Network Imaging in Parkinsonian and Other Movement Disorders: Network Dysfunction and Clinical Correlates

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Network Imaging in Parkinsonian and Other Movement Disorders: Network Dysfunction and Clinical Correlates Martin Niethammer, David Eidelberg* Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, United States *Corresponding author: e-mail address: [email protected]

Contents 1. Introduction and Overview of Imaging Techniques 2. Network Dysfunction in Parkinsonian Disorders 2.1 The PD-Related Motor Pattern 2.2 Biological Insights Into PDRP From Graph Theory 2.3 Imaging Preclinical PD 2.4 The PD-Related Tremor Pattern 2.5 Network Analysis and Cognition 2.6 Treatment-Related Network Imaging 2.7 Abnormal Networks in Atypical Parkinsonian Disorders 3. Network Analysis in Other Movement Disorders 3.1 Huntington’s Disease 4. Conclusion References

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Abstract Parkinson’s disease (PD) is classically defined as a disease of progressive dopaminergic dysfunction, thus explaining many of the levodopa-responsive motor features. However, even early in the disease, non-motor symptoms can appear, affecting sleep, cognition, and behavior. This implies the involvement of more widespread circuitry beyond the basal ganglia. In addition, the varied clinical presentation and the clinical overlap between PD and other diseases of dopamine degeneration (referred to as atypical parkinsonian syndromes), particularly early in the disease, have complicated diagnosis, treatment, and clinical trials. The increased use of functional imaging techniques, which can identify and quantify widespread functional networks, has provided insights into understanding these disorders beyond dopaminergic degeneration. In this chapter, we summarize such work as it relates to pathophysiology, diagnosis, progression, and treatment of parkinsonian disorders. We also briefly highlight findings in another neurodegenerative disorder, Huntington’s disease. International Review of Neurobiology ISSN 0074-7742 https://doi.org/10.1016/bs.irn.2018.10.004

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2018 Elsevier Inc. All rights reserved.

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1. INTRODUCTION AND OVERVIEW OF IMAGING TECHNIQUES Structural and functional imaging techniques have long been important for the study of neurological diseases in order to understand disease mechanisms or to help with diagnosis and assessment of treatments. Functional imaging, with its ability to measure regional metabolism or blood flow, provides the opportunity to assess changes in local neuronal activity. Often, such changes are detected by whole brain voxel-based comparisons to identify significant differences between different populations. However, this strategy is not able to capture changes that occur at the whole brain systems level. Neurodegenerative disease such as Parkinson’s disease, in particular, has been found to progress along specific pathways involving discrete brain networks, necessitating methods that can image these brain networks. A number of multivariate strategies have been developed for networkbased analysis of functional imaging data, especially in the resting state (Eidelberg, 2009; Habeck, 2010; Smith et al., 2011). One such approach, spatial covariance analysis, has proven useful in characterizing specific diseaserelated network abnormalities from patients with a number of neurological diseases, including parkinsonian disorders (Schindlbeck & Eidelberg, 2018; Tang, Poston, Dhawan, & Eidelberg, 2010; Tang, Poston, Eckert, et al., 2010; Vo et al., 2017), Alzheimer’s disease (Habeck et al., 2008; Mattis et al., 2016; Teune et al., 2014), Huntington’s disease (Feigin et al., 2001, 2007; Tang, Feigin, et al., 2010; Tang et al., 2013), and the dystonias (Carbon et al., 2010, 2013; Niethammer, Carbon, Argyelan, & Eidelberg, 2011). Most of the work we discuss is based on a computational technique known as the Scaled Subprofile Model (Habeck, 2010; Spetsieris & Eidelberg, 2011; Spetsieris et al., 2013). This approach merges resting imaging data from patients and control subjects and analyzes them as a combined group. Under these circumstances, variability in resting brain activity can be modeled as the multiplicative product of a large number of independent spatial elements. After removing the between-subject and between-region variability in the natural log-transformed imaging data, Scaled Subprofile Model/principal component analysis yields residual values that contain relevant biological signals that are independent of the global mean. Importantly, this model does not depend on prior assumptions regarding the functional intercorrelations between brain regions. Thus, the resulting spatial covariance patterns are entirely data driven, reflecting the relative contributions of all voxels within the network.

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The algorithm identifies linearly independent (orthogonal) sources of variability in the imaging data. One advantage of this analytical approach is that scalar expression of these patterns can be quantified in individual subjects, and the resulting spatial covariance patterns are considered to be disease related if the associated subject scores discriminate patients from controls according to prespecified criteria. Moreover, these patterns are invariant in prospective cohorts, and their expression in individual subjects of such cohorts can be directly utilized to validate the original pattern, to quantify rates of disease progression (Huang, Tang, et al., 2007; Tang, Poston, Dhawan, et al., 2010) or to objectively evaluate potential treatment effects (Feigin et al., 2007; Mure et al., 2011). While disease-specific networks can be quantified in relation to treatments or over time, these networks are derived in comparison to healthy controls, and may not necessarily change under those conditions. Thus, it can also be desirable to identify spatial covariance patterns that directly reflect the effect of treatment or progression over time in the same subjects. To this end, Habeck et al. (2005) devised ordinal trends/canonical variates analysis (OrT/CVA) to identify spatial covariance networks that increase monotonically in their expression between conditions or time points, while the correlative relationships between brain regions remain constant (cf. Dresel, Tang, & Eidelberg, 2015; Mure et al., 2012; Tang et al., 2013, for examples of applications of this method). We note that, although rigorous cross-validation procedures such as bootstrapping are typically performed to assess the stability of the initially identified network topographies, it is nevertheless essential to understand that the relevance of a given candidate network is determined ultimately by independent replication in new populations. The use of a particular covariance pattern as a biomarker also should be supported by the presence of consistent correlations between quantifiers of its expression in individual patients and independent clinical, physiological, and/or genotypic descriptors of the disease process. Ultimately, although not essential, the sensitivity or specificity of a network can be determined by long-term clinical followup or post-mortem confirmation (ensuring greater accuracy of the initial diagnosis). Typically, the work described has been performed with metabolic imaging with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET). FDG PET is widely available in Europe, North America, and Asia, and spatial covariance analysis has proven to be highly reproducible across different PET scanners and different reconstruction algorithms (Schindlbeck & Eidelberg, 2018; Tomse et al., 2017). Other PET modalities such as H215O

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PET have been used, but not as extensively validated. Nevertheless, FDG PET imaging is not universally available and does expose subjects to radiation. Unlike radiotracer imaging, MRI does not require injections, thereby not imposing a limit on the number of scans that can be performed. Accordingly, we will also discuss the recent development of analogous methods using resting-state functional MRI (rs-fMRI) techniques, which will be of potentially greater use in clinical practice. The main focus of this chapter will be on parkinsonian disorders, but we also briefly highlight some work in another movement disorder, Huntington’s disease. Work done in other disease, such as the dystonias, is beyond the scope of this chapter.

2. NETWORK DYSFUNCTION IN PARKINSONIAN DISORDERS 2.1 The PD-Related Motor Pattern FDG PET allows for the mapping of spatially distributed regions, as opposed to looking at isolated brain regions, but using multivariate methods as described above. Moreover, unlike methods relying on visual examination (Eckert et al., 2005; Hellwig et al., 2012), regions of interest, or connectivity analysis (Ko, Lee, & Eidelberg, 2017; Sala et al., 2017), spatial covariance methods quantify expression of disease-related patterns as a single measure, making this method extremely suitable for clinical applications. When applied to resting-state FDG PET scans from PD patients, this method identified an abnormal disease-related spatial covariance pattern, termed PDRP (Eidelberg, 2009; Eidelberg et al., 1994; Niethammer, Feigin, & Eidelberg, 2012). Specifically, this pattern was identified by voxel-wise analysis of FDG PET scans from a combined group of PD patients and healthy controls (Ma, Tang, Spetsieris, Dhawan, & Eidelberg, 2007; Spetsieris & Eidelberg, 2011). PDRP involves many elements of the cortico-striatopallido-thalamo-cortical circuit and is characterized by increased pallidal, thalamic, and pontine metabolic activity, coupled with relative reductions in premotor cortex, supplemental motor area, and parietal association areas (Fig. 1A). Indeed, the network-level abnormalities seen in PD do reflect the spatial distribution of the underlying pathogenesis (Ko, Lee, et al., 2017), reflected by the accumulation of α-synuclein (Surmeier, Obeso, & Halliday, 2017). PDRP is not restricted to humans, with an analogous pattern (termed PRP) seen in a non-human 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) primate model of PD (Ma et al., 2015, 2012; Peng, Ma, Flores,

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Fig. 1 Parkinson’s disease-related motor patterns. (A) Parkinson’s disease-related pattern (PDRP). This metabolic spatial covariance pattern is characterized by hypermetabolism in the thalamus, globus pallidus, pons, and primary motor cortex, associated with relative metabolic reductions in the lateral premotor (PMC) and posterior parietal areas (Ma et al., 2007). Relative metabolic increases are displayed in red; relative metabolic decreases are displayed in blue. Slices are overlaid on a standard MRI brain template. (B) PD tremor-related metabolic pattern (PDTP) identified using a within-subject network analysis of FDG PET scans from tremor-dominant PD patients scanned at baseline and during ventral intermediate (Vim) thalamic deep brain stimulation (Mure et al., 2011). This pattern is characterized by covarying increases in the metabolic activity of (Continued)

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Doudet, & Eidelberg, 2010) (Fig. 1C). Beyond FDG PET, PDRP-like patterns have also been derived from H15 2 O PET (Ma & Eidelberg, 2007), and 99m Tc-ethylcysteinate dimer SPECT (Eckert, Van Laere, et al., 2007; Feigin et al., 2002), though those modalities have not been applied in larger studies. PDRP has been extensively studied in numerous independent populations around the world, demonstrating a remarkable degree of consistency that network analysis can provide. Initially, it was shown that PDRP expression in individual subjects was highly reproducible at a single imaging center, remaining stable over hours to weeks (Ma et al., 2007). Expanding on that, PDRP can be derived from independent PD populations scanned with different scanners, with a high degree of consistency in region weights (Fig. 2A). In six independent cohorts, individual PDRP expression was consistently elevated compared to healthy controls, when scanned at rest at least 12 h after their last medication dose (i.e., in the off-state) (Fig. 2B) (Eidelberg, 2009; Holtbernd et al., 2015; Teune et al., 2013; Tripathi et al., 2016; Wu et al., 2013). The same held true, when subjects were scanned early in the disease in drug-naı¨ve patients, or in patients scanned 1 h after their medication dose. Thus, elevated PDRP expression appears to be a universal feature of PD, independent of treatment, or disease state. PDRP expression correlates strongly with spontaneous subthalamic nucleus activity recorded intraoperatively during deep brain stimulation surgery. These observations relate the abnormal PDRP functional topography directly to degeneration of nigrostriatal dopaminergic pathways (Tang, Poston, Dhawan, et al., 2010). Fig. 1—cont’d the sensorimotor cortex (SMC), cerebellum/dentate nucleus (DN), pons, and the putamen. (C) PRP, a PDRP-like pattern identified in non-human primates with MPTP-induced parkinsonism. This pattern is characterized by increased glucose metabolism in putamen/globus pallidus (GP) and sensorimotor regions, and decreased metabolism in posterior parietal cortices. (D) PRP scores were elevated in the MPTP-lesioned animals compared with the healthy controls in both the pattern derivation sample (left) and the validation sample (right). PRP scores were similar between the normal controls but mostly lower in the parkinsonian animals in the validation sample than those in the derivation sample. Panel (A): Reprinted from Eidelberg, D. (2009). Metabolic brain networks in neurodegenerative disorders: A functional imaging approach. Trends in Neurosciences, 32(10), 548–557. Copyright (2009), with permission from Elsevier; Panel (B): Adapted and reprinted from Mure, H., Hirano, S., Tang, C. C., Isaias, I. U., Antonini, A., Ma, Y., et al. (2011). Parkinson’s disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage, 54(2), 1244–1253. Copyright (2011), with permission from Elsevier; Panels (C) and (D): Reprinted from Ma, Y., Johnston, T., Peng, S., Zuo, C., Koprich, J., Fox, S., et al. (2015). Reproducibility of a parkinsonism-related network in nonhuman primates: A descriptive pilot study. Movement Disorders, 30(9), 1283–1288. Copyright (2015), with permission from John Wiley and Sons.

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Fig. 2 PDRP is reproducible across independent cohorts. (A) Region weights on PD-related spatial covariance patterns identified in four independent cohorts of patients and healthy control subjects scanned with FDG PET. Significant disease-related topographies from the different populations are depicted by colored lines connecting (Continued)

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Importantly for use as a biomarker, individual PDRP expression scores reliably correlate with Unified Parkinson’s Disease Rating Scale (UPDRS) motor ratings (Asanuma et al., 2006; Eidelberg et al., 1994, 1995; Feigin, Fukuda, et al., 2001; Lozza et al., 2004; Niethammer & Eidelberg, 2012). Additionally, PDRP expression declines (i.e., normalizes) with treatment, either dopaminergic medication or deep brain stimulation (Asanuma et al., 2006; Pourfar et al., 2009). Likewise, in a primate model of PD, PRP expression improved after retinal pigment epithelial cell implantation (Peng et al., 2016). As is the case with dopaminergic imaging, PDRP subject scores correlate mainly with bradykinesia and rigidity, rather than tremor (Antonini et al., 1998; Isaias et al., 2010; Mure et al., 2011). In fact, tremor Fig. 2—cont’d the loadings on 30 standardized regions of interest (ROIs). The PDRP gold standard pattern (Fig. 1A) is represented by the red line. Additional disease-related metabolic patterns were subsequently identified by spatial covariance analysis of data from separate groups of PD patients and control subjects scanned using the Siemens Biograph PET/CT camera (4.5 mm FWHM) at Huashan Hospital (Shanghai, China), the GE Discovery PET/CT camera (5.2 mm FWHM) at the Institute of Nuclear Medicine and Allied Sciences (New Delhi, India), and the Siemens HR1 PET camera (4.1 mm FWHM) at Groningen University Hospital (Groningen, the Netherlands). These patterns are, respectively, depicted by green, light blue, and dark blue lines. Voxel weights on the PDRP exhibited a close correlation with corresponding regional values on the subsequent disease-related metabolic topographies (r ¼ 0.90, P < 0.001). In all cohorts, diseaserelated metabolic patterns were identified according to prespecified criteria provided elsewhere (Spetsieris & Eidelberg, 2011). Region weights (y-axis) of absolute value 0.5 (dashed lines) denote ROIs in which local glucose metabolism contributed significantly to network activity (P < 0.025). (B) PDRP expression reliably discriminates PD subjects from healthy subjects. PDRP expression was increased in PD patients relative to normal subjects in the North Shore University Hospital derivation cohort (left, P < 2  107) (Ma et al., 2007) and PD patients consistently show elevated PDRP expression relative to normal controls across six independent testing samples when scanned in a medication-free (off ) state (Eidelberg, 2009; Holtbernd et al., 2015; Ko, Spetsieris, et al., 2017; Teune et al., 2013; Tomse et al., 2017; Tripathi et al., 2016; Wu et al., 2013). Cohorts are color-coded, with open circles representing healthy control subjects and filled circles representing PD patients. Group mean values and standard errors are plotted next to individual z-scored values. P-values (top) and number of subjects (bottom) are provided for each prospective sample. Panel (A): Reprinted from Niethammer, M., & Eidelberg, D. (2012). Metabolic brain networks in translational neurology: Concepts and applications. Annals of Neurology, 72(5), 635–647. Copyright (2012), with permission from John Wiley and Sons; Panel (B): Adapted and reprinted from Schindlbeck, K., & Eidelberg, D. (2018). Network imaging biomarkers: Insights and clinical applications in Parkinson’s disease. The Lancet Neurology, 17(7), 629–640. Copyright (2018), with permission from Elsevier.

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in PD shows a relation with a distinct tremor-related network (see below). It should be noted that these measures are not interchangeable (Eckert, Tang, & Eidelberg, 2007), describing separate yet related aspects of the disease. Previous studies had reported metabolic correlations with striatal and frontal [18F]-fluoro-L-dopa (FDOPA) uptake (Berti et al., 2010; Polito et al., 2012), but a recent study found more widespread correlations between FDOPA uptake and PDRP-related areas (Holtbernd et al., 2015). Indeed, PDRP expression does correlate with dopaminergic imaging measures obtained from FDOPA PET scans in the same subjects (Holtbernd et al., 2015). However, this correlation was restricted to FDOPA uptake in the posterior putamen, and of modest size. While PDRP and other disease-related covariance patterns have been well characterized in FDG PET scans, this approach relies on the availability, of short-lived radiotracers, and the technology is often limited to tertiary centers. Furthermore, PET imaging exposes subjects to radiation, which may limit the clinical utility of this approach, especially if it were to be used as a screening tool. Applying the network approach on arterial spin labeling MRI did show some success in identifying PD-related patterns (Ma et al., 2010; Melzer et al., 2011). rs-fMRI has shown changes in connectivity in PD (Helmich, Janssen, Oyen, Bloem, & Toni, 2011; Szewczyk-Krolikowski et al., 2014; Wu et al., 2011), and principal component analysis has been used to identify a significant PD-related covariance pattern (Wu et al., 2015). The use of rs-fMRI has been further refined, applying independent component analysis (Beckmann, DeLuca, Devlin, & Smith, 2005; Calhoun, Liu, & Adali, 2009) and bootstrap resampling to identify an MRI-based PDRP, termed fPDRP (Vo et al., 2017). This pattern is characterized by increased activity in the basal ganglia, thalamus, cerebellum/pons, anterior cingulate cortex, and supplementary motor area (Fig. 3A). There is considerable topographic similarity to the PET-derived PDRP, and fPDRP expression separates PD patients from healthy controls. Moreover, like PDRP, fPDRP expression exhibits correlation with clinical ratings of akinesia-rigidity, but not tremor (Fig. 3B). Re-scanning subjects following their usual dopaminergic medication leads to declines in network activity. The same method also identified a cognition-related pattern (fPDCP), which resembles the PET-derived cognitive pattern discussed below (Vo et al., 2017). Thus, fMRI has the potential to become a non-invasive method for network analysis in PD, though further studies in independent cohorts will be needed.

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Fig. 3 Functional MRI-derived PDRP. (A) PDRP identified in resting-state functional MRI (fPDRP, left) and PET (pPDRP, right) is shown on the Montreal Neurological Institute (MNI) 152 template. fPDRP, derived from 20 normal controls and 20 PD patients, is characterized by increased activity in the basal ganglia, thalamus, cerebellum/pons, anterior cingulate cortex, and supplementary motor area. The major network regions of fPDRP corresponded closely to the metabolically active (red areas) regional counterparts of the pPDRP topography. [The color stripes show Z-values thresholded at 0.5. Activity increases (fPDRP) or relative metabolic increases (pPDRP) are displayed in red; relative metabolic decreases (pPDRP) are displayed in blue.] (B) fPDRP subject scores correlated with UPDRS ratings for akinesia-rigidity (r ¼ 0.61, P < 0.005, circles) in the PD subjects; tremor ratings measured in the same subjects exhibited only a marginal relationship with network expression values (r ¼ 0.39, P ¼ 0.09, triangles). Panels (A) and (B): Reprinted from Vo, A., Sako, W., Fujita, K., Peng, S., Mattis, P. J., Skidmore, F. M., et al. (2017). Parkinson’s disease-related network topographies characterized with resting state functional MRI. Human Brain Mapping, 38(2), 617–630. Copyright (2017), with permission from John Wiley and Sons.

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2.2 Biological Insights Into PDRP From Graph Theory While PDRP does have clinical relevance, the biological basis underlying PDRP is not immediately clear. It is not obvious that PDRP represents a distinct brain network or connected nodes. Recently, Ko et al. approached this question by using graph theory to analyze whole brain functional connections (Ko, Spetsieris, & Eidelberg, 2017; Sporns, 2014). While PDRP assigns regional values according to each area’s functional significance to the abnormal PD topography, the graph theoretical representation, by contrast, is more of a topographical schematic based on functional relationships between major regions. This was followed by applying a social network computation to the graphs, delineating anatomical-functional connections within PDRP, and also allowing for visualization of mutually enhancing vs competing functional interactions between pairs of nodes (Correa, Crnovrsanin, & Ma, 2012; Correa, Crnovrsanin, Ma, & Keeton, 2009; Ko, Spetsieris, et al., 2017). The PD network exhibits a distinctive core-periphery structure that is not present in multiple groups of healthy subjects (Fig. 4A and B). Its core is defined by dense, mutually facilitating connections between metabolically active nodes in the putamen, globus pallidus, and thalamus. The periphery, on the other hand, contains metabolically less active cortical regions with weaker node-to-node interactions. Additionally, there was a separate module of interconnected, metabolically active nodes involving the cerebellum, pons, frontal cortex, and limbic regions. Organizationally, this PD network is characterized by an exaggeration of the small-world property, i.e., PD subjects (humans and non-human primates) have a greater-than-normal number of functional interconnections between nodes in the PDRP space (Ko, Spetsieris, et al., 2017). In general, such small-world properties, with increased clustering and reduced average path length, are thought to achieve efficient information processing at lower energetic cost (Bassett & Bullmore, 2016). Even in healthy controls, PDRP exhibits some degree of small-world property, likely reflecting the high information-processing demands on these brain regions even in the normal state. The increased network small-worldness in PD is consistent with a maladaptive response to nigrostriatal dopamine loss, with noisy and inefficient information transfer between network regions. Of note, the PD hyperconnectivity is arranged in discrete subnetworks consisting of three nodes each: the putamen, globus pallidus, and thalamus on the one hand (Fig. 4C, left), and the pons, cerebellum, and frontal cortex on the other hand (Fig. 4C, right), which may

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Fig. 4 Graph theoretic analysis insight into abnormal network activity underlying PD disease. (A) Abnormal network-level clustering in Parkinson’s disease. Graph theory can be used to identify the regions within the network space in which clustering

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mediate distinct clinical features such as bradykinesia-rigidity or tremor (Helmich, 2018; Ko, Spetsieris, et al., 2017; Ma et al., 2015; Ma, Chen, He, Ma, & Feng, 2017). It is interesting to note that these small-world changes were not reversed by clinically effective levodopa treatment, though levodopa does partially normalize the average path length between network nodes to improve information transfer (Ko, Spetsieris, et al., 2017). Future studies across multiple centers and different treatment modalities will be needed to further understand these network changes.

(defined by the number of triangles or “closed triples” formed when a node’s nearest neighbors are connected) is increased in one group of subjects relative to another. In the group of healthy controls (n ¼ 33), three discrete sets of interconnected nodes (open triples) were seen in: (1) the putamen, globus pallidus, and the thalamus; (2) the pons, cerebellar vermis, and premotor/prefrontal cortex; and (3) superior and middle frontal gyri, and inferior parietal lobule. (B) In the PD group (n ¼ 33, age-matched to the healthy subjects), additional interactions (“edges,” indicated by heavy lines) were detected, sealing off each of the triples as a discrete triangle. These edges denote specific node-to-node functional interactions present in PD but not in healthy subjects. Notably, the closed triples (triangles) in (1) and (2) were located within the core zones identified in the structural analysis of the PD network. These were formed by abnormal functional connections linking the nearest neighbors of core nodes through bidirectional, mutually facilitating interactions (red arrows). [Edges conforming to known anatomical connections between nodes are represented by black solid lines. Edges corresponding to functional links between nodes without corresponding direct anatomical correlations are represented by hatched lines. Corresponding edge values are represented by red curved arrows.] (C) Graph visualization based on the top 1% of centrality derivatives. In this radial graph display of the connectivity data, nodes connected by edges with high centrality sensitivity were positioned close to the center, while those with lower sensitivity were positioned in the periphery. In this directed graph, incident edges are represented by arrows; the radius of each node is proportioned to local EC. For each network node, corresponding PDRP region weights were color-coded such that metabolically active regions (PDRP weights 1.0) were depicted in red while relatively underactive regions (PDRP weights   1.0) were depicted in blue. This display revealed the presence of two distinct nodal clusters: a prominent basal gangliathalamo-cortical subnetwork with a distinct core-periphery mesostructure (left), and a smaller discrete subnetwork involving primarily ponto-cerebellar and limbic interconnections (right). Both clusters were centered around cores defined by high magnitude mutually reinforcing node-to-node interactions (i.e., edges with relatively high, positive centrality derivative values). Interestingly, the core nodes identified. in this display corresponded almost exclusively to metabolically active (red) PDRP regions, whereas the underactive (blue) regions were localized mainly to the network periphery. Panels (A)–(C): From Ko, J. H., Spetsieris, P. G., & Eidelberg, D. (2017). Network structure and function in Parkinson’s disease. Cerebral Cortex, 1–15 [ePub ahead of print], by permission of Oxford University Press.

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2.3 Imaging Preclinical PD Although abnormal network activity is a feature of early PD and increases over time (Ko et al., 2014; Niethammer & Eidelberg, 2012), precisely when the disease-related metabolic patterns occur is unknown. In general, the progression rate after the development of symptoms is similar for different cohorts, suggesting a fairly linear process (Ko et al., 2014; Tang, Poston, Dhawan, et al., 2010). Quantification of PDRP in each hemisphere in patients with early PD affecting only one side demonstrated that PDRP expression is asymmetric, with similar progression in both hemispheres while preserving the initial asymmetry (Tang, Poston, Dhawan, et al., 2010). Moreover, PDRP expression was already abnormal in the clinically unaffected hemisphere at baseline, suggesting that PDRP expression does not simply reflect symptoms, but begins to become abnormal even in preclinical disease. Identifying subjects at high risk for developing PD is an important goal for future trials of disease-modifying agents, though subjecting entire healthy populations to nuclear imaging is clearly not a reasonable option. Thus, focus has been on identifying potential markers of preclinical disease. While some genetic risk factors have been identified for PD, those are relatively rare (Karimi-Moghadam, Charsouei, Bell, & Jabalameli, 2018). A number of clinical symptoms have been studied as potential biomarkers of prodromal disease, such as family history, hyposmia, or constipation. The presence of REM sleep behavior disorder (RBD), especially if proven by polysomnography, appears to carry an especially high risk (Berg et al., 2015). Indeed, about 50% of patients with a diagnosis of idiopathic RBD (iRBD) develop a synucleinopathy (most commonly PD, but also dementia with Lewy bodies (DLB) and multiple system atrophy (MSA)) within 5 years (Iranzo, Santamaria, & Tolosa, 2016). Accordingly, there has been interest in applying network imaging to such patients (Barber, Klein, Mackay, & Hu, 2017). Voxel-wise comparison between iRBD subjects and healthy controls found increased metabolism in the hippocampus/parahippocampus, cingulate, supplementary motor area, and pons, and decreased metabolism in the occipital/lingual gyrus (Ge et al., 2015). Metabolism in several of these areas correlated either with disease duration or chin electromyography (Ge et al., 2015). On a network level, PDRP expression has been studied cross-sectionally in at least four independent iRBD cohorts. In each cohort, PDRP expression was found to be elevated compared to healthy controls,

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Fig. 5 PDRP activity as predictor of phenoconversion in subjects with REM sleep behavior disorder (RBD). PDRP expression was elevated in subjects with RBD who subsequently developed clinical manifestations of PD or DLB (black diamonds) relative to those who did not phenoconvert (CON[]) (black triangles). The three subjects with RBD who subsequently developed possible or probable MSA (black squares) exhibited negative network scores, indicative of expression levels that were below the normal mean. Three nonphenoconverters with high baseline PDRP expression are demarcated by arrows. Logistical regression analysis using a combination of PDRP expression values and age at the time of imaging accurately classified the subjects (P < 0.0001) as either phenoconverters to PD/DLB (blue ellipse) or MSA (green ellipse) or as nonphenoconverters (red ellipse). DLB ¼ dementia with Lewy bodies; MSA ¼ multiple system atrophy; PD ¼ Parkinson’s disease. Reprinted with permission from Holtbernd, F., Gagnon, J. F., Postuma, R. B., Ma, Y., Tang, C. C., Feigin, A., et al. (2014). Abnormal metabolic network activity in REM sleep behavior disorder. Neurology, 82(7), 620–627.

mostly at levels between patients with early PD and healthy controls (Holtbernd et al., 2014; Meles et al., 2018, 2017; Wu et al., 2014). Interestingly, in one study, the three subjects that developed MSA, had abnormally low PDRP scores (Fig. 5) (Holtbernd et al., 2014). Even patients with longstanding (average 12 years) of iRBD show evidence of prodromal PD markers (Iranzo et al., 2017), suggesting a potential overlap between the two diseases even in the absence of overt conversion to PD. In line with this, a metabolic covariance pattern for iRBD was recently described, characterized by relative hypermetabolism in the cerebellum, brainstem, thalamus, sensorimotor cortex, and hippocampus, with hypometabolism in the middle cingulate, temporal, occipital, and parietal cortices, a topography with partial overlap with PDRP (Meles et al., 2018). While identified

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in patients with iRBD, expression of this pattern was similar in PD patients regardless of the presence of RBD, suggesting a possible common pathway.

2.4 The PD-Related Tremor Pattern Within the clinical trial of PD, resting tremor has a somewhat unique place and does not consistently respond to dopaminergic treatment. Indeed, tremor is thought to involve a somewhat distinct pathophysiology compared to bradykinesia or rigidity (Zaidel, Arkadir, Israel, & Bergman, 2009). In addition to the cortico-striato-pallido-thalamo-cortical motor circuits that relate bradykinesia and rigidity, cerebello-thalamo-cortical pathways are involved in tremor (Helmich et al., 2011). Indeed, PDRP expression is not affected by severity or even presence of coincident parkinsonian tremor (Antonini et al., 1998; Eidelberg et al., 1995; Feigin et al., 2002). Applying OrT/CVA (Habeck et al., 2005) to metabolic images from tremulous PD patients acquired at baseline and following tremor suppression by stimulation of the ventral intermediate (Vim) thalamic nucleus, Mure et al. (2011) identified a network that specifically relates to PD tremor. OrT/CVA searches data from subjects scanned in multiple conditions for covariance patterns that consistently change in expression across time or treatment states. The tremor-related pattern (termed PDTP) was characterized by increased metabolic activity of the cerebellum/dentate nucleus and primary motor cortex, and, to a lesser degree, the caudate/putamen (Fig. 1B), brain regions known to be interconnected through the Vim thalamic nucleus (Bostan, Dum, & Strick, 2010; Hoshi, Tremblay, Feger, Carras, & Strick, 2005). At baseline (i.e., without stimulation), pattern expression values (subject scores) correlated significantly with concurrent accelerometric measurements of tremor amplitude. Moreover, Vim stimulation resulted in consistent reductions in pattern expression along with clinical improvement. Importantly, prospective PDTP computations in an independent group of 41 PD patients showed this network to be related specifically to the severity of tremor, but not akinesia-rigidity (Mure et al., 2011). On a network level, tremor ratings correlated with PDTP expression, but not with PDRP values measured in the same patients, highlighting the functional difference between these two PD-related metabolic networks. Indeed, comparison of the effects of Vim thalamic and subthalamic nucleus stimulation on network activity during tremor suppression further underscored these differences. At baseline (no stimulation), PDTP expression was abnormally elevated

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in PD patients with either Vim thalamic or subthalamic nucleus DBS implanted electrodes. In line with other clinical studies, tremor was effectively improved by stimulation at either target, whereas only subthalamic nucleus stimulation was able to improve akinesia-rigidity. Accordingly, both stimulations led to reductions of abnormally elevated PDTP expression at baseline, though the magnitude of the network response was notably greater for Vim stimulation. On the other hand, baseline PDRP elevations, were only suppressed by subthalamic nucleus DBS, consistent with the clinical benefit (Mure et al., 2011). Thus, PDTP and PDRP appear to be functionally independent networks, both as symptom biomarkers and targets of intervention.

2.5 Network Analysis and Cognition While the clinical diagnosis of PD rests largely on the motor signs and symptoms (Postuma et al., 2015), non-motor symptoms can be prominent and even precede the motor symptoms (Berg et al., 2015; Lang, 2011). For example, RBD, as discussed earlier, represents a strong risk factor for PD (Berg et al., 2015). Cognitive dysfunction can be substantial in PD, typically appearing later in the disease and progressing slower. Nevertheless, the point prevalence of PD is high (Aarsland & Kurz, 2010), and with time the majority of patients will develop cognitive impairment or dementia (Aarsland et al., 2017). The nature of progression to cognitive impairment and dementia in PD is not entirely clear, but may involve multiple transmitter systems, beginning with dopaminergic dysfunction, and involving cholinergic or noradrenergic systems later (Bohnen et al., 2006; Emre et al., 2004; Gratwicke, Jahanshahi, & Foltynie, 2015; Kehagia, Barker, & Robbins, 2013; Schapira, Chaudhuri, & Jenner, 2017). Regardless of pathology, cortical metabolic reductions are often evident early in PD, even in cognitively intact patients (Hosokai et al., 2009; Huang, Mattis, et al., 2007; Pappata et al., 2011). Moreover, different non-motor symptoms of PD correlate with distinct metabolic changes, such as cognitive dysfunction with increased posterior cingulate metabolism and decreased temporoparietal lobe metabolism, and depressive symptoms with increased amygdala metabolism (Carbon et al., 2003; Huang et al., 2013; Lozza et al., 2004; Mentis et al., 2002). Network analysis allows for perspective on widespread changes regardless of underlying pathology. Indeed, spatial covariance analysis has revealed a specific metabolic topography associated with cognitive function in PD. This network, termed PDCP, was originally identified in a cohort of

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non-demented PD patients and is characterized by hypometabolism of dorsolateral prefrontal cortex, rostral supplementary cortex, precuneus, and posterior parietal regions, associated with relative metabolic increases in the cerebellum (Huang et al., 2008; Huang, Mattis, et al., 2007; Mattis, Tang, Ma, Dhawan, & Eidelberg, 2011) (Fig. 6A). Much like PDRP, PDCP is not restricted to a single site or imaging modality, as a similar pattern has been derived independently from a separate cohort imaged with a different scanner, with high correlation between expression scores of the two versions (Meles et al., 2015). As was done for PDRP, a cognitive pattern analogous to the PET-derived PDCP has been derived from rs-fMRI scans (Vo et al., 2017). Clinically, PDCP expression correlates with performance on neuropsychological tests of memory and executive functioning (but not motor disability) in non-demented PD patients in the original cohort, as well as independent validation cohorts (Huang et al., 2008; Meles et al., 2015). Additionally, PDCP expression increases with increasing cognitive dysfunction, with substantial increases seen in PD patients with dementia or in DLB (Ko, Lee, et al., 2017; Mattis et al., 2016) (Fig. 6B). Importantly, PDCP does not simply reflect cognitive decline regardless of cause. Synucleinopathies such as PD and DLB show less metabolic decline in the medial temporal lobes compared to Alzheimer’s disease. Consistent with this, PDCP is topographically distinct from the independently characterized AD-related patterns (ADRP), which exhibits relatively decreased metabolism in the hippocampal and temporoparietal regions (Mattis et al., 2016; Teune et al., 2014) (Fig. 6C). ADRP expression was significantly higher in patients with mild cognitive impairment (MCI) who progress to AD compared to those that do not (Meles et al., 2017). When compared in separate cohorts of AD and PD patients, ADRP but not PDCP expression was elevated in AD and correlated with cognitive measures in those subjects (Mattis et al., 2016). Conversely, patients with PD showed slight ADRP expression (Fig. 6D), likely due to topographic overlap with PDCP, but only their PDCP expression values correlated with clinical measures and increased as cognitive function and executive performance declined (Mattis et al., 2016). These findings further emphasize that the network approach identifies disease-specific patterns that reflect underlying pathology rather than specific clinical symptoms, though in a given disease its pattern expression does correlate with clinical findings in those patients. Independent component analysis has shown some promise in predicting progression from MCI to AD, though to our knowledge, this has not been applied to PD to date (Pagani et al., 2017).

Fig. 6 See legend on opposite page.

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Functionally, the two main PD-related metabolic patterns (PDRP and PDCP) are independent. Although both PDRP and PDCP expression progress over time (Tang, Poston, Dhawan, et al., 2010), PDCP expression lags behind (Schindlbeck & Eidelberg, 2018; Tang, Poston, Dhawan, et al., 2010), consistent with cognitive dysfunction being a feature developing later in PD. While the PDCP topography does encompass both dopaminergic and cholinergic projections, as would be expected with the more widespread dysfunction seen in more advanced PD, there is a relation to dopaminergic Fig. 6 Cognition-related patterns. (A) The Parkinson’s disease-related cognitive pattern (PDCP) was identified by spatial covariance analysis of FDG PET scans from 15 patients with PD with varying levels of cognitive dysfunction (Huang, Mattis, et al., 2007). PDCP is characterized by reduced activity in the presupplementary motor area, premotor, and prefrontal regions and in parietal associative cortex, with relative increases in the cerebellar vermis and dentate nuclei. Inset: PDCP expression correlates with performance on neuropsychological tests of memory and executive functioning in non-demented PD patients. For the California Verbal Learning Test: Sum 1 to 5 (CVLTsum), this correlation was significant for the entire cohort (n ¼ 56: r ¼ 0.67, P < 0.001), as well as for the original group used for pattern derivation (circles, n ¼ 15: r ¼ 0.71, P < 0.003) and in two prospective validation groups (squares, n ¼ 25: r ¼  0.53, P < 0.007; triangles, n ¼ 16: r ¼  0.80, P < 0.001) (Huang et al. 2007). (B) Mean PDCP expression values are displayed for patients with PD with no evidence of mild cognitive impairment [MCI(); n ¼ 18], single-domain MCI [MCI(s); n ¼ 30], multiple-domain MCI [MCI(m); n ¼ 30], and PD with dementia (PDD; n ¼ 11); values for normal controls (NL; n ¼ 15) are provided for reference (see text). Arrows indicate post-hoc Dunnett test relative to MCI() group. (C) The Alzheimer’s disease-related pattern (ADRP), identified by spatial covariance analysis of FDG PET scans of 20 patients with AD and 20 normal controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), was characterized by reduced activity in the hippocampus, parahippocampal gyrus, and parietal and temporal association regions, with relative increases in the cerebellum, sensorimotor cortex, and supplementary motor area. In this derivation sample ADRP expression values (subject scores) accurately discriminated patients from healthy controls (P < 0.0003; permutation test). Prospectively computed ADRP subject scores achieved comparable group separation (P < 0.004; Student’s t-test) in two separate testing samples. (D) Unlike PDCP, ADRP’ subject scores, reflecting the expression of the AD-related pattern after the exclusion of ADRP/PDRP overlap regions, did not differ significantly from normal in any of the PD subgroups, regardless of cognitive status. Panel (A): Reprinted with permission from Mattis, P. J., Niethammer, M., Sako, W., Tang, C. C., Nazem, A., Gordon, M. L., et al. (2016). Distinct brain networks underlie cognitive dysfunction in Parkinson’s and Alzheimer’s diseases. Neurology, 87(18), 1925–1933; From Niethammer, M., Feigin, A., & Eidelberg, D. (2012). Functional neuroimaging in Parkinson’s disease. Cold Spring Harbor Perspectives in Medicine, 2(5), a009274, by permission of Cold Spring Harbor Laboratory Press; Panels (B)–(D): Reprinted with permission from Mattis, P. J., Niethammer, M., Sako, W., Tang, C. C., Nazem, A., Gordon, M. L., et al. (2016). Distinct brain networks underlie cognitive dysfunction in Parkinson’s and Alzheimer’s diseases. Neurology, 87(18), 1925–1933.

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dysfunction. PDRP expression correlates with dopaminergic dysfunction in the caudate and putamen; this remains significant only on the posterior putamen once corrected for PDCP (Holtbernd et al., 2015). Conversely, PDCP expression is correlated with dopaminergic dysfunction in the anterior striatum (Holtbernd et al., 2015; Niethammer et al., 2013) (Fig. 7A and B), highlighting the importance of nigrostriatal input into the caudate and cognitive functioning even in early PD. Nevertheless, the correlations between PDCP expression and striatal dopaminergic function are modest, suggesting that cognition-related metabolic network activity is not determined solely by loss or dysfunction of nigral dopaminergic projections to the caudate. Rather, extra-striatal dopaminergic and other neurotransmitter systems and/or pathological processes are likely to be involved in the disease. The partial relation between PDCP expression and dopaminergic dysfunction may help explain why non-demented PD patients can have different changes in cognition when receiving dopaminergic treatments. Specifically, improvement in verbal learning that some patients exhibit with levodopa treatment, depends on baseline PDCP expression (Mattis et al., 2011). PD patients with caudate tracer uptake in the 35–50% range exhibit modest PDCP elevations and show improved cognitive response with medications. In contrast, those with a relatively intact caudate dopaminergic system (and low PDCP values) exhibit cognitive decline with levodopa, which is in accordance with a dopamine overdose hypothesis (Carbon et al., 2004; Cools, 2006). Similarly, patients with advance dopaminergic dysfunction and high PDCP scores lose the cognitive benefit from levodopa, possibly due to advance pathology in key PDPC nodes (Niethammer et al., 2013) (Fig. 7C). Similar to findings in the motor-related networks (Ko, Spetsieris, et al., 2017), graph theory has found increased local connectedness with decreased long-range connections in PD subjects with MCI (Baggio et al., 2014). PD-MCI patients showed a marked reduction in the average correlation strength between cortical and subcortical regions compared with controls, demonstrating that even early stages of cognitive decline in PD are associated with a disruption in large-scale network coordination and decreased efficiency of parallel information processing (Pereira et al., 2015). Combining rs-fMRI with dopaminergic imaging, Lebedev et al. were able to link dopaminergic function with cognitive impairment (Lebedev et al., 2014). In this study, relative preservation of nigrostriatal dopaminergic function was associated with better executive function, increased dorsal fronto-parietal cortical processing, and inhibited subcortical and primary sensory involvement

Fig. 7 See legend on next page.

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(Lebedev et al., 2014). Similarly, patients with DLB exhibit prominent impairment in striato-cortical, but not meso-limbic pathways (Caminiti et al., 2017). Other non-motor symptoms in PD such as apathy also have been linked to alterations in fronto-striatal connectivity (Baggio et al., 2015). Decreased connectivity in the default mode network and central executive network, combined with increased connectivity in the salience network 36 months after initiating dopaminergic therapy is found in patients who develop impulse control disorders (Voon et al., 2017), compared to those who do not, providing another link between nigrostriatal dysfunction and striato-cortical circuits relating to cognition and behavior (Tessitore et al., 2017). Fig. 7 Parkinson’s disease cognitive network correlation with caudate dopaminergic function. (A) A single cluster was identified in a voxel-wise whole brain search of the FPCIT PET images for regions with significant correlations between DAT binding and PDCP activity. This region was localized to the left caudate nucleus (x ¼ 14, y ¼ 20, z ¼  2 mm; Zmax ¼ 4.06, P < 0.05, corrected). A smaller cluster (arrow) was identified in the right caudate nucleus at the less stringent hypothesis-testing threshold of P ¼ 0.005, uncorrected. [The display was superimposed onto a single-subject MRI brain template and thresholded at t ¼ 2.95, P < 0.005. L indicates the left cerebral hemisphere.] (B) Display of individual data from a spherical volume-of-interest centered at the peak voxel of the left caudate cluster (P < 0.001, top) and from a mirror volume placed at the same coordinates on the right cerebral hemisphere (P < 0.01, bottom). [The best fitting regression line for each correlation is depicted by a solid line; the 95% confidence intervals by broken curves.] (C) Hypothetical relationship between levodopa-mediated changes in cognitive performance, baseline PDCP expression, and caudate DAT binding. Based on the findings of Mattis et al. (2011) and Niethammer et al. (2013), we propose that an inverted U-shaped relationship exists between the changes in cognitive functioning observed during levodopa treatment (y-axis) and baseline measurements of caudate dopaminergic input (horizontal bar) and PDCP metabolic network activity (x-axis). The numbers at the top of the horizontal bar represent the caudate DAT binding values that correspond to PDCP scores of 1.0, 2.0, and 3.0, respectively. These estimates were based upon the best fitting linear relationship of the two measures (mean caudate DAT binding ¼ 0.94–0.15 ∗ PDCP score). The area under the curve associated with treatment-mediated cognitive benefit (yellow) was defined by an increase in individual subject test performance exceeding the independently determined Reliable Change Index (RCI) (Maassen, 2004) on the psychometric outcome measure. For verbal learning performance, the RCI cutoff (arrows) was determined to be 0.44, which is hypothesized to correspond to PDCP scores between +1.0 and +3.0 (Mattis et al., 2011). No cognitive benefit during treatment is expected for PDCP values on either side of this interval (gray). Panels (A)–(C): Reprinted from Niethammer, M., Tang, C. C., Ma, Y., Mattis, P. J., Ko, J. H., Dhawan, V., et al. (2013). Parkinson’s disease cognitive network correlates with caudate dopamine. Neuroimage, 78, 204–209. Copyright (2013), with permission from Elsevier.

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2.6 Treatment-Related Network Imaging While PDRP and PDCP expression can decrease with effective PD treatment such as levodopa or DBS (Asanuma et al., 2006; Jourdain et al., 2016; Pourfar et al., 2009), this effect is relatively modest and non-specific, and may thus not be sufficient for clinical trials. Especially smaller trials face a number of hurdles, where network imaging may prove to be beneficial. Accuracy of clinical diagnosis is important for any trial, and atypical parkinsonian disorders, while pathologically distinct, may be difficult to differentiate from PD, especially early in the disease course. Clinical trials that utilized dopaminergic imaging (FDOPA or dopamine transporter system imaging) have reported evidence that 10–15% of subjects that were clinically categorized as idiopathic PD lack findings of a dopaminergic deficit (Marek & Seibyl, 2003; Schwingenschuh et al., 2010; Whone et al., 2003), and even in a trial of advanced PD patients, a significant portion of patients probably did not have PD based on imaging criteria (LeWitt et al., 2011; Niethammer et al., 2017). Network approaches to the diagnosis of PD will be discussed in further detail below. Lastly, placebo effects can be a particularly prominent confounder in PD trials, with 16% of PD subjects randomized to placebo demonstrating a >50% improvement in UPDRS motor ratings in a review of placebocontrolled PD trials (Galpern et al., 2012). Network analysis opens the possibility to identifying subject that may be highly susceptible to placebo effect, and taking this into consideration during trial design and enrollment. Employing OrT/CVA (Habeck et al., 2005; Ko et al., 2014), we were able to identify and validate a specific metabolic brain network associated with the placebo response in PD patients enrolled in a AAV2-GAD gene therapy trial (LeWitt et al., 2011). This sham surgery-related pattern primarily involved anatomicalfunctional pathways linking the posterior cerebellar vermis to limbic cortex via the ventral anterior thalamus, amygdala, and caudate nucleus (Ko et al., 2014). Baseline network expression, which was measured prior to randomization, correlated with the motor response that was subsequently observed under blinded trial conditions in the sham group. Crucially, these network changes did not appear following the experimental subthalamic nucleus AAV2-GAD gene therapy, and they were reversed by unblinding. These results strongly suggest that a resting-state metabolic brain network underlies the placebo response in PD, though future studies will be needed to determine if this sham surgery-related pattern is universal to PD or unique to this trial.

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2.7 Abnormal Networks in Atypical Parkinsonian Disorders As mentioned above, the diagnosis of PD and parkinsonian disorders can be challenging, especially early in the disease course. Some of the cardinal features of PD, such as tremor, bradykinesia, and rigidity, can be present in patients with other degenerative (and some non-degenerative diseases). Among disease with nigrostriatal degeneration, the atypical parkinsonian syndromes (APS) encompass several specific diseases with distinct pathology and prognosis, including progressive supranuclear palsy (PSP), MSA, and corticobasal degeneration (CBD). The diagnosis of PD and APS is made based on clinical examination, relying on established consensus criteria (Armstrong et al., 2013; Gilman et al., 2008; Hoglinger et al., 2017; Postuma et al., 2015). Nevertheless, post-mortem studies show only 76% accuracy in the diagnosis of PD (Hughes, Daniel, Ben-Shlomo, & Lees, 2002). Though this accuracy does increase with longer follow-up and evaluations by movement disorders specialists, it remains significantly lower for atypical syndromes (Hughes et al., 2002; Josephs & Dickson, 2003). This uncertainty necessitates better diagnostic criteria, especially in the clinical trial setting when participant numbers may be small, or subjects early in the diseases are being enrolled. Conventional MR imaging is widely available, and potential diseasespecific findings have been described. In general, however, such imaging tends to be of low sensitivity with significant overlap between the syndromes (Holtbernd & Eidelberg, 2014). Dopaminergic imaging can distinguish between degenerative parkinsonian syndromes (including DLB) from those not involving nigrostriatal degeneration, but has low utility in distinguishing between PD and the various APS (Cummings et al., 2011; Vlaar, van Kroonenburgh, Kessels, & Weber, 2007). This has led to an increased interest in utilizing metabolic imaging in the differential diagnosis of parkinsonian disorders. Compared to structural and dopaminergic imaging, visual inspection of FDG PET scans by trained readers, aided by standard templates based on statistical parametric mapping (SPM) has been shown to have improved diagnostic accuracy in differentiating between PD and APS (Eckert et al., 2005; Hellwig et al., 2012). However, this approach is difficult to scale and does not yield quantifiable measures. Using the same approach described above for PD, disease-specific metabolic patterns have been identified and validated for PSP, MSA, and CBD (Eckert et al., 2008; Ge et al., 2018; Niethammer et al., 2014; Spetsieris, Ma, Dhawan, & Eidelberg, 2009). The MSA-related pattern

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(MSARP) was characterized by decreased metabolism in the putamen and cerebellum (Fig. 8A, left). The PSP-related pattern (PSPRP) was characterized by metabolic decreases in the brainstem and medial frontal cortex (Fig. 8B, left). The CBD-related pattern (CBDRP) was characterized by bilateral, asymmetric metabolic reductions involving frontal and parietal cortex, thalamus, and caudate nucleus. In accordance with the clinical picture of these disorders, MSARP and PSPRP were relatively symmetric, while in CDBRP, these pattern-related changes were greater in magnitude in the cerebral hemisphere opposite the more clinically affected body side (Fig. 8C, left). MSARP expression was significantly elevated in subject with MSA compared to those with PD or healthy controls. Moreover, in MSA patients, MSARP, but not PDRP expression was correlated with clinical severity and disease duration (Poston et al., 2012) (Fig. 8A, right). Clinically, MSA is further subdivided into cerebellar (MSA-C) and parkinsonian (MSA-P) subtypes. Remarkably, while both subgroups display some difference in regional metabolism, both had similar MSARP elevations, consistent with the notion that these covariance networks provide descriptors based on underlying disease pathology rather than specific clinical features (Poston et al., 2012). By nature of their derivation, disease-specific covariance networks can easily distinguish between patients and healthy controls. In order to automatically differentiate between different diseases, one might expect that multiple patterns would need to be utilized (Holtbernd & Eidelberg, 2014). To that end, Tang and colleagues developed a multiple-pattern imaging algorithm to calculate the probability that patients with uncertain clinical diagnosis at the time of imaging might have PD, MSA, or PSP (Tang, Poston, Eckert, et al., 2010). Briefly, the algorithm classified patient as having PD or APS if certain cutoff criteria for pattern expression was met, or as indeterminate parkinsonism if neither was met. A second level analysis was then used to differentiate those subjects with a diagnosis of APS into MSA or PSP. When compared to the final clinical diagnosis on follow-up, this method was able to correctly classify these subjects with high sensitivity (PD: 84%, MSA: 85%, PSP: 88%), specificity (PD: 97%, MSA: 96%, PSP: 94%), and high positive predictive values (PPV) (PD: 98%, MSA: 97%, PSP: 91%). This approach was recently validated in an independent cohort of 129 parkinsonian patients with uncertain diagnosis (Tripathi et al., 2016, 2012) (Fig. 9A). Sixty percent of patients had disease duration of under 2 years.

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Fig. 8 Abnormal networks in atypical parkinsonian disorders. (A) Left: Multiple system atrophy-related metabolic pattern (MSARP) identified by spatial covariance analysis of FDG PET scans from 10 MSA patients and 10 healthy volunteer subjects. This pattern was characterized by reduced metabolic activity (blue) in the putamen and the cerebellum (Eckert et al., 2008; Poston et al., 2012). Right: In MSA patients, increased MSARP expression (red circles) correlated with greater clinical disability as measured by increased Unified Parkinson’s Disease Rating Scale (UPDRS) motor ratings (r ¼ 0.57, P < 0.001). In contrast, increased motor disability in these subjects was associated with lower PDRP expression values (blue triangles; r ¼ 0.53, P < 0.002). (B) Left: Progressive supranuclear palsy-related metabolic pattern (PSPRP), identified by spatial covariance analysis of FDG PET scans from 10 PSP patients and 10 healthy volunteer subjects, was characterized by reduced metabolic activity (blue) in the medial prefrontal cortex (PFC), the (Continued)

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Fig. 8—cont’d frontal eye fields, the ventrolateral prefrontal cortex, the caudate nuclei, the medial thalamus, and the upper brainstem (Eckert et al., 2008). The display of the MSARP and PSPRP covariance maps was thresholded at Z ¼ 3.61, P < 0.001 and overlaid on T1-weighted magnetic resonance template images. Right: One-way ANOVA showed significant effect of group (F(7,186) ¼ 18.741, P < 0.001). PSPRP score was significantly higher in PSP patients compared all other groups (P < 0.001; post-hoc Bonferroni test) except corticobasal syndrome (CBS) (P ¼ 1.0). *Significantly different from normal controls (P < 0.05; post-hoc Bonferroni test). $Significantly different from PSP (P < 0.05; post-hoc Bonferroni test). (C) Left: Corticobasal degeneration-related pattern (CBDRP) identified by spatial covariance analysis of FDG PET scans from a derivation cohort of 10 patients with CBD and 10 normal control (NL) subjects scanned at the North Shore University Hospital (eight CBD and 10 control subjects) and Stanford University (two CBD subjects). This pattern was characterized by metabolic reductions in the left frontal and parietal lobes, precentral gyrus, thalamus, and caudate head, associated with increased metabolism in the left occipital lobe, left lingual gyrus, right occipital lobe and right inferior occipital gyrus. [The display represents regions that contributed significantly to the network at Z ¼ 2.33 (P < 0.01) and were demonstrated to be reliable (P ¼ 0.01; 1000 iterations) by bootstrap resampling. Voxels with positive region weights (metabolic increases) are color-coded red and those with negative region weights (metabolic decreases) are color-coded blue. Left hemisphere is labeled as “L.”] Right: CBDRP asymmetry index: The hemi-CBDRP topography was defined by the left hemisphere of the original whole brain CBDRP. As the side opposite the more affected limbs in patients with CBD, the left hemisphere contained the bulk of the local metabolic reductions that constitute this disease topography (Niethammer et al., 2014). The expression of the left hemi-CBDRP was separately computed in the two hemispheres of each subject. The left-right difference in these values was used to compute a CBDRP asymmetry index for each subject. One-way ANOVA showed significant effect of group (F(7, 186) ¼ 9.373, P < 0.001). Only CBD patients showed significantly increased CBDRP asymmetry index compared to NL (P < 0.001; post-hoc Bonferroni test) while all other disease groups showed significantly lower asymmetry index compared to the CBS patients (P < 0.013). *Significantly different from NL (P < 0.05; post-hoc Bonferroni test). $Significantly different from CBS (P < 0.05; post-hoc Bonferroni test). Panel (A): Adapted from Eckert, T., Tang, C., Ma, Y., Brown, N., Lin, T., Frucht, S., et al. (2008). Abnormal metabolic networks in atypical parkinsonism. Movement Disorders, 23(5), 727–733. Copyright (2008), with permission from John Wiley and Sons; Reprinted with permission from Poston, K. L., Tang, C. C., Eckert, T., Dhawan, V., Frucht, S., Vonsattel, J. P., et al. (2012). Network correlates of disease severity in multiple system atrophy. Neurology, 78(16), 1237–1244; Panel (B): Adapted from Eckert, T., Tang, C., Ma, Y., Brown, N., Lin, T., Frucht, S., et al. (2008). Abnormal metabolic networks in atypical parkinsonism. Movement Disorders, 23(5), 727–733. Copyright (2008), with permission from John Wiley and Sons; Reprinted from Ko, J. H., Lee, C. S., & Eidelberg, D. (2017). Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations. Journal of Cerebral Blood Flow Metabolism, 37(2), 683–693; Panel (C): From Niethammer, M., Tang, C. C., Feigin, A., Allen, P. J., Heinen, L., Hellwig, S., et al. (2014). A disease-specific metabolic brain network associated with corticobasal degeneration. Brain, 137(11), 3036–3046, by permission of Oxford University Press; Reprinted from Ko, J. H., Lee, C. S., & Eidelberg, D. (2017). Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations. Journal of Cerebral Blood Flow Metabolism, 37(2), 683–693.

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Fig. 9 Using network analysis for differential diagnosis. (A) Three-dimensional plot of pattern expression. Expression values for PDRP (x-axis), MSARP (y-axis), and PSPRP (z-axis) topographies are shown for Indian validation cohort. This group comprised 129 parkinsonian patients with uncertain clinical diagnosis. On the basis of serial clinical examinations by movement disorders specialists blind to scan data, 81 of these subjects were subsequently diagnosed with idiopathic PD (IPD), and 48 subjects with APS (20 with MSA and 28 with PSP). Pattern expression values were computed in FDG PET scans from these subjects acquired 2.2  0.4 years (mean  SD) before final clinical diagnosis. (B) Predicted disease probabilities for differential diagnosis of patients with uncertain parkinsonism in the whole parkinsonian sample. Left: Frequency distribution of predicted probabilities for IPD (PIPD, top row of x-axis) and APS (PAPS, bottom row of x-axis). Right and left dashed lines, respectively, denote cutoff probabilities for IPD (PIPD ¼ 0.81) and APS (PAPS ¼ 0.79) determined in a previous study (Tang, Poston, Eckert, et al., 2010). Subjects falling between two dashed lines were categorized as indeterminate. Right: Display of ROC curves for IPD (red) and APS (black). AUC was high (0.95, P < 0.0001), denoting excellent diagnostic accuracy for the two conditions based on a logistic discrimination function identified by Tang, Poston, Eckert, et al. (2010). Panels (A) and (B): Reprinted from Tripathi, M., Tang, C. C., Feigin, A., DeLucia, I., Nazem, A., Dhawan, V., et al. (2016). Automated differential diagnosis of early parkinsonism using metabolic brain networks: A validation study. Journal of Nuclear Medicine, 57(1), 60–66.

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Nevertheless, idiopathic PD subjects were distinguished from APS with 94% specificity and 96% PPV. The algorithm achieved 90% specificity and 85% PPV for MSA, and 94% specificity and 94% PPV for PSP. Importantly, diagnostic accuracy was similarly high (specificity and PPV > 90%) for parkinsonian subjects with short symptom duration, underlining the possible utility of this approach early in the disease course. Based on a small study, a similar approach can be used to distinguish patients with CBD from those with PSP (Niethammer et al., 2014). However, an additional step was required in this case. While PDRP and MSARP values are elevated predominantly in their respective disease, it is worth noting that CBDRP and PSPRP are both abnormally elevated in both CBD and PSP cohorts (Ko, Lee, et al., 2017; Niethammer et al., 2014). In order to differentiate the two diseases, the algorithm used expression of PSPRP, as well as an index of asymmetry in CBDRP expression (Fig. 8B and C, right). CBD and PSP are clinically and pathologically thought to be distinct, but both are classified as tauopathies and share deposition of four repeat tau. Perhaps abnormally elevated expression of both patterns in PSP and CBD is also a consequence of regional overlap in their respective neuropathological topographies. In contrast, preliminary results did not find elevated CBDRP expression with patients with AD, which has a different tau pathology. To date, CBDRP has not been incorporated into the larger algorithm for PD and APS, and it is not clear which (if any) pattern would be elevated in patients with pathologically proven CBD who have a different clinical syndrome in life (Boeve, 2011; Hassan, Whitwell, & Josephs, 2011; Hu et al., 2009; Litvan et al., 1997). Other automated methods for the differential diagnosis of PD and APS have been reported, such as relevance vector machine analysis (Garraux et al., 2013) and decision trees applied to covariance patterns (Mudali, Teune, Renken, Leenders, & Roerdink, 2015) with promising results. Prospective validation studies, preferably comparing multiple methods in larger cohorts, will be needed before the relative utility of these methods can be determined.

3. NETWORK ANALYSIS IN OTHER MOVEMENT DISORDERS While the majority of network analysis research has focused on PD and related disorders, some of these methods have been applied to other neurological conditions. We have already alluded to AD-related patterns above. Fundamentally, in AD, derivation of the patterns has used the same

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approach, and ADRP expression can be related to clinical symptoms. Longitudinal studies are lacking at this time. In this section, we will describe the use of network analysis in Huntington’s disease, highlighting some additional applications.

3.1 Huntington’s Disease Huntington’s disease (HD) is a progressive neurodegenerative disorder with progressive decline in motor, cognitive, and behavioral function. HD is inherited in autosomal dominant fashion, with nearly 100% penetrance. Moreover, at-risk subjects can be tested for the length of CAG expansion, which bear a relationship to age of disease onset. The ability to test for the mutation affords the possibility of using brain imaging to identify a potential progression biomarker in carriers of this mutation prior to the disease onset (premanifest HD). Such imaging could then be used in trials of diseasemodifying agents, even prior to having clinical measures to rely on. Factors such as the CAG repeat length and age can be modeled to fit progression of structural imaging, though this has not been directly applied to clinical progression (Warner & Sampaio, 2016) in patient cohorts. Using functional imaging, Feigin et al. reported a unique spatial covariance metabolic pattern related to HD, with elevated expression in premanifest carriers compared to controls, and further elevations in the symptomatic group (Feigin, Leenders, et al., 2001). This pattern, termed HD-related pattern (HDRP), was characterized by reductions in striatal and anterior cingulate metabolic activity, associated with relative metabolic increases in the ventrolateral/ventral posterolateral thalamus, cerebellar vermis, and in the primary motor and visual regions of the cerebral cortex. The same investigators then followed 12 of the premanifest subjects with repeat FDG and raclopride PET scanning over 44 months (Feigin, Tang, et al., 2007). Unlike striatal D2 receptor binding, which declines during both premanifest and symptomatic stages of HD, over time, HDRP expression initially increased during the premanifest stage before decreasing as patients approached and reached the symptomatic stage (Feigin, Tang, et al., 2007). Thus, while HDRP is abnormally elevated in premanifest and manifest HD, it is only of limited use in the study of disease progression. In order to overcome this drawback, Tang et al. expanded the network approach by using OrT/CVA to longitudinal metabolic imaging to derive a distinct and significant spatial covariance pattern that was associated with disease progression (Tang et al., 2013) (Fig. 10A). Expression of this HD progression network increased linearly over 7 years and, crucially, was not

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Fig. 10 Network analysis in Huntington’s disease (HD). (A) HD metabolic progression pattern. This spatial covariance pattern was characterized by areas of declining (blue) and increasing (red) metabolic activity over time. The pattern is displayed as a reliability map of voxel weights thresholded at z ¼ 2.33, P < 0.01 (one-tailed), using a bootstrap resampling procedure (ICV range ¼  6.02, 5.63, P < 0.0001; 1000 iterations). (B) All premanifest subjects exhibited a monotonic increase in pattern expression (P < 0.001; permutation test) across the first three time points. (C) In this longitudinal cohort, pattern expression increased linearly with disease progression (P < 0.0001; individual growth modeling—IGM) at an estimated progression rate of 0.21/year (95% CI ¼ 0.15, 0.27). Values from the five early symptomatic members of the HD2 testing cohort (yellow triangles) are provided for reference. (D) In an independent longitudinal testing cohort,

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influenced by intercurrent phenoconversion (Fig. 10B–D). Increasing activity of the network progressed at approximately twice the rate of single region measurements from the same subjects, and at faster rates than a similarly derived pattern related to progressive volume loss or the decline in dopaminergic function (Fig. 10E). Thus, such metabolic network measurements progression may provide a highly sensitive means of quantitatively evaluating HD disease progression (Tang et al., 2013). In addition to the ability to measure progression prior to disease onset, accurate prediction of conversion to symptomatic HD would be beneficial for clinical trials. Traditionally, a model using CAG repeat length and age has been used (Langbehn, Brinkman, Falush, Paulsen, & Hayden, 2004), and pattern expression exhibited a similar linear increase with advancing disease (P < 0.0001; IGM) at a nearly identical rate of 0.19/year (95% CI ¼ 0.11, 0.26). The longitudinal data from each subject are connected by lines. In (A) and (B), red lines denote the initially premanifest subjects who subsequently phenoconverted (i.e., were clinically diagnosed as HD); blue lines denote their counterparts who did not phenoconvert during the study. Values before and after phenoconversion are represented by open and filled symbols. The horizontal broken line represents the mean (equal to 0) of the healthy control group; the dotted lines represent 2 SD above and below the normal mean. In (C) and (D), the solid lines represent the best model-fitted lines with a 95% CI (broken curves). (E) Time course of disease progression: network vs. regional biomarkers. Linear trajectories (solid lines) of the network and regional imaging measures for the premanifest longitudinal cohort according to the best fitting models. The rate of increase in the HD metabolic pattern expression (red) was greater than that for the volume-loss progression pattern (orange; P < 0.05, IGM) and the rates of decline measured for caudate D2 receptor binding (light blue; P < 0.0001) and tissue volume (dark blue; P < 0.0005). To allow for direct comparison of network progression (increasing time course) with corresponding changes in regional measures (decreasing time course), the values for caudate D2 receptor binding and tissue volume were flipped and analyzed as increasing mirror lines (dotted lines). The y-axis represents the standard z-scale. The horizontal dotted line represents the normal mean (equal to 0) for each parameter. The vertical dotted line represents the time of phenoconversion (i.e., years-to-onset was zero). The estimated value for the metabolic progression pattern at phenoconversion (i.e., the y-axis intercept) is signified by a red arrow. The estimated “start time” for the decline of caudate D2 receptor binding (i.e., the x-axis intercept) is signified by a light blue arrow. Inset: Bubble plots depicting the estimated rates of disease progression and values at phenoconversion for the HD metabolic and volume-loss patterns (red and orange discs) and for caudate D2 receptor binding and tissue volume (light and dark blue discs). The diameter of each disc is proportional to the standard error for each parameter estimate. Panels (A)–(E): Republished with permission of American Society for Clinical Investigation, from Tang, C. C., Feigin, A., Ma, Y., Habeck, C., Paulsen, J. S., Leenders, K. L., et al. (2013). Metabolic network as a progression biomarker of premanifest Huntington’s disease. Journal of Clinical Investigation, 123(9); permission conveyed through Copyright Clearance Center, Inc.

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accuracy of prediction can be improved by incorporating clinical measures and structural imaging (Paulsen et al., 2014). More recently, Mason et al. (2018) combined structural imaging with resting-state network coupling in a machine learning model to predict phenoconversion within 5 years. This model showed improved sensitivity compared to the Langbehn model in predicting conversion (Mason et al., 2018).

4. CONCLUSION The potential for metabolic network biomarkers in the evaluation of neurological diseases is being increasingly appreciated through multiple, convergent studies. The adoption of these techniques into clinical practice and clinical trials is only beginning depending on considerations such as the availability and cost-benefit ratio of functional imaging procedures. Some reluctance to integrate PET into clinical practice is due to limited availability of the technique, its relative invasiveness, and the need for radiation exposure. Recent technical advances in PET have largely obviated these concerns, and this increasingly standardized procedure is beginning to be more widely available in Asia, as well as Europe and North America. Functional network imaging using PET has thus become a realistic option for trials of new therapies for neurological disorders. Nevertheless, before this approach is broadly approved by regulatory bodies, more blinded diagnostic studies are likely needed. Additionally, recent research has demonstrated the potential of this approach in the more broadly available MRI environment. Even so, this work is at early stages and restricted to PD. More studies are needed to optimize acquisition parameters for the identification of disease-related patterns in PD and other diseases, including those for APS as well as other neurodegenerative disorders.

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