. Patterns of fractional anisotropy changes in white matter of cerebellar peduncles distinguish spinocerebellar ataxia-1 from multiple system atrophy and other ataxia syndromes. Neuroimage, 2009; 47 Suppl 2:T72-81, doi: 10.1016/j.neuroimage.2009.05.013. Taoka T, Kin T, Nakagawa H, Hirano M, Sakamoto M, Wada T, Takayama K, Wuttikul C, Iwasaki S, Ueno S, Kichikawa K. Diffusivity and diffusion anisotropy of cerebellar peduncles in cases of spinocerebellar degenerative disease. Neuroimage, 2007; 37(2):387-93, doi: 10.1016/j.neuroimage.2007.05.028. Koeppen AH. Friedreich's ataxia: pathology, pathogenesis, and molecular genetics. J Neurol Sci, 2011; 303(1-2):1-12, doi: 10.1016/j.jns.2011.01.010. Zalesky A, Akhlaghi H, Corben LA, Bradshaw JL, Delatycki MB, Storey E, Georgiou-Karistianis N, Egan GF. Cerebello-cerebral connectivity deficits in Friedreich ataxia. Brain Struct Funct, 2013, doi: 10.1007/s00429-013-0547-1. Clemm von Hohenberg C, Schocke MF, Wigand MC, Nachbauer W, Guttmann CR, Kubicki M, Shenton ME, Boesch S, Egger K. Radial diffusivity in the cerebellar peduncles correlates with clinical severity in Friedreich ataxia. Neurol Sci, 2013; 34(8):1459-62, doi: 10.1007/s10072-0131402-0. Akhlaghi H, Yu J, Corben L, Georgiou-Karistianis N, Bradshaw JL, Storey E, Delatycki MB, Egan GF. Cognitive deficits in Friedreich ataxia correlate with micro-structural changes in dentatorubral tract. Cerebellum, 2014; 13(2):187-98, doi: 10.1007/s12311-013-0525-4. Della Nave R, Ginestroni A, Diciotti S, Salvatore E, Soricelli A, Mascalchi M. Axial diffusivity is increased in the degenerating superior cerebellar peduncles of Friedreich's ataxia. Neuroradiology, 2011; 53(5):367-72, doi: 10.1007/s00234-010-0807-1. Salat DH, Tuch DS, van der Kouwe AJ, Greve DN, Pappu V, Lee SY, Hevelone ND, Zaleta AK, Growdon JH, Corkin S, Fischl B, Rosas HD. White matter pathology isolates the hippocampal formation in Alzheimer's disease. Neurobiol Aging, 2010; 31(2):244-56, doi: 10.1016/j.neurobiolaging.2008.03.013. Lowe J, Lennox, G., Leigh, P.N. Disorders of movement and system degeneration, in Greenfield’s Neuropathology, D.L. Graham, Lantos, P.L., Editor. 1997, Arnold: London, England. 281-366, doi: Firat AK, Karakas HM, Firat Y, Yakinci C. Quantitative evaluation of brain involvement in ataxia telangiectasia by diffusion weighted MR imaging. Eur J Radiol, 2005; 56(2):192-6, doi: 10.1016/j.ejrad.2005.04.009. Seidel K, Siswanto S, Brunt ER, den Dunnen W, Korf HW, Rub U. Brain pathology of spinocerebellar ataxias. Acta Neuropathol, 2012; 124(1):1-21, doi: 10.1007/s00401-012-1000-x. Reginold W, Lang AE, Marras C, Heyn C, Alharbi M, Mikulis DJ. Longitudinal quantitative MRI in multiple system atrophy and progressive supranuclear palsy. Parkinsonism Relat Disord, 2014; 20(2):222-5, doi: 10.1016/j.parkreldis.2013.10.002. Guimaraes RP, D'Abreu A, Yasuda CL, Franca MC, Jr., Silva BH, Cappabianco FA, Bergo FP, Lopes-Cendes IT, Cendes F. A multimodal evaluation of microstructural white matter damage in spinocerebellar ataxia type 3. Mov Disord, 2013; 28(8):1125-32, doi: 10.1002/mds.25451. Lu CF, Soong BW, Wu HM, Teng S, Wang PS, Wu YT. Disrupted cerebellar connectivity reduces whole-brain network efficiency in multiple system atrophy. Mov Disord, 2013; 28(3):362-9, doi: 10.1002/mds.25314. Kang JS, Klein JC, Baudrexel S, Deichmann R, Nolte D, Hilker R. White matter damage is related to ataxia severity in SCA3. J Neurol, 2014; 261(2):291-9, doi: 10.1007/s00415-013-7186-6. Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci, 2003; 4(6):469-80, doi: 10.1038/nrn1119.

ACCEPTED MANUSCRIPT [282]

[290]

[291]

[292]

[293]

[294]

[295] [296] [297]

ED

[289]

PT

[288]

CE

[287]

AC

[286]

MA NU

SC

[285]

RI P

T

[283] [284]

Le Bihan D, Johansen-Berg H. Diffusion MRI at 25: exploring brain tissue structure and function. Neuroimage, 2012; 61(2):324-41, doi: 10.1016/j.neuroimage.2011.11.006. Sporns O. Discovering the human connectome. 2012: MIT press. Assaf Y, Alexander DC, Jones DK, Bizzi A, Behrens TE, Clark CA, Cohen Y, Dyrby TB, Huppi PS, Knoesche TR, Lebihan D, Parker GJ, Poupon C, consortium C, Anaby D, Anwander A, Bar L, Barazany D, Blumenfeld-Katzir T, De-Santis S, Duclap D, Figini M, Fischi E, Guevara P, Hubbard P, Hofstetter S, Jbabdi S, Kunz N, Lazeyras F, Lebois A, Liptrot MG, Lundell H, Mangin JF, Dominguez DM, Morozov D, Schreiber J, Seunarine K, Nava S, Poupon C, Riffert T, Sasson E, Schmitt B, Shemesh N, Sotiropoulos SN, Tavor I, Zhang HG, Zhou FL. The CONNECT project: Combining macroand micro-structure. Neuroimage, 2013; 80:273-82, doi: 10.1016/j.neuroimage.2013.05.055. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TE, Bucholz R, Chang A, Chen L, Corbetta M, Curtiss SW, Della Penna S, Feinberg D, Glasser MF, Harel N, Heath AC, Larson-Prior L, Marcus D, Michalareas G, Moeller S, Oostenveld R, Petersen SE, Prior F, Schlaggar BL, Smith SM, Snyder AZ, Xu J, Yacoub E, Consortium WU-MH. The Human Connectome Project: a data acquisition perspective. Neuroimage, 2012; 62(4):2222-31, doi: 10.1016/j.neuroimage.2012.02.018. Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Consortium WU-MH. The WU-Minn Human Connectome Project: an overview. Neuroimage, 2013; 80:62-79, doi: 10.1016/j.neuroimage.2013.05.041. Van Essen DC, Ugurbil K. The future of the human connectome. Neuroimage, 2012; 62(2):1299310, doi: 10.1016/j.neuroimage.2012.01.032. Jbabdi S, Sotiropoulos SN, Savio AM, Grana M, Behrens TE. Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems. Magn Reson Med, 2012; 68(6):1846-55, doi: 10.1002/mrm.24204. Setsompop K, Kimmlingen R, Eberlein E, Witzel T, Cohen-Adad J, McNab JA, Keil B, Tisdall MD, Hoecht P, Dietz P, Cauley SF, Tountcheva V, Matschl V, Lenz VH, Heberlein K, Potthast A, Thein H, Van Horn J, Toga A, Schmitt F, Lehne D, Rosen BR, Wedeen V, Wald LL. Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. Neuroimage, 2013; 80:220-33, doi: 10.1016/j.neuroimage.2013.05.078. Sotiropoulos SN, Jbabdi S, Andersson JL, Woolrich MW, Ugurbil K, Behrens TE. RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. IEEE Trans Med Imaging, 2013; 32(6):969-82, doi: 10.1109/TMI.2012.2231873. Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S, Auerbach EJ, Glasser MF, Hernandez M, Sapiro G, Jenkinson M, Feinberg DA, Yacoub E, Lenglet C, Van Essen DC, Ugurbil K, Behrens TE, Consortium WU-MH. Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage, 2013; 80:125-43, doi: 10.1016/j.neuroimage.2013.05.057. Sotiropoulos SN, Moeller S, Jbabdi S, Xu J, Andersson JL, Auerbach EJ, Yacoub E, Feinberg D, Setsompop K, Wald LL, Behrens TE, Ugurbil K, Lenglet C. Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE. Magn Reson Med, 2013; 70(6):1682-9, doi: 10.1002/mrm.24623. Ugurbil K, Xu J, Auerbach EJ, Moeller S, Vu AT, Duarte-Carvajalino JM, Lenglet C, Wu X, Schmitter S, Van de Moortele PF, Strupp J, Sapiro G, De Martino F, Wang D, Harel N, Garwood M, Chen L, Feinberg DA, Smith SM, Miller KL, Sotiropoulos SN, Jbabdi S, Andersson JL, Behrens TE, Glasser MF, Van Essen DC, Yacoub E, Consortium WU-MH. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. Neuroimage, 2013; 80:80-104, doi: 10.1016/j.neuroimage.2013.05.012. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging, 2001; 13(4):534-46, doi: 10.1002/jmri.1076. Ennis DB, Kindlmann G. Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn Reson Med, 2006; 55(1):136-46, doi: 10.1002/mrm.20741. Jones DK, Knosche TR, Turner R. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. Neuroimage, 2013; 73:239-54, doi: 10.1016/j.neuroimage.2012.06.081. Wheeler-Kingshott CA, Cercignani M. About "axial" and "radial" diffusivities. Magn Reson Med, 2009; 61(5):1255-60, doi: 10.1002/mrm.21965.

ACCEPTED MANUSCRIPT [298]

[299]

[307]

[308]

[309]

[310]

[311]

[312]

[313]

[314] [315] [316]

[317]

SC

MA NU

[306]

ED

[304] [305]

PT

[303]

CE

[302]

AC

[301]

RI P

T

[300]

Kindlmann G, Ennis DB, Whitaker RT, Westin CF. Diffusion tensor analysis with invariant gradients and rotation tangents. IEEE Trans Med Imaging, 2007; 26(11):1483-99, doi: 10.1109/TMI.2007.907277. Le Bihan D. The 'wet mind': water and functional neuroimaging. Phys Med Biol, 2007; 52(7):R5790, doi: 10.1088/0031-9155/52/7/R02. Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage, 2005; 27(1):48-58, doi: 10.1016/j.neuroimage.2005.03.042. Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn Reson Med, 2008; 59(6):1347-54, doi: 10.1002/mrm.21577. Zhang H, Hubbard PL, Parker GJ, Alexander DC. Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. Neuroimage, 2011; 56(3):1301-15, doi: 10.1016/j.neuroimage.2011.01.084. Assaf Y, Cohen Y. Assignment of the water slow-diffusing component in the central nervous system using q-space diffusion MRS: implications for fiber tract imaging. Magn Reson Med, 2000; 43(2):191-9. Tuch DS. Q-ball imaging. Magn Reson Med, 2004; 52(6):1358-72, doi: 10.1002/mrm.20279. Tuch DS, Reese TG, Wiegell MR, Wedeen VJ. Diffusion MRI of complex neural architecture. Neuron, 2003; 40(5):885-95, doi: 10.1016/S0896-6273(03)00758-X. Heidemann RM, Porter DA, Anwander A, Feiweier T, Heberlein K, Knosche TR, Turner R. Diffusion imaging in humans at 7T using readout-segmented EPI and GRAPPA. Magn Reson Med, 2010; 64(1):9-14, doi: 10.1002/mrm.22480. Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S, Gunther M, Glasser MF, Miller KL, Ugurbil K, Yacoub E. Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One, 2010; 5(12):e15710, doi: 10.1371/journal.pone.0015710. Menzel MI, Tan ET, Khare K, Sperl JI, King KF, Tao X, Hardy CJ, Marinelli L. Accelerated diffusion spectrum imaging in the human brain using compressed sensing. Magn Reson Med, 2011; 66(5):1226-33, doi: 10.1002/mrm.23064. Michailovich O, Rathi Y, Dolui S. Spatially regularized compressed sensing for high angular resolution diffusion imaging. IEEE Trans Med Imaging, 2011; 30(5):1100-15, doi: 10.1109/TMI.2011.2142189. Shemesh N, Barazany D, Sadan O, Bar L, Zur Y, Barhum Y, Sochen N, Offen D, Assaf Y, Cohen Y. Mapping apparent eccentricity and residual ensemble anisotropy in the gray matter using angular double-pulsed-field-gradient MRI. Magn Reson Med, 2012; 68(3):794-806, doi: 10.1002/mrm.23300. Shemesh N, Ozarslan E, Bar-Shir A, Basser PJ, Cohen Y. Observation of restricted diffusion in the presence of a free diffusion compartment: single- and double-PFG experiments. J Magn Reson, 2009; 200(2):214-25, doi: 10.1016/j.jmr.2009.07.005. Hansen MB, Jespersen SN, Leigland LA, Kroenke CD. Using diffusion anisotropy to characterize neuronal morphology in gray matter: the orientation distribution of axons and dendrites in the NeuroMorpho.org database. Front Integr Neurosci, 2013; 7:31, doi: 10.3389/fnint.2013.00031. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012; 61(4):1000-16, doi: 10.1016/j.neuroimage.2012.03.072. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009; 10(3):186-98, doi: 10.1038/nrn2575. Matthews PM, Honey GD, Bullmore ET. Applications of fMRI in translational medicine and clinical practice. Nat Rev Neurosci, 2006; 7(9):732-44, doi: 10.1038/nrn1929. van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol, 2010; 20(8):519-34, doi: 10.1016/j.euroneuro.2010.03.008. Mascalchi M, Ginestroni A, Toschi N, Poggesi A, Cecchi P, Salvadori E, Tessa C, Cosottini M, Stefano ND, Pracucci G, Pantoni L, Inzitari D, Diciotti S, investigators VT. The burden of microstructural damage modulates cortical activation in elderly subjects with MCI and leukoaraiosis. A DTI and fMRI study. Hum Brain Mapp, 2012, doi: 10.1002/hbm.22216.

ACCEPTED MANUSCRIPT [318]

[325] [326]

[327]

[328] [329]

[330] [331]

[332] [333] [334] [335]

[336] [337]

SC

MA NU

[324]

ED

[323]

PT

[322]

CE

[321]

AC

[320]

RI P

T

[319]

Tessa C, Lucetti C, Diciotti S, Baldacci F, Paoli L, Cecchi P, Giannelli M, Ginestroni A, Del Dotto P, Ceravolo R, Vignali C, Bonuccelli U, Mascalchi M. Decreased and increased cortical activation coexist in de novo Parkinson's disease. Exp Neurol, 2010; 224(1):299-306, doi: 10.1016/j.expneurol.2010.04.005. Ginestroni A, Diciotti S, Cecchi P, Pesaresi I, Tessa C, Giannelli M, Della Nave R, Salvatore E, Salvi F, Dotti MT, Piacentini S, Soricelli A, Cosottini M, De Stefano N, Mascalchi M. Neurodegeneration in Friedreich's ataxia is associated with a mixed activation pattern of the brain. A fMRI study. Hum Brain Mapp, 2012; 33(8):1780-91, doi: 10.1002/hbm.21319. Passamonti L, Salsone M, Toschi N, Cerasa A, Giannelli M, Chiriaco C, Cascini GL, Fera F, Quattrone A. Dopamine-transporter levels drive striatal responses to apomorphine in Parkinson's disease. Brain Behav, 2013; 3(3):249-62, doi: 10.1002/brb3.115. Jezzard PJ, Matthews PM, Smith SM. Functional MRI: an introduction to methods. 2002: Oxford university press. Lee MH, Smyser CD, Shimony JS. Resting-State fMRI: A Review of Methods and Clinical Applications. AJNR Am J Neuroradiol, 2013; 34(10):1866-1872, doi: 10.3174/ajnr.A3263. Ashburner J, Friston KJ. Morphometry, in Human Brain Function, F.K. Frackowiak RSJ, Frith C, Dolan R, Friston KJ, Price CJ, Zeki S, Ashburner J, Penny WD, Editor. 2003, Academic Press. 707724. Chung MK, Worsley KJ, Robbins S, Paus T, Taylor J, Giedd JN, Rapoport JL, Evans AC. Deformation-based surface morphometry applied to gray matter deformation. Neuroimage, 2003; 18(2):198-213, doi: Good CD, Ashburner J, Frackowiak RS. Computational neuroanatomy: new perspectives for neuroradiology. Rev Neurol (Paris), 2001; 157(8-9 Pt 1):797-806. Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR, Jr., Weiner MW, Thompson PM, Alzheimer's Disease Neuroimaging I. Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage, 2008; 43(3):458-69, doi: 10.1016/j.neuroimage.2008.07.013. Pereira JB, Ibarretxe-Bilbao N, Marti MJ, Compta Y, Junque C, Bargallo N, Tolosa E. Assessment of cortical degeneration in patients with Parkinson's disease by voxel-based morphometry, cortical folding, and cortical thickness. Hum Brain Mapp, 2012; 33(11):2521-34, doi: 10.1002/hbm.21378. Takao H, Abe O, Ohtomo K. Computational analysis of cerebral cortex. Neuroradiology, 2010; 52(8):691-8, doi: 10.1007/s00234-010-0715-4. Della Nave R, Ginestroni A, Tessa C, Cosottini M, Giannelli M, Salvatore E, Sartucci F, De Michele G, Dotti MT, Piacentini S, Mascalchi M. Brain structural damage in spinocerebellar ataxia type 2. A voxel-based morphometry study. Mov Disord, 2008; 23(6):899-903, doi: 10.1002/mds.21982. Constantinescu R, Mondello S. Cerebrospinal fluid biomarker candidates for parkinsonian disorders. Front Neurol, 2012; 3:187, doi: 10.3389/fneur.2012.00187. Kroksveen AC, Opsahl JA, Aye TT, Ulvik RJ, Berven FS. Proteomics of human cerebrospinal fluid: discovery and verification of biomarker candidates in neurodegenerative diseases using quantitative proteomics. J Proteomics, 2011; 74(4):371-88, doi: 10.1016/j.jprot.2010.11.010. Zhang AH, Sun H, Wang XJ. Recent advances in metabolomics in neurological disease, and future perspectives. Anal Bioanal Chem, 2013; 405(25):8143-50, doi: 10.1007/s00216-013-7061-4. Zhang J, Goodlett DR, Montine TJ. Proteomic biomarker discovery in cerebrospinal fluid for neurodegenerative diseases. J Alzheimers Dis, 2005; 8(4):377-86. Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. 2005: Elsevier Science. Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev, 2012; 36(4):1140-52, doi: 10.1016/j.neubiorev.2012.01.004. Mwangi B, Tian TS, Soares JC. A Review of Feature Reduction Techniques in Neuroimaging. Neuroinformatics, 2013, doi: 10.1007/s12021-013-9204-3. Diciotti S, Ginestroni A, Bessi V, Giannelli M, Tessa C, Bracco L, Mascalchi M, Toschi N. Identification of mild Alzheimer's disease through automated classification of structural MRI features. Conf Proc IEEE Eng Med Biol Soc, 2012; 2012:428-31, doi: 10.1109/EMBC.2012.6345959.

ACCEPTED MANUSCRIPT

[344]

[345]

[346]

[347]

[348]

[349]

[350]

[351]

[352]

[353]

T

RI P

SC

[343]

MA NU

[342]

ED

[341]

PT

[340]

CE

[339]

Cosottini M, Giannelli M, Vannozzi F, Pesaresi I, Piazza S, Belmonte G, Siciliano G. Evaluation of corticospinal tract impairment in the brain of patients with amyotrophic lateral sclerosis by using diffusion tensor imaging acquisition schemes with different numbers of diffusion-weighting directions. J Comput Assist Tomogr, 2010; 34(5):746-50, doi: 10.1097/RCT.0b013e3181e35129. Giannelli M, Cosottini M, Michelassi MC, Lazzarotti G, Belmonte G, Bartolozzi C, Lazzeri M. Dependence of brain DTI maps of fractional anisotropy and mean diffusivity on the number of diffusion weighting directions. J Appl Clin Med Phys, 2010; 11(1):2927. Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed, 2010; 23(7):803-20, doi: 10.1002/nbm.1543. Koay CG, Carew JD, Alexander AL, Basser PJ, Meyerand ME. Investigation of anomalous estimates of tensor-derived quantities in diffusion tensor imaging. Magn Reson Med, 2006; 55(4):930-6, doi: 10.1002/mrm.20832. Koay CG, Chang LC, Carew JD, Pierpaoli C, Basser PJ. A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. J Magn Reson, 2006; 182(1):115-25, doi: 10.1016/j.jmr.2006.06.020. Alexander AL, Lee JE, Wu YC, Field AS. Comparison of diffusion tensor imaging measurements at 3.0 T versus 1.5 T with and without parallel imaging. Neuroimaging Clin N Am, 2006; 16(2):299309, doi: 10.1016/j.nic.2006.02.006. Padhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T, Dzik-Jurasz A, Ross BD, Van Cauteren M, Collins D, Hammoud DA, Rustin GJ, Taouli B, Choyke PL. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia, 2009; 11(2):102-25. Dalaker TO, Larsen JP, Bergsland N, Beyer MK, Alves G, Dwyer MG, Tysnes OB, Benedict RH, Kelemen A, Bronnick K, Zivadinov R. Brain atrophy and white matter hyperintensities in early Parkinson's disease(a). Mov Disord, 2009; 24(15):2233-41, doi: 10.1002/mds.22754. Nopoulos PC, Aylward EH, Ross CA, Johnson HJ, Magnotta VA, Juhl AR, Pierson RK, Mills J, Langbehn DR, Paulsen JS, Group P-HICoHS. Cerebral cortex structure in prodromal Huntington disease. Neurobiol Dis, 2010; 40(3):544-54, doi: 10.1016/j.nbd.2010.07.014. Soneson C, Fontes M, Zhou Y, Denisov V, Paulsen JS, Kirik D, Petersen A, Huntington Study Group P-HDi. Early changes in the hypothalamic region in prodromal Huntington disease revealed by MRI analysis. Neurobiol Dis, 2010; 40(3):531-43, doi: 10.1016/j.nbd.2010.07.013. Spulber G, Simmons A, Muehlboeck JS, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Spenger C, Lovestone S, Wahlund LO, Westman E, dNeuroMed c, for the Alzheimer Disease Neuroimaging I. An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment. J Intern Med, 2013; 273(4):396-409, doi: 10.1111/joim.12028. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Liu E, Morris JC, Petersen RC, Saykin AJ, Schmidt ME, Shaw L, Siuciak JA, Soares H, Toga AW, Trojanowski JQ, Alzheimer's Disease Neuroimaging I. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement, 2012; 8(1 Suppl):S1-68, doi: 10.1016/j.jalz.2011.09.172. Sasaki M, Yamada K, Watanabe Y, Matsui M, Ida M, Fujiwara S, Shibata E, Investigators AJ. Variability in Absolute Apparent Diffusion Coefficient Values across Different Platforms May Be Substantial: A Multivendor, Multi-institutional Comparison Study. Radiology, 2008; 249(2):624630, doi: DOI 10.1148/radiol.2492071681. Pagani E, Hirsch JG, Pouwels PJ, Horsfield MA, Perego E, Gass A, Roosendaal SD, Barkhof F, Agosta F, Rovaris M, Caputo D, Giorgio A, Palace J, Marino S, De Stefano N, Ropele S, Fazekas F, Filippi M. Intercenter differences in diffusion tensor MRI acquisition. J Magn Reson Imaging, 2010; 31(6):1458-68, doi: 10.1002/jmri.22186. Vollmar C, O'Muircheartaigh J, Barker GJ, Symms MR, Thompson P, Kumari V, Duncan JS, Richardson MP, Koepp MJ. Identical, but not the same: Intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0 T scanners. NeuroImage, 2010; 51(4):1384-1394, doi: 10.1016/j.neuroimage.2010.03.046. Teipel SJ, Reuter S, Stieltjes B, Acosta-Cabronero J, Ernemann U, Fellgiebel A, Filippi M, Frisoni G, Hentschel F, Jessen F, Kloppel S, Meindl T, Pouwels PJ, Hauenstein KH, Hampel H. Multicenter

AC

[338]

ACCEPTED MANUSCRIPT

[359]

[360]

[361]

[362] [363] [364]

[365]

[366]

[367] [368]

[369]

[370]

T

RI P

SC

MA NU

[358]

ED

[357]

PT

[356]

CE

[355]

AC

[354]

stability of diffusion tensor imaging measures: a European clinical and physical phantom study. Psychiatry Res, 2011; 194(3):363-71, doi: 10.1016/j.pscychresns.2011.05.012. Zhu T, Hu R, Qiu X, Taylor M, Tso Y, Yiannoutsos C, Navia B, Mori S, Ekholm S, Schifitto G, Zhong J. Quantification of accuracy and precision of multi-center DTI measurements: a diffusion phantom and human brain study. Neuroimage, 2011; 56(3):1398-411, doi: 10.1016/j.neuroimage.2011.02.010. Fox RJ, Sakaie K, Lee JC, Debbins JP, Liu Y, Arnold DL, Melhem ER, Smith CH, Philips MD, Lowe M, Fisher E. A validation study of multicenter diffusion tensor imaging: reliability of fractional anisotropy and diffusivity values. AJNR Am J Neuroradiol, 2012; 33(4):695-700, doi: 10.3174/ajnr.A2844. Magnotta VA, Matsui JT, Liu D, Johnson HJ, Long JD, Bolster BD, Jr., Mueller BA, Lim K, Mori S, Helmer KG, Turner JA, Reading S, Lowe MJ, Aylward E, Flashman LA, Bonett G, Paulsen JS. Multicenter reliability of diffusion tensor imaging. Brain Connect, 2012; 2(6):345-55, doi: 10.1089/brain.2012.0112. Huang L, Wang X, Baliki MN, Wang L, Apkarian AV, Parrish TB. Reproducibility of structural, resting-state BOLD and DTI data between identical scanners. PLoS One, 2012; 7(10):e47684, doi: 10.1371/journal.pone.0047684. Farrell JA, Landman BA, Jones CK, Smith SA, Prince JL, van Zijl PC, Mori S. Effects of signal-tonoise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T. J Magn Reson Imaging, 2007; 26(3):756-67, doi: 10.1002/jmri.21053. Giannelli M, Belmonte G, Toschi N, Pesaresi I, Ghedin P, Traino AC, Bartolozzi C, Cosottini M. Technical Note: DTI measurements of fractional anisotropy and mean diffusivity at 1.5 T: Comparison of two radiofrequency head coils with different functional designs and sensitivities. Medical Physics, 2011; 38(6):3205-3211, doi: Doi 10.1118/1.3592013. Landman BA, Farrell JA, Huang H, Prince JL, Mori S. Diffusion tensor imaging at low SNR: nonmonotonic behaviors of tensor contrasts. Magn Reson Imaging, 2008; 26(6):790-800, doi: 10.1016/j.mri.2008.01.034. Jones DK, Basser PJ. "Squashing peanuts and smashing pumpkins": How noise distorts diffusionweighted MR data. Magnetic Resonance in Medicine, 2004; 52(5):979-993, doi: Doi 10.1002/Mrm.20283. Jones DK. Precision and accuracy in diffusion tensor magnetic resonance imaging. Top Magn Reson Imaging, 2010; 21(2):87-99, doi: 10.1097/RMR.0b013e31821e56ac. Schmithorst VJ, Dardzinski BJ. Automatic gradient preemphasis adjustment: a 15-minute journey to improved diffusion-weighted echo-planar imaging. Magn Reson Med, 2002; 47(1):208-12, doi: Bammer R, Markl M, Barnett A, Acar B, Alley MT, Pelc NJ, Glover GH, Moseley ME. Analysis and generalized correction of the effect of spatial gradient field distortions in diffusion-weighted imaging. Magn Reson Med, 2003; 50(3):560-9, doi: 10.1002/mrm.10545. Delakis I, Moore EM, Leach MO, De Wilde JP. Developing a quality control protocol for diffusion imaging on a clinical MRI system. Phys Med Biol, 2004; 49(8):1409-22, doi: 10.1088/00319155/49/8/003. Nagy Z, Weiskopf N, Alexander DC, Deichmann R. A method for improving the performance of gradient systems for diffusion-weighted MRI. Magn Reson Med, 2007; 58(4):763-8, doi: 10.1002/mrm.21379. Wu YC, Alexander AL. A method for calibrating diffusion gradients in diffusion tensor imaging. J Comput Assist Tomogr, 2007; 31(6):984-93, doi: 10.1097/rct.0b013e31805152fa. Chenevert TL, Galban CJ, Ivancevic MK, Rohrer SE, Londy FJ, Kwee TC, Meyer CR, Johnson TD, Rehemtulla A, Ross BD. Diffusion Coefficient Measurement Using a Temperature-Controlled Fluid for Quality Control in Multicenter Studies. Journal of Magnetic Resonance Imaging, 2011; 34(4):983-987, doi: Doi 10.1002/Jmri.22363. Wang ZYJ, Seo Y, Chia JM, Rollins NK. A quality assurance protocol for diffusion tensor imaging using the head phantom from American College of Radiology. Medical Physics, 2011; 38(7):44154421, doi: Doi 10.1118/1.3595111. Mohammadi S, Nagy Z, Moller HE, Symms MR, Carmichael DW, Josephs O, Weiskopf N. The effect of local perturbation fields on human DTI: characterisation, measurement and correction. Neuroimage, 2012; 60(1):562-70, doi: 10.1016/j.neuroimage.2011.12.009.

ACCEPTED MANUSCRIPT [371] [372]

SC

MA NU ED PT CE

[375]

AC

[374]

RI P

T

[373]

De Santis S, Evans CJ, Jones DK. RAPID: A routine assurance pipeline for imaging of diffusion. Magn Reson Med, 2012, doi: 10.1002/mrm.24465. Walker L, Curry M, Nayak A, Lange N, Pierpaoli C. A framework for the analysis of phantom data in multicenter diffusion tensor imaging studies. Hum Brain Mapp, 2013; 34(10):2439-54, doi: 10.1002/hbm.22081. Lauzon CB, Asman AJ, Esparza ML, Burns SS, Fan Q, Gao Y, Anderson AW, Davis N, Cutting LE, Landman BA. Simultaneous analysis and quality assurance for diffusion tensor imaging. PLoS One, 2013; 8(4):e61737, doi: 10.1371/journal.pone.0061737. Malyarenko D, Galban CJ, Londy FJ, Meyer CR, Johnson TD, Rehemtulla A, Ross BD, Chenevert TL. Multi-system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice-water phantom. J Magn Reson Imaging, 2013; 37(5):1238-46, doi: 10.1002/jmri.23825. Giannelli M, Sghedoni R, Iacconi C, Iori M, Traino AC, Guerrisi M, Mascalchi M, Toschi N, Diciotti S. MR scanner systems should be adequately characterized in diffusion-MRI of the breast. PLoS One, 2014; 9(1):e86280, doi: 10.1371/journal.pone.0086280.

ACCEPTED MANUSCRIPT Figure legends:

Fig. 1. WM regions showing (through whole brain tract-based spatial statistics analysis) overlapping

RI P

T

significant changes in distinct DTI-derived indices (FA, MD, DR, DA) for subjects with Alzheimer’s disease (AD, panel A and B) as well as amnestic mild cognitive impairment (a-MCI, panel C) as compared to

SC

healthy subjects. (Adapted and reproduced with permission from Bosch et al. [45]).

MA NU

Fig. 2. DTI-derived indices (FA, MD, DR, DA) changes of a group of early-stage Alzheimer’s (AD) disease patients which was followed-up for a period of 12 months. Whole-brain tract-based spatial statistics analysis showed regions with significantly increased DR as well as reduced FA. No significant changes in MD or DA were found. (Adapted and reproduced with permission from Acosta-Cabronero et al. [49]).

ED

Fig. 3. Boxplots showing the distribution of DTI-derived indices (FA, MD, DR, DA) in patients with

PT

amnestic mild cognitive impairment (aMCI), patients with Alzheimer’s (AD) disease, and healthy controls (HC). The boxes represent the interquartile ranges, which contain 50% of individual subjects’ values. The

CE

whiskers are lines that extend from the box to the highest and lowest values, excluding outliers. A line across the box indicates the median value. ILF, SLF and IFO are the inferior longitudinal fasciculus, superior

AC

longitudinal fasciculus and inferior frontooccipital fasciculus, respectively. *P < 0.05 on General Linear Model test. (Adapted and reproduced with permission from Pievani et al. [89]).

Fig. 4. Whole brain voxel-based analysis of DTI-derived indices of FA and MD: t-map showing significantly increased MD in bilateral orbitofrontal cortices (A) and bilateral inferior temporal gyri (B), decreased MD in bilateral parietal lobes and left precentral gyrus (C), and decreased FA in bilateral cerebella and right rectus gyrus (D) in a group of PD patients versus a healthy subject group. The red color stands for an increase while blue-green stands for a decrease. (Adapted and reproduced with permission from Zhang et al. [141]).

Fig. 5. Tract-based spatial statistic analysis showing the WM areas where FA values correlate with MiniMental State examination (MMSE) scores in patients with PD. (A) The skeleton is shown in green. The WM

ACCEPTED MANUSCRIPT areas where FA values are significantly correlated with MMSE scores in patients with PD are shown in color (red to yellow). (B) Rendered version of (A). (Adapted and reproduced with permission from Hattori et al.

RI P

T

[146]).

Fig. 6. Locations in which fractional anisotropy is significantly reduced in patients with PLS (A), bulbaronset ALS (B), limb-onset ALS (C) or PMA (D) when compared with healthy subjects are shown in red.

SC

Specific anatomical locations/WM structures are indicated by coloured arrows. CST, CC and IC are the

permission from van der Graaff et al. [202]).

MA NU

corticospinal tract, corpus callosum and internal capsule, respectively. (Adapted and reproduced with

Fig. 7. Whole brain tract-based spatial statistics analysis of clusters of voxels with significantly lower FA (red) in (A) patients with ALS compared with patients with PLS, and significantly greater MD (blue) in (B)

ED

patients with PLS compared with patients with ALS in selected axial, coronal and sagittal sections. MNI

PT

coordinates are shown and images are displayed in radiological convention (right brain on left side).

CE

(Adapted and reproduced with permission from Iwata et al. [212]).

Fig. 8. Microstructural and macrostructural changes in pre-HD subjects and HD patients. The left panels

AC

display the results of the comparison between pre-HD versus controls. The central panels display the results of the comparison between pre-HD versus HD. The right panels display the results of the comparison between HD versus healthy subjects. The top panels show the results of the callosal thickness region of interest (ROI) analysis. The middle panels show the results of the voxel-based morphometry (VBM) analysis. The bottom panels show the results of the tract-based spatial statistics (TBSS) analysis of DTI derived indices (FA, DA, DR). Compared with controls, pre-HD patients show a reduced callosal thickness in the isthmus (A); a reduced WM density in the isthmus and splenium (D); a reduced FA in the isthmus (G); no changes in DA (P); increased DR in the isthmus (M). Compared with pre-HD, HD patients show a reduced thickness mainly in the body (B); a reduced WM density in the splenium, isthmus, and rostrally in the rostrum (E); reduced FA and DR across the entire corpus callosum (H and N) as well as an increased DA in the body (K). Compared with controls, HD patients show a reduced thickness almost across the entire

ACCEPTED MANUSCRIPT corpus callosum (C); a reduced WM density in the splenium, isthmus, and body (F); reduced FA and DR in all the corpus callosum (I and O) as well as an increased DA in the isthmus and body (L). (Adapted and

RI P

T

reproduced with permission from Di Paola et al. [236]).

Fig. 9. TBSS analysis of FA (A), DA (B), DR (C) and MD (D) maps in SCA2 patients versus healthy subjects at the level of the middle cerebellar peduncles. Areas of significantly decreased FA (A) are shown in

SC

red and include the left corticospinal tract, the transverse pontine fibers, the medial and lateral lemnisci and

MA NU

the spinothalamic tracts, the middle cerebellar peduncels and short intracerebellar fibers. Areas of significantly increased DA (pink) include the medial and lateral lemnisci and the spinothalamic tracts, while decreased DA (sky-blue) is observed in the middle cerebellar peduncles and short intracerebellar fibers in the right cerebellar hemisphere (B). Areas of significantly increased DR (C) are shown in green and include the left corticospinal tracts, the transverse pontine fibers, the medial and lateral lemnisci and the spinothalamic

ED

tracts, the middle cerebellar peduncels and short intracerebellar fibers. Areas of significantly increased MD

PT

are shown in yellow (D) and include the transverse pontine fibers, the middle cerebellar peduncles and short

CE

intracerebellar fibers. (Adapted and reproduced with permission from Della Nave et al. [260]).

Table 1. MR imaging-aetiological correlation in degenerative ataxias.

AC

Spinal Atrophy FRDA AVED

Cortical Cerebellar Atrophy AT AOA1-2 EA-2 EOCA Gluten Ataxia ILOCA SCA4 SCA5 SCA6 SCA8 SCA10 SCA12 SCA14 SCA15 SCA16 SCA17 SCA18 SCA19 SCA21 SCA22 SCA25

Ponto Cerebellar Atrophy DRPLA EOCA MSA-C SCA1 SCA2 SCA3 SCA7 SCA13

ACCEPTED MANUSCRIPT AVED = Ataxia with Vitamin E Deficiency; AT = Ataxia Telangectasia; DRPLA = Dentato-Rubro-PaliidoLuysian Atrophy; EA-2 = Episodic Ataxia type 2; EOCA = Early Onset Cerebellar Ataxia with retained

T

tendon reflexes; FRDA = Friedreich Ataxia; ILOCA = Idiopathic Late Onset Cerebellar Ataxia (pure); MSA-

AC

CE

PT

ED

MA NU

SC

RI P

C= Multiple Systema Atrophy- Cerebellar type; SCA = SpinoCerebellar Ataxia.

PT

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

AC

CE

Figure 1

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

AC

CE

PT

Figure 2

Figure 3

AC

CE

PT

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

PT

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

AC

CE

Figure 4

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

AC

CE

PT

Figure 5

AC

CE

PT

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

Figure 6

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

AC

CE

PT

Figure 7

Figure 8

AC

CE

PT

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

RI P

T

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

MA NU

SC

Figure 9