Functional MRI in Parkinson's Disease Cognitive Impairment

CHAPTER TWO

Functional MRI in Parkinson’s Disease Cognitive Impairment
1 Hugo C. Baggio, Carme Junque University of Barcelona, Barcelona, Spain 1 Corresponding author e-mail address: [email protected]

Contents 1. General Aspects 1.1 Analytic Approaches—Overview 1.2 Functional Connectivity 1.3 Resting-State Functional Connectivity—Head Motion Artifacts 2. Task Related FMRI Studies on Specific Cognitive Functions 2.1 Attention/Working Memory 2.2 Executive Functions 2.3 Perception 2.4 Memory 3. Resting-State FMRI Studies 3.1 Large-Scale Intrinsic Connectivity Networks 3.2 Functional Connectomics and Graph Theory 3.3 State-of-the-Art Functional Connectivity Analyses 4. Conclusions References

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Abstract Functional magnetic resonance imaging (fMRI) has been used to study the neural bases of cognitive deficits in Parkinson’s disease for several years. Traditionally, task-based fMRI has been applied to study specific cognitive functions, providing information on disease-related alterations and regarding the physiological bases of normal cognition, the dopaminergic system, and the frontostriatal circuits. More recently, functional connectivity techniques using resting-state fMRI data have been developed. Unconstrained by specific cognitive tasks, these techniques allow assessing whole-brain patterns of connectivity believed to be useful proxies for the underlying functional architecture of the brain. These methods have shown that different types of Parkinson’s diseaserelated cognitive deficits are associated with patterns of altered connectivity within and between resting-state intrinsic connectivity networks. Although methodological standardization and the vulnerability of fMRI techniques to artifacts mandate further technical refinement, early studies provide encouraging results regarding the potential of fMRI-derived parameters for the ultimate goal of individual-subject classification.

International Review of Neurobiology, Volume 144 ISSN 0074-7742 https://doi.org/10.1016/bs.irn.2018.09.010

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2019 Elsevier Inc. All rights reserved.

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1. GENERAL ASPECTS 1.1 Analytic Approaches—Overview Traditional task-based functional MRI (fMRI) techniques consist of assessing regional blood-oxygen-level-dependent (BOLD) signal intensity during the performance of a given behavioral task compared with the signal intensity during a control condition or “rest.” Simply put, this comparison results in maps of relative activation or deactivation of brain regions, according to whether signal intensity increases or decreases in a statistically significant way. Over the last two decades, task-based activation studies have provided much of the current knowledge regarding the alterations in brain function underlying pathological cognitive decline in Parkinson’s disease (PD) and other disorders, as well as on the physiological bases of cognition. In the specific case of PD, task-based fMRI studies have provided a wealth of data that help understand the physiologic role of the dopaminergic system and of the frontostriatal circuits on different cognitive functions, as well as how changes in these systems explain some of the typical cognitive deficits seen in PD patients. This analytic approach often relies on a localizationist or modular paradigm, which assumes that specific brain functions (and their alterations) can be mapped onto individual specialized processing regions (Bressler & Menon, 2010; Fuster, 2006). In recent times, the study of cognition is increasingly focusing on the network paradigm. This paradigm posits that cognitive functions arise from the conjoint working of distributed and interconnected brain systems, organized into large-scale networks (Bressler & Menon, 2010). The network perspective of cognition, applied to fMRI studies of neurodegenerative diseases, is showing promising results that might prove to have clinical usefulness for differential diagnosis, monitoring of disease progression, or as markers of cognitive prognosis.

1.2 Functional Connectivity The simplest approach to study the interactivity of different brain regions through fMRI is by assessing their functional connectivity, i.e., the temporal dependency between their corresponding BOLD signal variation. This approach considers that, if the signal variation in a given pair of regions is temporally coherent, these regions are part of a functional circuit (Murphy, Birn, & Bandettini, 2013)—this does not, however, imply the existence of direct structural connections between them. If the whole brain is segmented

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into different regions, their respective signal time course is extracted, and the corresponding pairwise correlations are computed, the whole set of functional connections in the brain will be described; this characterizes the functional connectome (Biswal et al., 2010) (see Fig. 1). Unlike activation studies, functional connectivity assessment using fMRI is often done in the absence of any explicit cognitive task, i.e., in the resting state. In resting-state fMRI, functional images of the brain are acquired over the course of several minutes while the subject is generally only instructed not to think of anything in particular, not to move, and not to fall asleep. During “rest,” patterns of low-frequency BOLD signal oscillation are

Fig. 1 Seed-based connectivity—seed-to-seed approach. In the simplest form of seedto-seed functional connectivity, the correlation or regression coefficient between the mean BOLD signal time courses of two regions of interest (here, A and B) is calculated. The corresponding correlation or regression coefficient is taken as the strength of connectivity between the two regions. In the connectomics approach the whole-brain gray matter is divided, or parcellated, into several regions of interest. The correlation or regression coefficients for the time series of all pairs of regions are then computed. The resulting functional connectome therefore describes all possible functional connections for the parcellation scheme chosen.

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observed across the brain. The decomposition of this spontaneous signal reveals distributed areas with correlated or anti-correlated fluctuations. These patterns, characterizing a network of brain regions with coherent patterns of signal variation, are called resting-state intrinsic connectivity networks. Intrinsic connectivity networks are highly consistent and replicable within and across subjects. Comparisons between intrinsic connectivity networks and patterns of activation obtained through task-based fMRI show a high spatial correspondence between intrinsic connectivity networks and brain networks activated in different behavioral tasks. These observations give support to the idea that spontaneous BOLD signal fluctuations reflect the “baseline” functional brain architecture, not constrained by the choice of a specific cognitive task. Resting-state fMRI can therefore be used to assess the intrinsic functional organization of the brain and, due to the ease of acquisition, can be employed even in cognitively-impaired patients who might otherwise have difficulty complying with fMRI tasks. It is not surprising, therefore, that the use of resting-state fMRI as a tool for studying disorders such as PD has been growing steadily. In resting-state fMRI, as opposed to task-related fMRI, there is no explicit task on which to model the expected brain response. Comparisons of different conditions to identify activations thus cannot be made; instead, the most frequently used analytic techniques are based on the coherence of signal variation across the brain, i.e., on functional connectivity, as mentioned above. This is done, in its simplest form (called seed-based connectivity analysis), by extracting the time course of a “seed” region of interest and correlating it with the time courses of other brain regions. This region of interest can consist of a single voxel (the basic unit of a three-dimensional digital image, or the three-dimensional analog of a pixel) or of a discrete region made up of several voxels (in which case the average signal variation across all voxels included is usually used as the time series of that region of interest). In the seed-to-seed approach, the signal in a seed region is correlated with the signal in other seed regions (see Fig. 1). If, on the other hand, these seed time courses are correlated with those of all brain voxels, a connectivity brain map is produced where each point in the image indicates the strength of functional correlation with the seed (the seed-to-voxel approach, see Fig. 2). Seed-based connectivity approaches rely on the choice of a few regions of interest a priori, based on a certain hypothesis. Besides these model-driven techniques, data-driven methods are also widely used to identify the underlying structure of the data (Mckeown, Hansen, & Sejnowski, 2003) without needing to specify a model.

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Fig. 2 Seed-based connectivity—seed-to-voxel approach. Similar to the seed-to-seed approach, the BOLD signal time series of a region of interest is extracted. Instead of calculating its correlation with other pre-specified regions of interest, a correlation or regression is performed between the seed region (green voxels) time series and the time series of all voxels in the brain (top panel). A new image is then produced, called a correlation map, where the value of each brain voxel is given by the corresponding correlation/regression coefficient, which indicates how strongly it is functionally connected to the seed.

The most frequently used data-driven technique to identify coherent spatial patterns of BOLD signal fluctuation is independent component analysis. Independent component analysis blindly separates the underlying signal into independent components, represented by three-dimensional spatial maps and a corresponding temporal course (Griffanti et al., 2017). These components can have neural origins, or they may be related to any source of coherent signal variation in the image, such as head motion or physiologic noise (e.g., respiratory cycles or vascular pulsation). Neural components (i.e., intrinsic connectivity networks) mainly involve gray matter regions, with spatial patterns consistent with the brain regions activated, e.g., during

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Fig. 3 Resting-state intrinsic connectivity networks. Using techniques such as independent component analysis to blindly separate the sources of signal variation in a restingstate fMRI acquisition, several consistent large-scale brain networks can be extracted. Specific networks correspond spatially to the patterns of activation in specific taskbased fMRI paradigms. The figure shows some intrinsic connectivity networks that are relevant for cognitive processes: the so-called task-positive networks—dorsal attention network (DAN), left and right frontoparietal networks (FPN), and salience network— and the task-negative network, the default-mode network (DMN). The sensorimotor (SM) and visual networks are also shown.

visual, auditory, sensorimotor, or higher-cognitive tasks (Fig. 3). One intrinsic connectivity networks that is consistently identified in resting-state imaging studies is the default-mode network (DMN), which symmetrically involves the medial prefrontal cortex, the precuneus and posterior cingulate gyrus, the inferior parietal lobe, among other areas (Raichle, 2015). The DMN has the particularity of showing reduced activation during most tasks involving externally-directed cues when compared with the resting state. For this reason, the DMN has been linked to self-referential or internally-directed cognitive states (Raichle, 2015). Initial observations that the DMN spatially overlaps with regions of hypometabolism in Alzheimer’s disease, followed by the finding that Alzheimer’s patients show altered patterns of activation and deactivation of the DMN, led this network to be seen as a potential

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imaging biomarker in neurodegenerative diseases. The DMN has since become the most studied intrinsic connectivity networks in different disorders, including PD, as described below.

1.3 Resting-State Functional Connectivity—Head Motion Artifacts While resting-state fMRI data are very easy to acquire, processing it for analysis is not trivial. As mentioned above, as opposed to traditional task-related fMRI approaches, resting-state fMRI analyses do not rely on a model of expected BOLD signal variation. Separating neural signal from non-neural (i.e., noise) sources of signal intensity variation therefore becomes difficult— a significant issue considering that the fMRI sequences available are highly susceptible to noise. Among different sources of noise, head motion is the most problematic. In studies of patients with movement disorders, this issue merits special attention. The effect of movement artifacts on fMRI images has long been recognized, but recent studies reveal that even small amounts of head motion can significantly affect resting-state fMRI connectivity analyses (Van Dijk, Sabuncu, & Buckner, 2012; Yan et al., 2013). Head motion causes changes in signal intensity that contaminate the signal of interest and leads to erroneous functional connectivity estimation. Specifically, the effect of head motion on functional connectivity measures is dependent on the anatomical distance of the regions assessed: greater head motion decreases the estimated functional connectivity between anatomically distant regions (such as in distant nodes of large-scale intrinsic connectivity networks), and increases the connectivity calculated between nearby structures (Satterthwaite et al., 2012; Van Dijk et al., 2012). Several image preprocessing strategies have been developed to overcome the deleterious effects of head motion on resting-state fMRI data. Nonetheless, there is no consensus regarding the optimal scheme, and no available strategy is completely effective in removing the effects of motion from resting-state fMRI data sets (Parkes, Fulcher, Y€ ucel, & Fornito, 2018; Power, Barnes, Snyder, Schlaggar, & Petersen, 2012). Besides, complicating the comparison of findings across studies, intergroup functional connectivity differences vary depending on the preprocessing method used (Parkes et al., 2018). This is an important limitation for the study of PD and PD-related cognitive deficits with resting-state fMRI, as PD patients tend to show greater head motion during scanning than controls (as do cognitively-impaired PD patients compared with cognitively-preserved ones), and preprocessing methods vary greatly among published studies.

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It is not possible to ascertain the degree to which head motion may have biased the results of a given study. Nonetheless, the interpretation of resting-state functional connectivity papers addressing PD samples should include a critical assessment of the preprocessing pipeline used. Such pipelines should include more than simply the regression of head motion parameters and mean white matter/cerebrospinal fluid time courses, preferably using ICA-based or component-based image denoising. Ideally, head motion parameters should be reported for each study group, and if significant differences are found they should be used as covariates in group-level analyses (Yan et al., 2013).

2. TASK RELATED FMRI STUDIES ON SPECIFIC COGNITIVE FUNCTIONS 2.1 Attention/Working Memory In PD, several gray and white matter abnormalities that can explain attention dysfunction have been described in structural studies using volumetric, whole-brain voxel-based morphometry, as well as cortical thickness techniques. Among these structural abnormalities, we can mention those seen in the superior parietal, dorsolateral frontal, and anterior cingulate cortices, which are relevant nodes in attentional brain networks such as the dorsal attention and frontoparietal networks. In addition, the dopaminergic and noradrenergic systems are capital for arousal and to promote activation of the cortical systems involved in the potentiation of perceptions, as well as for preparing the body for the ensuing responses. In summary, the simple knowledge of the structural brain changes associated with PD allows predicting that PD patients will suffer from attention deficits since before the diagnosis, and that these deficits will worsen following the progressive cortical degeneration involving attentional systems. FMRI studies have in part supported these predictions. Some studies have combined task-based and resting-state fMRI analyses on PD and attention. Posner proposed the existence of three attentional systems: alerting (achieving and maintaining an alert state), orienting (focusing on a source of sensory input), and executive control (resolving conflict), which show a good correspondence with brain networks evidenced by fMRI using a specific test called Attention Network Test (Fan, McCandliss, Fossella, Flombaum, & Posner, 2005; Posner, 2012). During performance of the Attention Network Test, to ignore irrelevant stimuli, PD patients were found to engage four regions of intrinsic connectivity networks associated with

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attention processes (belonging to the dorsal attention and frontoparietal networks) to a significantly greater extent than controls (Boord, Madhyastha, Askren, & Grabowski, 2017). The interpretation of this finding was that PD patients required more neural resources to perform the task than controls, probably due to underlying metabolic alterations in parietal and frontal regions described in PET studies. In another recent study, a group of non-demented PD patients was assessed with a working memory paradigm and resting-state fMRI in both the “off” state and the “on” state (Simioni, Dagher, & Fellows, 2017). Compared with controls, patients off levodopa were seen to have reduced task-related activation in the left prefrontal and bilateral parietal cortices, as well as worse working memory performance. In the “on” state, working memory performance improved, and an increased activation of the left ventrolateral prefrontal cortex was observed compared with the “off” state. Seed-based analyses in the resting state using the caudate nucleus as a seed revealed that caudate-parietal connectivity was lower in the “off” state compared with the “on” state. Furthermore, this connectivity was associated with working memory performance, suggesting a compensatory striatocortical effect strengthened by levodopa. The Stroop word-color interference test is recognized as a good tool to assess the complex attentional functions mainly involving the capacity of inhibiting automatic responses. Inhibitory capabilities are linked to the integrity of the anterior cingulate in addition to premotor and prefrontal cortical regions and basal ganglia (Peterson et al., 1999). The Stroop effect manifests itself as a slowing of response and increased response time in the incongruent condition, i.e., when the name of a color (blue, green, red, or yellow) is printed in a different color (e.g., the word “red” printed in blue ink instead of red ink) and the subject is asked to name the ink color. Performance in the Stroop test is often impaired in PD patients, but not always due to altered interference (the effect of color-text incongruence) but rather for slowness in all subtests. Fera et al., following previous evidence that the administration of levodopa is associated with better performance on specific executive tasks, evaluated the brain response during a simple attention task and during neutral and incongruent manual responses of the Stroop Task, both with and without the effect of levodopa (Fera et al., 2007). Their design had the novelty of a long pharmacological washout. They found that PD patients differed from controls in all three attention tasks in accuracy, and patients in the “on” condition performed better than in the “off” condition. This finding confirms the presence of attention

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deficits in PD and, more interestingly, a positive effect of levodopa treatment. The direct analysis of on/off states in the regions related to the tasks showed that levodopa led to both increased and decreased activation in brain regions related to inhibition, but the improved accuracy of patients correlated only with increased activation of discrete bilateral regions of the frontal gyrus. Using an fMRI-adapted variant of the Stroop test involving congruent and incongruent conditions, and analyzing facilitation and interference, two differential effects of levodopa replacement in the ventral and lateral striatum have been demonstrated. The ventral striatum, which mediates encoding of stimulus associations, decreases its activation in the levodopa “on” condition, whereas activation of the dorsal striatum (involved in interference control) increased in this condition. These findings show that fMRI explains both positive and negative effects of levodopa on cognition seen in the cognitive evaluation of PD patients (MacDonald et al., 2011). Working memory is the ability to maintain and manipulate information in temporary storage directed at giving a response after a short period. This function involves the interaction between posterior parietal/temporal and prefrontal regions. The basal ganglia also participate in maintaining this information. Regarding its neurochemistry, dopamine is the main neurotransmitter involved in working memory. On-off designs in PD patients, as well as the fact that PD patients have working memory impairment since early disease stages, have supported the role of the dopaminergic system in this cognitive function. Among several verbal and non-verbal working memory designs, spatial working memory deficits appear to be the cognitive impairment most amenable to dopamine restoration therapy (Robbins & Cools, 2014). The Sternberg paradigm is a classical working memory task that is easy to use in computed assisted neuropsychological testing in clinical practice as well as in fMRI task experiments. Poston et al. selected a Sternberg paradigm that consisted of five different numbers (high load) contrasted with five identical numbers (low load) (Poston et al., 2016). PD patients had lower accuracy than controls in the high load condition, as well as hyperactivation of the bilateral putamen and posterior insula. PD patients in the “off” state showed greater load-dependent activation of the bilateral putamen, bilateral caudate, left dorsolateral prefrontal cortex, left hippocampus, and left supplementary motor area than patients in the “on” condition. Dopaminergic restoration did not improve accuracy but led to shorter reaction times. Considering these results, the authors argued that PD patients can maintain

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intact cognitive performance in working memory through compensatory hyperactivation of the putamen, but dopamine replacement did not improve working memory performance. N-back tasks are probably the most used fMRI paradigm to assess working-memory-related activation in most neurological and psychiatric disorders. These tasks systematically activate bilateral parietal and prefrontal regions (Owen, McMillan, Laird, & Bullmore, 2005). In an early fMRI study using an event-related paradigm, patients with executive dysfunction (defined by impairment in the Tower of London test) had a pattern of hypoactivation in the caudate nuclei compared with patients with normal performance during the retrieval condition. Patients with executive dysfunction also showed hypoactivation of the caudate nucleus and dorsolateral cortices during the information-maintenance phase (mental manipulation) (Lewis, Dove, Robbins, Barker, & Owen, 2003). In a study using an on-off design, it was described that motor regions showed greater activation during the “on” state, whereas the cortical regions subserving working memory displayed greater activation during the “off” condition. These results are also consistent with evidence that the hypodopaminergic state is associated with decreased efficiency in prefrontal cortical information processing, and that dopaminergic therapy improves the physiological efficiency of this region (Mattay et al., 2002). Trujillo et al. used a visuospatial n-back working memory task and selected the bilateral inferior parietal cortex, bilateral dorsolateral prefrontal cortex, and bilateral caudate nucleus as regions of interest related to this paradigm (Trujillo et al., 2015). PD patients, compared with controls, showed increased task-related activity in the left dorsolateral prefrontal cortex and showed differences in effective connectivity within the frontoparietal network. The authors interpret that the decrease in connectivity within networks was compensated for by the hyperactivation of individual taskrelated brain areas, and that this compensation underlies the relatively preserved working memory task performance. This compensatory-mechanism interpretation was supported by the fact that dopamine transporter binding correlated with accuracy in the n-back task.

2.2 Executive Functions The Wisconsin Card Sorting Test is probably the most paradigmatic test to assess prefrontal functioning because its correct performance involves strategic planning, organized searching, using environmental feedback to shift

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cognitive sets, directing behavior toward achieving a goal, and modulating impulsive responding. In this test, a set of four cards (each containing one to four equal figures, represented by one of four possible shapes, with one of four possible colors) are presented to the subject. The participant is then shown a new card and told to match it to one of the four original cards (without being instructed on the matching rule). A positive or a negative feedback is then given according to their choice. A simplified version was found to be suitable for fMRI experiments in a study by Monchi, Petrides, Petre, Worsley, and Dagher (2001). Using an event-related paradigm, Monchi et al. described that PD patients showed decreased and increased activation in prefrontal areas depending on the type of reinforcement during the Wisconsin Card Sorting Task (Monchi et al., 2004). Decreased activation was observed in the ventrolateral prefrontal cortex when receiving negative feedback whereas greater activation was seen in the posterior and the dorsolateral prefrontal cortex when receiving positive or negative feedback. Moreover, hyper- or hypoactivation in the cortex depends on basal ganglia participation. Increased cortical activation was observed in patients compared with the control group in the condition not specifically requiring the caudate nucleus. Conversely, decreased activation was observed in the condition that significantly involves the caudate nucleus (Monchi, Petrides, MejiaConstain, & Strafella, 2007). More recently, addressing the DMN, the same group of researchers found that, while performing the Wisconsin Card Sorting Task, PD patients deactivated the prefrontal cortex similarly to the control group, but had lower deactivation in the posterior cingulate and precuneus; PD patients also showed a reversed pattern of activation and deactivation of the DMN. The authors interpreted the data as suggestive of functional disconnections secondary to dopaminergic depletion (van Eimeren, Monchi, Ballanger, & Strafella, 2009). However, in our opinion, the role of non-dopaminergic structural changes in the posterior regions of the DMN cannot be ruled out. According to principal component analysis, executive functions include three components that can be isolated by neuropsychological testing. Working memory could be assessed with the digit span forward and backward tests. Stroop interference and trail making test part B minus part A could measure response inhibition, and the Wisconsin Card Sorting Task can be used to assess task-switching abilities. These tests have been found to be impaired in PD and seem to be dissociable regarding their BOLD correlates. The results described above in part demonstrate that they

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have different neural bases, and justify their inclusion in the clinical neuropsychological assessment of executive functions in PD (Gawrys et al., 2014). Impairment in the performance of the Tower of London test in PD is also reported in several studies. For this reason, this test is mentioned in the MDS Task Force on mild cognitive impairment (MCI) in PD as a tool to assess executive functions—both to diagnose MCI and to characterize its subtype (Litvan et al., 2012). As expected, during an fMRI version of the Tower of London test, PD patients, compared with controls, showed lower task-relevant network activation both related to planning and to increasing task load. Regarding connectivity analyses, PD patients had lower task-related functional connectivity in the frontoparietal network (Trujillo et al., 2015). Farid et al. compared the BOLD response during a go/no-go task in “on” and “off” conditions (Farid et al., 2009). Patients off levodopa exhibited more extended and more numerous areas of activation than controls. After levodopa restoration, activation decreased in several brain regions, indicating that levodopa favors the efficacy of cortical regions to solve the task, or alternatively that dopamine deficiency could be compensated for by an increased recruitment of brain regions to solve this cognitive task. The cingulate cortex was more activated in its posterior regions during the “off” condition and in the anterior regions during the “on” condition. The inability to concurrently perform a cognitive and a motor task (e.g., “stops walking while talking”) is a classical symptom in PD. This impairment has been hypothesized to be caused by the increased competition between frontostriatal circuits due to dopamine depletion. The cerebral dysfunctions responsible for such impairment were investigated by Wu and Hallett (2008). During dual tasks, patients had greater activity in the cerebellum, premotor area, parietal cortex, precuneus, and prefrontal cortex compared with normal subjects, suggesting a compensatory activation due to the difficulty of this task. In another study, it has been described that, during dual tasks, PD patients recruited a striatal territory (ventro-posterior putamen) not engaged during either the cognitive or the motor task alone, and that this recruitment is not seen in normal controls. In line with the effort hypothesis, increased activity in this region was related to worse performance in the dual task. These findings agree with the well-known dorso-posterior to ventroanterior gradient of striatal dopamine depletion. The authors interpreted the deficits in dual tasks as loss of the normal segregation among the fronto-basal ganglia circuits (Nieuwhof et al., 2017).

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Some PD patients show impulsive and risk-seeking behavior that has been ascribed to dopamine agonist treatment. In a sample of early-PD patients with normal performance in memory and executive functions as measured by the Stroop and Wisconsin tests and the Game of Dice Task revealed impaired decision-making. Behavioral data showed that, compared with controls, these PD patients selected the risky options more frequently and presented a pattern of right parietal hypoactivation during the Game of Dice Task (Labudda et al., 2010). The lack of inhibitory control responsible for impulsivity has also been investigated using the stop-and-go paradigm. Despite showing slowness in the initial phase of the task, untreated patients did not show impaired performance in inhibition; nonetheless, the network related to inhibition was found to be altered. During successful inhibition, PD patients showed significantly decreased activity in the right and left inferior frontal gyrus compared with healthy controls. Dysfunction of this network could not be attributed to a dopaminergic effect. These results are in contradiction with those that found increased activation during go/no-go tasks. The authors explain that the discrepancy is due to the degree of task difficulty (Vriend et al., 2014).

2.3 Perception The perceptual cognitive tasks that merited the most attention in PD fMRI research were olfactory and emotion facial recognition. This is because these functions have been largely demonstrated to be impaired in PD even in early stages of the disease. As mentioned above, odor identification is a perceptual dysfunction seen at early stages of the disease, as well as in the premotor phase of PD. Olfactory impairment in the early stages of the degenerative process is due to neuronal dysfunction in the olfactory bulb. When the illness progresses, however, atrophy of other regions involved in the complex perception of odors could contribute to worsening this deficit. Both in patients with PD and healthy controls, olfactory stimulation activates brain regions relevant for olfactory processing (i.e., the amygdaloid complex, lateral orbitofrontal cortex, striatum, thalamus, midbrain, and the hippocampal formation). In PD, however, the amygdala and hippocampus were found not to activate in the right hemisphere. Comparing the activations of patients and controls, PD patients showed hyperactivation in the inferior frontal gyrus, anterior cingulate gyrus, and the striatum. These results are interpreted as a compensatory mechanism (Westermann et al., 2008). Similarly, Moessnang et al. found that PD patients have a pattern of

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hyperactivation during perception of common odors such as banana, pineapple, and lavender oil (Moessnang et al., 2011). Significant hyperactivation in patients, however, was observed in the piriform cortex, the main part of the primary olfactory cortex, and in the orbitofrontal area, which is an associative region that, in normal subjects, allows the most complex olfactory perception. The authors also interpreted the increased activation as a compensatory mechanism due to neurodegeneration caused by the loss of signal transmission from primary to associative regions. Impaired recognition of negative facial emotions is a visual perceptual deficit seen in early-stage PD patients. In an fMRI study using angry and fearful facial expressions, PD patients showed lower activation of the amygdala and fusiform gyrus compared with controls in both “on” and “off” states. However, after levodopa administration, both regions displayed increased activation during the task, suggesting a neurochemical basis for this deficit (Tessitore et al., 2002). Levodopa effects on activation during facial recognition were also observed in the posterior midline and lateral parts of the DMN (Delaveau et al., 2010). It could be considered that structural changes in the amygdala, orbitofrontal cortex, and the fusiform gyrus could be partly responsible for the poor performance in this task and for the hypoactivation in such regions. Patients with normal performance in facial emotion processing showed decreased bilateral putaminal activation and increased activation in the right dorsomedial prefrontal cortex. The authors of the paper believe that this increased activation reflects a top-down cognitive control to compensate for the subcortical impairment (Moonen et al., 2017). Increased cortical recruitment in patients with normal performance in facial emotion recognition was also reported by Wabnegger et al., but in the somatosensorial regions, probably reflecting the recruitment of regions involved in the mirror neuron system for imitation (Wabnegger, Leutgeb, & Schienle, 2016). Brain activation during visuoperceptual tasks such as face identification is useful to investigate possible functional abnormalities associated with visual hallucinations. In a block-design fMRI study contrasting BOLD activation during face perception and during mosaic-like colored patterns, PD patients showed a pattern of hypoactivation in several prefrontal regions compared with patients without hallucinations. The decreased activation of frontal regions may suggest that impaired face identification includes an attentional component (Ramı´rez-Ruiz et al., 2008). Language perception is poorly investigated because, according to the subcortical profile described in PD, this function is largely preserved in PD patients. Grossman et al. described that, during sentence comprehension,

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PD patients recruited several regions, such as striatal, anteromedial, prefrontal, and right temporal cortices, to a lesser extent than controls (Grossman et al., 2003). Patients furthermore showed increased activation of right inferior frontal and left posterolateral temporo-parietal areas. These results are indicative of compensatory up-regulation of cortical activity that allows patients with mild PD to maintain sentence comprehension accuracy. In summary, decreased and increased activation of several regions involved in the perception of visual and auditory stimuli is seen in PD patients. Patterns of hypo- or hyperactivation probably depend on whether the associated function is normal or impaired, and therefore on the degree of structural and functional impairment of the region that subserves that function. The hyperactivation of higher-order cortical regions directly or indirectly involved in a perceptual function is reported mainly in subjects with preserved function at the behavioral level. This suggests that the normal performance is due to the recruitment of other cognitive strategies and brain regions that can eventually solve the perceptual task.

2.4 Memory Impairment of declarative memory has been systematically found in the neuropsychological assessment of PD patients. However, following the dopaminergic hypothesis on the origin of cognitive dysfunctions in PD, memory deficits have often been interpreted as secondary to attention and executive dysfunctions. Hippocampal atrophy seen in structural MRI (Beyer et al., 2013), the neuropathological studies by Braak et al. (2003), and the correlations found between hippocampal atrophy and memory deficits called the dopaminergic explanation of memory deficits in PD into question. Memory has been found to be the cognitive function with the largest effect sizes even in newly-diagnosed patients (Muslimovic, Post, Speelman, & Schmand, 2005). Memory-task fMRI activation studies have contributed to clarify the brain regions involved in declarative memory deficits. As expected, hippocampal dysfunction plays a pivotal role, but other cortical regions contribute to memory deficits. The memory tests used in clinical practice cannot be directly applied in fMRI studies: to reduce task-related head motion, most fMRI tasks assess recognition rather than oral retrieval. In search of suitable fMRI memory tasks, Cohn et al. proposed an associative reinstatement memory task, consisting of the presentation of 144 word pairs (some previously learned, some new, and some mixed) that activated the hippocampus in normal

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subjects and also in PD patients (Cohn, Giannoylis, De Belder, Saint-Cyr, & McAndrews, 2016). This task did not correlate with classical tasks of attention or executive functions (Wisconsin Card Sorting Task and trail making test) but had significant positive correlations with declarative memory tests used in clinical practice, such as free recall and delayed recognition from the Hopkins verbal learning test. The comparison between patients and controls showed hypoactivation of the left hippocampus for patients. Thus, this task seems to be suitable to test hippocampal functionality in PD patients. Recognition memory in PD is of high clinical interest because it is relatively preserved in non-demented PD patients and is clearly impaired in demented ones (Pagonabarraga & Kulisevsky, 2012). It is therefore potentially useful as an early marker for evolution toward dementia. Using independent component analysis in an fMRI study, brain networks involved in the recognition of words were found to be impaired before patients showed deficits in this function in clinical tests used to assess declarative memory functions in PD. During a task that involved the recognition of 35 previously seen words, patients performed normally in terms of correct responses and reaction time, although they presented a trend toward more false-positive errors. Compared with controls, patients showed a pattern of decreased activations and deactivations in the recognition network. In particular, the reduced task-related BOLD signal in the orbitofrontal cortex and frontal poles in patients with PD strongly indicates an underlying dysfunction in memory-related networks in these patients. The deactivation of the DMN during the task was also significantly decreased in PD patients, indicating a failure to adequately deactivate some areas of this network such as the precuneus, supramarginal gyrus, and temporo-occipital cortex (Ibarretxe-Bilbao et al., 2011). In a 35-month follow-up study of this sample, PD patients remained stable in terms of learning and memory, as assessed by clinical neuropsychological tests. However, a decline in fMRI recognition-task performance was observed, consisting of an increase in false-positive responses. Interestingly, at follow-up, the authors observed a progressive loss in the pattern of activation and deactivation of the recognition memory network, together with a deterioration in the strength of connectivity between the main areas involved in this network. The analysis between the main regions of interest revealed that PD patients showed a progressive decrease in frontoparietal connectivity, whereas connectivity between the frontal areas remained stable (Segura et al., 2013). Using the same paradigm but with a different cohort, recognition memory has been found to be impaired in PD patients compared with healthy

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controls in the number of correct responses as well as in false-negative errors. In a region-of-interest fMRI analysis, patients showed lower activation in the right inferior orbitofrontal cortex during the task. Similar results were seen for a task involving learning of new words (Lucas-Jim
enez et al., 2015). As mentioned in the previous section, the DMN is the most studied network in resting-state fMRI studies due to its implication in Alzheimer’s disease. The areas that have been identified as part of the DMN are the medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, precuneus, medial temporal lobe, and inferior parietal cortex. Thus, part of DMN overlaps with the declarative memory circuitry. In a study using seed-based functional connectivity and fMRI with a verbal memory paradigm, functional connectivity with a seed in the posterior cingulate cortex was found to be significantly decreased in the left and right medial temporal lobe in PD patients compared with healthy controls. Functional connectivity between the posterior cingulate cortex and left medial temporal lobe correlated with verbal and visual memory, whereas connectivity between the posterior cingulate cortex and the right medial temporal lobe correlated only with visual memory. In this study, to further interpret the DMN functional connectivity correlates in PD patients a hierarchical regression model was used. The overall model combining gray and white matter correlates was the best predictor of functional connectivity differences between the posterior cingulate cortex and left medial temporal lobe in PD patients. In conclusion, this study supports that the functional connectivity alteration between the regions of the DMN is not only related to gray matter atrophy but also to alterations in adjacent white matter regions. These findings highlight the importance of investigating PD using a multimodal approach (Lucas-Jim
enez et al., 2016). In summary, learning and recognition paradigms are able to activate the hippocampus and to detect its dysfunctions in PD, and functional connectivity impairments in PD related to memory dysfunctions are explained by structural changes in gray and white matter.

3. RESTING-STATE FMRI STUDIES 3.1 Large-Scale Intrinsic Connectivity Networks Unlike task-based fMRI studies, published resting-state studies on cognition in PD usually do not address single cognitive functions. Typically, study subjects undergo a neuropsychological assessment covering several cognitive functions that tend to be altered in PD. Based on the results of this

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assessment, some studies then divide the PD patient group into cognitively unimpaired and cognitively impaired subgroups to perform intergroup comparisons. More recently, guidelines for diagnosing MCI in PD were published (Litvan et al., 2012), and several studies apply them to subdivide their patient samples. Besides classifying subjects into subgroups according to cognitive status, some studies perform correlation or regression analyses to assess the relationship between cognitive scores and the functional connectivity parameters obtained. In consonance with the literature on other neurological and psychiatric disorders, the DMN is the most studied intrinsic connectivity networks in PD. Early task-based studies indicate that, as had previously been observed in Alzheimer’s disease, PD is associated with altered patterns of activation and deactivation (Ibarretxe-Bilbao et al., 2011; van Eimeren & Monchi, 2009). The association between resting-state DMN connectivity and cognitive performance in PD was first suggested in a study including a small sample cognitively-unimpaired PD patients and using independent component analysis (Tessitore et al., 2012). The authors found that, compared with controls, PD patients had reduced functional connectivity in the right medial temporal lobe and bilateral inferior parietal cortex. Even though patients did not fulfill criteria for cognitive impairment, connectivity of the right medial temporal lobe with the DMN correlated significantly with memory performance, and that in the inferior parietal cortex was associated with visuospatial performance. In a more recent study by Hou et al. comparing early-stage, drug-naı¨ve PD patients (with MCI (PD-MCI) or with unimpaired cognition) and healthy controls, using a seed-to-voxel approach to assess the functional connectivity of the main regions of the DMN, intra-DMN connectivity reductions were found in the PD-MCI group; connectivity levels between the anterior temporal lobe and middle temporal gyrus were associated with attention/working memory, and connectivity between the hippocampus and the inferior frontal gyrus was associated with memory performance (Hou et al., 2016). In another recent study, a seed-to-voxel approach was used to study the DMN in a group of non-demented PD patients (Lucas-Jim
enez et al., 2016). PD patients were found to have reduced connectivity between the posterior cingulate cortex seed and bilateral medial temporal lobe regions. Connectivity between the posterior cingulate cortex and the left medial temporal lobe was associated with performance in verbal and visual memory, whereas connectivity between the posterior cingulate cortex and the right medial temporal lobe correlated with performance in visual cognition.

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These studies provide evidence supporting the hypothesis that DMN connectivity alterations underlie some of the cognitive deficits seen in PD but do now allow concluding whether this network plays a differential role in PD-related cognitive decline. Most recent studies do not limit their analyses to a single intrinsic connectivity networks and indicate that connectivity changes in other networks are also relevant. There is, nonetheless, considerable heterogeneity concerning the affected intrinsic connectivity networks, the pattern of connectivity alteration, and the association between specific cognitive functions and specific intrinsic connectivity networks. These discrepancies are likely due to different resting-state preprocessing strategies, variability in the cognitive tests used to assess a given function, and variability regarding the patient samples used. Again, the role of artifacts such as head motion effects should not be overlooked. One study by Baggio et al., using independent component analysis to characterize four cognitively-relevant intrinsic connectivity networks (the DMN, the dorsal attention networks, and the bilateral frontoparietal networks), assessed a sample of PD patients (divided according to the presence or absence of MCI) and healthy controls. PD-MCI patients were found to have reduced functional connectivity between the dorsal attention network and right frontoinsular regions, which correlated with deficits in attention/executive functions (Baggio et al., 2015). This connectivity reduction was not significantly associated with structural degeneration; this observation is in line with the putative role of dopamine imbalances and of the anterior insula in the etiology of attention/executive deficits in PD (Christopher et al., 2014). On the other hand, DMN connectivity with occipital and posterior parietal cortical regions was found to be increased in the PD-MCI group. This connectivity increment was then found to correlate with worse visuospatial/visuoperceptual performance, and with occipito-parietal cortical thinning. These findings agree with the hypothesis that visuospatial/perceptual deficits in PD derive from primary posterior cortical pathology rather than dopamine deficits, being markers of higher risk of progression to dementia (Williams-Gray et al., 2009). This hypothesis is supported by a longitudinal study of non-demented PD patients (Olde Dubbelink et al., 2014); in this study, posterior cortical connectivity abnormalities were found to be associated with global cognitive decline over time. In another recent study by Peraza et al., a group of newly-diagnosed PD patients (classified as having MCI or not) and healthy controls were analyzed using independent component analysis to assess connectivity within and

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between several intrinsic connectivity networks (Peraza et al., 2017). Several connectivity reductions were observed in PD patients without MCI compared with controls, involving the right frontoparietal, dorsal attention, motor, and frontal pole networks, as well as increased connectivity in the left frontoparietal network. Regarding PD-MCI patients compared with controls, connectivity reductions were only observed in the lateral visual, dorsal attention, and temporal networks. It is noteworthy that patients without MCI were the group with the highest levels of head motion. Connectivity levels in the regions of significant intergroup differences were associated with performance in different cognitive functions and motor scale scores. Not all studies, however, observed significant correlations between connectivity parameters and cognitive variables. In one study, Gorges et al. compared healthy controls, cognitively-unimpaired PD patients, and PD patients with cognitive impairment (including PD-MCI and dementia) (Gorges et al., 2015). Cognitively-unimpaired patients showed increased functional connectivity compared with controls in intrinsic connectivity networks such as the DMN, frontoparietal networks, and ventral attention network. The authors interpreted these findings as a possible compensatory response that allowed maintaining a normal cognitive performance. Patients with impaired cognition, on the other hand, displayed reduced connectivity in the DMN.

3.2 Functional Connectomics and Graph Theory A technique that has been used to study the resting-state functional bases of cognitive decline in PD is the framework of graph theory. As mentioned above, functional connectivity techniques allow the construction of the functional connectome, a network consisting of a collection of nodes (individual brain regions) connected by edges (the strength of functional connection between each pair of nodes). From this connectivity network, several graph theory parameters that summarize the topological characteristics of the brain network can be computed. In one study, drug-naı¨ve PD patients underwent resting-state fMRI and Ioflupane (123I) (DaTSCAN) imaging (Lebedev et al., 2014). Nodal strength (average connectivity strength of all edges connected to a node) was found to be associated with executive functions performance in dorsal frontal and parietal regions. This pattern was also found to correlate with nigrostriatal dopaminergic function. Memory deficits, on the other hand, were associated with lower nodal strength in prefrontal and limbic regions.

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Again supporting the hypothesis that dopaminergic dysfunction is specifically associated with attention/executive deficits in PD, memory deficits were not significantly associated with striatal dopamine levels. In another study by Baggio et al., healthy controls were compared with PD patients with and without MCI (Baggio et al., 2014). PD-MCI patients showed weakening of several connections, especially long-range, interlobar ones. Regarding topological parameters, PD-MCI patients’ functional networks displayed higher modularity (i.e., networks that can more easily be decomposed into modules of nodes that are highly interconnected, but with few connections to nodes in other modules) and higher clustering coefficients (i.e., high local interconnectivity). These measures were negatively associated with performance in memory and visuospatial/visuoperceptual tests. Furthermore, the characteristics of network hubs were assessed. Hubs are nodes that are more critical for the flow of information across the network, and are described to be more vulnerable to degenerative and other neurological processes (Crossley et al., 2014). In this study, hubs showed reduced centrality in the PD-MCI sample, indicating a reorganization of information flow away from these regions. A recent study by Rittman et al. also assessed the connectivity of network hubs in PD through resting-state fMRI, combined with regional expression of two genes (tau gene MAPT and alpha-synuclein gene SNCA) using data obtained from healthy subjects (Rittman et al., 2016). The authors observed that regions with higher MAPT expression tended to be more highly connected, i.e., more likely to be functional hubs. In PD patients, these regions were also more affected by connectivity loss. This effect was not observed with the SNCA gene. These findings indicate that tau pathology, which is hypothesized to be involved in the progression to dementia in PD (Irwin, Lee, & Trojanowski, 2013), is involved in the functional topological alterations seen in PD patients. Previous observations on the effect of dopamine modulation in healthy subjects suggest that dopamine imbalances (either disease-related deficits or treatment-related local “overdose”) may also account for some of these changes (Achard & Bullmore, 2007).

3.3 State-of-the-Art Functional Connectivity Analyses As explained above and illustrated in Fig. 1, functional connectivity techniques rely on some measure of coherence between BOLD-signal time courses of pairs of brain regions. The traditional approach includes all time

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points to derive a single, stationary measure of association (i.e., edge strength) for the entire resting-state acquisition. After initial observations that patterns of functional connectivity fluctuate over time (Chang & Glover, 2010), some studies have begun employing techniques sensitive to time-varying connectivity (dynamic functional connectivity) under the assumption that this would provide a more thorough characterization of the functional configuration of the brain. Put simply, this approach relies on the computation of pairwise time course correlations in short sliding windows across time, typically generating hundreds of connectomes—each representing a specific temporal segment of the resting-state data set. With the development of newer fMRI sequences with higher temporal resolution, dynamic connectivity studies will probably become frequent. Currently, a few published studies have used this technique to assess cognition in PD. In one study by Madhyastha et al., the dynamic connectivity between regions of networks relevant for attentional processes (DMN, dorsal attention network, and frontoparietal network) was assessed in PD patients and controls (Madhyastha, Askren, Boord, & Grabowski, 2015). It was observed that measures of dynamic connectivity in the dorsal attention and frontoparietal networks during rest were better able to explain subsequent performance in an attention test (Attention Network Test) than stationary parameters. In a more recent study, healthy controls were compared with PD patients divided into groups with and without MCI (Dı´ezCirarda et al., 2018). Analysis of the dynamic connectivity data revealed two alternating states, one of hypoconnectivity and another of hyperconnectivity. PD-MCI patients were found to show a greater number of state transitions and decreased dwell time in the hypoconnectivity state. Similar findings had previously been reported when comparing healthy controls and PD patients not stratified according to cognitive status (Kim et al., 2017), suggesting that PD is characterized by the prevalence of a state of increased interaction between intrinsic connectivity networks. The PD-MCI group also showed reduced connectivity between intrinsic connectivity networks such as the sensorimotor, visual, auditory, cognitive control, subcortical, and DMN in the hypoconnectivity state. Neuroimaging studies in general, including those addressing PD-related cognitive alterations, typically employ analyses based on group differences or correlations between imaging and clinical/cognitive parameters. To become clinically useful, however, imaging biomarkers should display more than significant differences between groups: they must also have predictive power at the individual patient level. In recent years, several studies have begun

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applying machine-learning techniques to assess the value of neuroimaging markers for individual patient classification. One recent study by Abo´s et al. addressed the discriminating power of data derived from the functional connectome and a supervised machine-learning algorithm (support vector machine) to discriminate PD-MCI from patients without cognitive impairment (Abo´s et al., 2017). Using training sample, a subset of discriminating functional connections was extracted to be used in the classification procedure. When applied to the validation sample (not involved in the training sample to provide unbiased results), the algorithm showed a discrimination accuracy of 80%. Furthermore, the mean connectivity strength across this subset of connections was found to be associated with performance in memory and executive functions. While this approach has not been extensively tested to address cognition in PD, a previous study obtained a high accuracy (93.6%) in discriminating healthy subjects from PD patients, also using the functional connectome as input data (Chen et al., 2015). These findings, alongside the development of more advanced fMRI acquisition and preprocessing techniques, provide encouraging evidence regarding the role of functional imaging in the development of non-invasive biomarkers for diagnosis, prognosis, and follow-up of cognitive impairment in PD. The discrepancy observed between studies, however, indicates that methodological differences as well as disease heterogeneity (Badea, Onu, Wu, Roceanu, & Bajenaru, 2017) indicate that several obstacles will need to be overcome before these results can be generalizable to independent PD populations.

4. CONCLUSIONS The studies summarized in this chapter show that fMRI techniques can reveal functional brain alterations associated with specific clinical aspects of PD. In particular, traditional task-based fMRI studies have provided invaluable data regarding the pathophysiological changes in the frontostriatal circuits associated with attention and executive function deficits in PD. They have also shed light on the cerebral bases of the effects of dopamine deficits and dopaminergic replacement therapy on cognition in PD, which resulted in an improved understanding of the role played by dopamine in normal attention and executive function processes. Resting-state functional connectivity studies have provided further evidence that cortico-striatal dopamine-dependent connectivity changes as well as dopamine-independent connectivity disruptions are associated with specific types of cognitive

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impairment in PD. There is still considerable variability among studies, highlighting the need to standardize image preprocessing and analytic techniques, and to invest in appropriate quality control measures. Notwithstanding these caveats, the results discussed here motivate recent attempts to develop imaging biomarkers useful at the individual-patient level, which show promising initial results.

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