Curr Neurol Neurosci Rep (2014) 14:448 DOI 10.1007/s11910-014-0448-6

MOVEMENT DISORDERS (M OKUN, SECTION EDITOR)

Resting State Functional Magnetic Resonance Imaging in Parkinson’s Disease Janey Prodoehl & Roxana G. Burciu & David E. Vaillancourt

# Springer Science+Business Media New York 2014

Abstract Neuroimaging advances over the past several decades have provided increased understanding of the structural and functional brain changes that occur with Parkinson’s disease (PD). Examination of resting state functional magnetic resonance imaging (rs-fMRI) provides a noninvasive method that focuses on low-frequency spontaneous fluctuations in the blood-oxygenation-level-dependent signal that occurs when an individual is at rest. Several analysis methods have been developed and used to explore how PD affects resting state activity and functional connectivity, and the purpose of this review is to highlight the critical advances made thus far. Some discrepancies in the rs-fMRI and PD literature exist, and we make recommendations for consideration in future studies. The rs-fMRI technique holds promise for investigating brain changes associated with the motor and nonmotor symptoms of PD, and for revealing important variations across large-scale networks of the brain in PD.

This article is part of the Topical Collection on Movement Disorders J. Prodoehl Physical Therapy Program, Midwestern University, 555 31st Street, Downers Grove, IL, USA R. G. Burciu : D. E. Vaillancourt (*) Department of Applied Physiology and Kinesiology, University of Florida, PO Box 118205, Gainesville, FL, USA e-mail: [email protected] D. E. Vaillancourt Department of Neurology and Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, USA D. E. Vaillancourt Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

Keywords fMRI . Resting . Connectivity . Network . Parkinson’s disease

Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms which emerge after an estimated 50 % or more of dopaminergic cells in the substantia nigra pars compacta (SNc) have been lost [1,2]. This loss of dopaminergic neurons is thought to lead to widespread changes throughout the cortico-striatal network and beyond. The clinical course of the disease involves a preclinical phase which may extend over many years, and a clinical phase characterized by progressively increasing motor and nonmotor symptoms leading to increasing functional limitations and disability. Neuroimaging advances over the past several decades have provided increased understanding of the structural and functional brain changes that occur with PD. Several neuroimaging techniques have been developed for examining task-independent changes in the resting neural state in patients with neurological disorders. These techniques include functional magnetic resonance imaging (fMRI), positron emission tomography, and single-photon-emission computed tomography. In contrast to positron emission tomography and single-photon-emission computed tomography, resting state (or “task-free”) fMRI (rs-fMRI) provides a noninvasive method of assessing changes in resting state functional connectivity across brain regions in patients with PD [3]. Resting state connectivity fMRI focuses on low-frequency (i.e., below 0.1 Hz) spontaneous fluctuations in the bloodoxygenation-level-dependent (BOLD) signal that occur when an individual is at rest [4]. Biswal et al. [3] were the first to report a synchrony of low-frequency fluctuations in the BOLD signal arising from the right and left primary motor cortices at rest, leading to a connectivity pattern that closely

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resembled the activation pattern obtained from a bilateral finger-tapping task. An abnormal pattern of resting activity and disrupted connectivity in different neural networks across the brain may explain some of the motor and nonmotor deficits seen in patients with PD [5]. The purpose of this review is (1) to describe how our understanding of abnormalities in task-free BOLD signal activity detected through rs-fMRI has evolved in PD, and (2) to describe how information from multiple methods using rs-fMRI has converged to advance our understanding of the motor and nonmotor deficits associated with PD.

Background to Methodological Approaches to Assessing BOLD Signal Changes Using rs-fMRI Different methodological approaches have been used to assess resting state BOLD signal changes in the human brain, and different terminology has emerged on the basis of the analysis approach used. Functional connectivity seeks to describe “synchrony” between and within brain regions, whereas effective connectivity examines the strength and directionality of information flow within a brain network. Functional connectivity in PD was first examined using a seed-based voxelwise analysis approach to detect brain regions that are functionally connected in the absence of a task (Fig. 1). Typically, a single “seed” is placed in a specific region of interest (ROI) [3]. Spontaneous fluctuations of the BOLD signal from the seed region are then correlated with BOLD signal fluctuations from all other voxels in the resting brain to create a resting state connectivity map. This method is a suitable tool for testing a priori assumptions. Hierarchical clustering has been used to expand seed-based analyses by using a correlation matrix developed from the use of multiple seeds to determine which regions are most closely related [6]. An advantage of seed-based approaches to analyzing functional connectivity is that the results are focused on specific ROIs and may therefore be easier to interpret in relation to neuroanatomy. For patients with PD, these ROIs typically include structures within the cortico-striatal network which may be related to the motor symptoms of PD, although there is increasing attention on other networks that may help us to understand the nonmotor features of the disease. An alternative to seed-based analyses is the use of datadriven methods which consider activation across all voxels in the brain at the same time. Graph theory is an example of a mathematical model of network connectivity that measures both global and local connectivity between brain regions (Fig. 1) [7]. The outcome of this analysis is a set of nodes that represent brain regions which are spatially and functionally interconnected. Independent component analysis (ICA) is a network-based approach that decomposes the resting state

Fig. 1 Numerous methods have been developed and used to study resting state functional connectivity and within-voxel oscillations in Parkinson’s disease. This figure highlights the approach taken across each analytic technique. These techniques provide a different viewpoint of resting state oscillations in the human brain. BOLD blood oxygenation level dependent, ROI region of interest

BOLD time series into separate source signals [8]. On the basis of the concept that sources of resting state signals across the brain should be statistically independent, each component is hypothesized to represent an independent network of similar BOLD activity. These analyses are not limited by a priori hypotheses, and can allow the detection and removal of physiological noise from the resting state signal. Another approach used to assess functional connectivity in patients with movement disorders is regional homogeneity (ReHo) analysis [9]. ReHo analysis measures the similarity of the time series of one voxel with those of its nearest neighbors in a voxelwise manner. Unlike ICA, ReHo analysis focuses on shortdistance functional connectivity. These resting state analyses do not provide any information on the amplitude of brain activity in a region within the network or its location. To address this, an analysis of the amplitude of low-frequency fluctuations (ALFF) has been developed. The ALFF is an index of the rs-fMRI signal calculated as the sum of amplitudes within a specific low-frequency range [10]. Whereas all

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of these techniques examine the temporal similarity or pattern of neural activity (i.e., functional connectivity), Granger causality analysis (GCA) has been developed to assess the directionality of information flow, by assuming that activity in one region causes activity in another region [11]. This has been termed relative or effective connectivity. Given the variety of analytical approaches to exploring neural activity in the resting state, comparing results across rs-fMRI studies in PD can be challenging. Since PD is a progressive neurodegenerative disorder which is responsive to dopaminergic medication, comparison between studies is further complicated by differences in the disease severity of patients studied and whether patients are scanned when they are taking or when they are not taking dopaminergic medication. In the next section, we review the findings from rs-fMRI studies in PD patients broken down by the method used, with attention to the stage of the disease and the medication state of the patients studied.

Network Approach to Assessing Functional Connectivity in PD The default mode network (DMN) is a baseline network of brain activity that has been well described in the healthy brain. The DMN includes the medial prefrontal cortex, the inferior parietal lobule (IPL), the hippocampus, and the posteromedial and temporal cortices [12,13]. Changes in the DMN have been shown to occur with healthy aging [14] as well as with various neurodegenerative and psychiatric diseases such as Alzheimer’s disease [15] and depression [16]. Our understanding of DMN dysfunction in PD is less clear. Krajcovicova et al. [17] examined the DMN in cognitively intact patients with PD while they were on dopaminergic medication and compared its resting activity with that of healthy controls. The results showed no differences in DMN integrity between controls and PD patients. In a similar group of patients with PD tested when they were taking medication, Tessitore et al. [18] found decreased resting state functional connectivity of the right medial temporal lobe and bilateral inferior parietal cortex within the DMN compared with controls. Since dopaminergic medication has been shown to affect task-related changes in the DMN in PD [11], it is possible that testing patients when they are taking medication may have contributed to these differences in findings. Shine et al. [19] found increased connectivity between the dorsal anterior cingulate cortex and the contralateral anterior IPL of the DMN in patients who did not exhibit hallucination symptoms compared with those who did. However, no control group was studied. Using a nonnetwork seed-based region-to-region analysis, Gorges et al. [20] examined changes in the resting DMN in medicated patients with PD and showed decreased region-to-region functional connectivity between the medial

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prefrontal cortex and the posterior cingulate cortex as well as increased connectivity between bilateral hippocampus in PD patients compared with controls. Taken together, these results generally suggest dysfunction of the DMN in PD, although examination of patients when they are not taking medication might help to unify the pattern of findings. In addition to the DMN, several other resting state networks have been examined, including the fronto-parietal network, the sensorimotor network, and the visual and auditory networks [21,22]. Tessitore et al. [23] used ICA to compare resting state connectivity among patients with PD who exhibited freezing of gait symptoms, patients with PD who did not exhibit freezing of gait, and healthy controls. Patients who exhibited freezing of gait had reduced connectivity in specific areas within the right fronto-parietal network (a network believed to be an executive-attention network) and within the visual network. The clinical severity of freezing of gait was significantly correlated with decreased connectivity in these two networks, suggesting that changes in specific resting networks may be relevant to specific PD symptoms. Specific changes have also been shown in the sensorimotor network of patients with PD. Wu et al. [5] were among the first to use graph theory to examine the resting motor network in patients with mild to moderate PD tested when both taking and not taking medication. Patients also performed a fingertapping task to identify ROIs for the resting state motor network analyses. At rest, nonmedicated PD patients showed significantly decreased functional connectivity in the supplementary motor area (SMA), left dorsolateral prefrontal cortex (DLPFC), and left putamen, and significantly increased connectivity in the left cerebellum, left M1, and left parietal cortex compared with controls. When comparing the with- and without-medication states in PD, significantly increased connectivity in the left DLPFC but decreased connectivity in the right parietal cortex, right primary motor cortex (M1), and left cerebellum was found in the with-medication state. Moreover, there were significant negative correlations in the SMA, bilateral putamen, left thalamus, bilateral premotor area, and left parietal cortex with the without-medication motor Unified Parkinson’s Disease Rating Scale (mUPDRS) score. Considering the basal ganglia, the correlation results suggest that resting activity of the putamen may play a significant role in the motor abnormalities in PD. Göttlich et al. [24] used graph theory to examine connectivity across the whole brain in patients with PD. Compared with controls, medicated PD patients showed decreased connectivity in the caudate, orbitofrontal, and occipital regions, and increased connectivity of sensorimotor and parietal areas. These results suggest an increased connectivity in the sensorimotor network and decreased connectivity within the visual network in medicated PD patients. Using a different method (i.e., ICA), Esposito et al. [25] found similar findings of increased connectivity in the sensorimotor network in PD

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patients taking antiparkinsonian medication. In addition, they found that immediate levodopa administration in de novo PD led to a significantly enhanced functional connectivity in the SMA that points to a specificity of action of levodopa. Regardless of the methods used, increased connectivity in the sensorimotor network in PD patients tested when taking antiparkinsonian medication appears to be a consistent finding.

Seed-Based Approaches to Assessing Functional Connectivity in PD Given that PD is characterized by dopamine depletion in the striatum, several rs-fMRI studies have used seed-based approaches to examine resting state connectivity related to the striatum. Using seeds placed in the putamen, caudate, and SMA, Yu et al. [26] found enhanced connectivity between the putamen and the SMA in patients with mild to moderate PD in the without-medication state, but did not find increased connectivity between the caudate and the SMA. Helmich et al. [27] also used seeds in the caudate but further subdivided the putamen into anterior and posterior parts, and examined the temporal coupling between rs-fMRI BOLD signal changes across the brain in patients with mild to moderate PD and healthy control subjects. Between-group differences were specific to the putamen, with decreased connectivity between the posterior putamen and specific cortical somatosensory and motor regions compared with an increased connectivity between the anterior putamen and selected cortical regions in PD. The results were consistent with the dopamine loss theory: a more pronounced loss in the dorsal and posterior striatum in PD [28,29]. Kwak et al. [30] used a similar seed-based approach to examine the effects of dopaminergic medication on resting state connectivity in patients with mild to moderate PD using six striatal seeds. The connectivity maps in patients were similar to those in controls. Consistent with the results of Helmich et al. [27], there was an increased cortico-striatal connectivity which was more prominent in the dorsal putamen seeds. Connectivity decreased with medication. Different connectivity patterns, however, emerge in de novo PD patients who are given an immediate dose of levodopa. Esposito et al. [25] found that immediate levodopa administration in de novo PD led to a significantly enhanced functional connectivity in the SMA when they examined the sensorimotor network. These results highlight the importance of considering the medication state and the disease stage when comparing results across rs-fMRI studies in PD. Examining patients in different stages of the disease may provide insight into how changes in functional connectivity evolve. Luo et al. [31] examined resting state striatal functional connectivity changes in early-stage drug-naïve PD patients

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using seeds in bilateral anterior and posterior putamen and caudate. Compared with controls, there was reduced connectivity in mesolimbic–striatal and cortico-striatal loops in PD. Although the caudate connectivity pattern was relatively spared, posterior putaminal connectivity changes were pronounced and extended to include the sensorimotor cortex. Despite using methods and seed regions similar to those used by Helmich et al. [27], Luo et al. [31] found that the patients in their study did not exhibit any increased functional connectivity in cortico-striatal loops. Patient selection might explain the differences between results. This suggests the possibility that changes in the striatum in the early stages of the disease process in PD are limited to the posterior putamen, with relative sparing of the anterior putamen and no evidence of compensatory changes in connectivity with the sensorimotor cortex. Testing patients in more advanced stages of the disease process is challenging especially because of tremor. Hacker et al. [32] used rs-fMRI to examine striatal connectivity in patients with advanced PD compared with healthy controls and, although likely related to the advanced stage of the disease, patients were tested when they were taking their dopaminergic medication. Seeds were placed in the bilateral caudate, anterior putamen, and posterior putamen. The findings revealed weaker striatal connectivity in PD with the thalamus, midbrain, pons, and cerebellum, and an increased connectivity with specific parts of the motor cortex. Baudrexel et al. [33] were interested in examining connectivity between the subthalamic nucleus (STN) and the motor cortex in patients with relatively early stage PD who were not taking medication compared with healthy controls. They found increased resting state connectivity in patients with PD between the STN and cortical motor areas. Comparing a subgroup of patients with tremor with a subgroup of patients without tremor revealed abnormal coupling between specific areas of the primary sensorimotor cortex. Wu et al. [34] extended this to consider specific motor deficits in PD. They placed seeds in the pre-SMA and bilateral M1 in an attempt to explain motor planning deficits (pre-SMA) and motor execution deficits (M1) in PD. The results showed stronger connectivity in PD between the pre-SMA and M1 but weaker connectivity between the pre-SMA and putamen, insula, premotor cortex, and IPL compared with controls. These findings are consistent with deficits in networks supporting movement planning and initiation in PD. Further attempting to relate functional connectivity in the resting state with different PD phenotypes, Liu et al. [35] compared resting state functional connectivity within the cerebello-thalamo-cortical loop in patients with mild to moderate PD who exhibited an akinetic/rigid presentation with connectivity in patients with a tremor-dominant presentation. Seeds were placed bilaterally in the dentate nucleus. When comparing both patient groups with healthy controls, they found increased connectivity between the dentate and the

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cerebellar posterior lobe in the patient groups, consistent with enhanced local cerebellar connectivity in PD. A betweenpatient-group comparison revealed decreased connectivity between the dentate and the cerebellar posterior lobe in the tremor-dominant PD group compared with the akinetic/rigid PD group, although there were no significant correlations between connectivity and disease severity as measured by the mUPDRS. The authors suggested that increased local cerebellar connectivity in PD may be a compensatory mechanism for disrupted cortical and subcortical connectivity within the cerebello-thalamo-cortical loop. Sharman et al. [36] used a seed-driven hierarchical model to examine sensorimotor connectivity between defined ROIs in patients with PD and healthy controls. Although PD patients abstained from medication on the day of testing, it is not clear if they completed a 12-h withdrawal, which is considered the standard procedure for testing in the without-medication state in PD. Their results showed reduced connectivity between the sensorimotor cortex and the thalamus, between the globus pallidus and both the putamen and the thalamus, and between the substantia nigra and the globus pallidus, putamen, and thalamus in patients with PD. Increased functional connectivity was found for nonsensorimotor connections such as that between the putamen and associative cortex. However, there was no significant relation between the mUPDRS score, the Hoehn and Yahr score, or disease duration with connectivity values for the disease-affected and contralateral hemispheres, suggesting that resting state connectivity using the approach of Sharman et al. does not significantly drive motor symptoms.

Regional Approaches to Assessing Functional Connectivity in PD ReHo analysis examines local synchronization of spontaneous BOLD signals by calculating the similarity of voxel fluctuations within a given cluster [37]. Wu et al. [38] were the first to use ReHo analysis to assess ReHo in PD. They showed that in PD patients not taking medication compared with controls, ReHo was generally decreased in the putamen, thalamus, and SMA, and increased in the cerebellum, primary sensorimotor cortex, and premotor areas. These changes were normalized by administration of levodopa. There was a negative correlation between the without-medication mUPDRS score and ReHo in several areas, including the bilateral putamen, and a positive correlation between the mUPDRS score and ReHo in bilateral cerebellum and left lingual gyrus. Two additional studies [39,40] have since examined ReHo changes in less affected PD patients tested when not taking medication. The most consistent findings between these studies are reduced ReHo in the putamen and increased ReHo in the cerebellum, medial frontal gyrus, and middle temporal gyrus in PD

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patients compared with controls. However, these studies showed opposite changes in ReHo in PD patients versus controls in several other areas, including the IPL. Such discrepancies point to the need for further studies that could inform our understanding of how ReHo is affected by dopaminergic medication and disease severity in PD.

Spontaneous Neural Oscillations Approach to Assessing Functional Connectivity in PD Rather than assessing connectivity between brain regions, analysis of the ALFF is thought to provide insight into changes in neural activity in specific brain regions indicating how much BOLD signal oscillation there is in a given region at rest. For example, Skidmore et al. [41] showed differences in the regional pattern of the ALFF between patients with PD who presented with high apathy clinical ratings and those who presented with higher depression clinical ratings [41]. Whereas disease severity, as measured by the mUPDRS was best predicted by the ALFF in the right putamen, apathy score was best predicted by the ALFF in the left SMA, right orbitofrontal cortex, and right middle frontal cortex. Depression score, on the other hand, was best predicted by the ALFF in the right subgenual cingulate. Kwak et al. [42] used the ALFF to investigate the effects of dopaminergic medication in PD patients on spontaneous neural oscillations. When not taking medication, PD patients showed abnormally elevated ALFF in primary and secondary motor areas, as well as in prefrontal cortical areas. Immediate administration of dopaminergic medication reduced but did not normalize these abnormally high BOLD signal oscillations in PD patients. In contrast, Skidmore et al. [43] found that patients not taking medication showed reduced ALFF in the SMA, mesial frontal regions, middle frontal gyrus, and the left inferior cerebellum. The only area to show increased ALFF in PD was the right superior cerebellum. Methodological specificities (e.g., physiological noise correction) might account for the differences in connectivity results between these studies.

Effective Connectivity in PD Effective connectivity is used to infer directionality of neural interactions to examine causal connections. Wu et al. [44] were the first to examine connectivity using GCA, focusing on effective connectivity of the SNc in healthy controls and patients with mild to moderate PD. They demonstrated a positive influence of the SNc on the contralateral SNc, medulla, pons, bilateral putamen, caudate, internal and external globus pallidus, thalamus, STN, insula, temporal cortex, and cerebellum in both patients and controls. However, whereas

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SNc activity in control subjects predicted increased activity in the SMA, DMN and DLPFC, in patients with PD, activity in the SNc predicted decreased activity in these same structures. Also, as the mUPDRS score increased, the influence of the SNc on these regions decreased. Ghasemi and Mahloojifar [45] extended the examination of effective connectivity in PD by using a multivariate GCA within the motor network, including the cerebellum. They showed PD patients had weaker causal connectivity between the cerebellum and the caudate as well as disorganized symmetric connections and smaller flow of information in the motor network in general. The results also suggest that the putamen is more influenced by other regions in PD patients than in controls. In a very recent study, Kahan et al. [46] used dynamic causal modeling to examine the effects of deep brain stimulation on rs-fMRI effective connectivity in PD. Task-related fMRI was used to identify areas of interest for rs-fMRI analysis. They found that deep brain stimulation increased the effective connection strength of the cortico-striatal, direct, and thalamo-cortical pathways, whereas it decreased the strength of all the STN afferents and efferents, the hyperdirect pathway, striatal afferents, and STN thalamic connections. As the connection strength in the direct or indirect pathways increased, clinical impairment was reduced, regardless of whether deep brain stimulation was being applied or not. The opposite was true for the striato–STN pathway, such that the stronger the pathway, the more impaired the patient.

Conclusion The available studies in the literature used different methods of assessing resting state connectivity with rs-fMRI. The studies collectively suggest dysfunction in multiple areas of the resting brain in individuals with PD. This dysfunction in resting activity extends beyond the sensorimotor network to include areas across the fronto-parietal and visual networks, as well as the DMN. Within the basal ganglia, resting activity within the posterior putamen may play a significant role in the motor abnormalities seen in PD, and this pattern of findings fits with known patterns of degeneration in PD [47]. It is clear that antiparkinsonian medication has a significant effect on resting brain activity in PD, highlighting the importance of considering the medication status of patients when comparing results across studies. In addition to the prolonged use of dopaminergic medication, increases in disease severity may lead to reorganization of functional neural networks, and future studies should continue to probe how resting neural activity changes with prolonged medication exposure, as well as with advancing disease. It is also the case that small changes in the data processing pipeline, such as alignment procedures, global signal correction, and other covariates, can

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have dramatic effects on rs-fMRI analyses. Future studies that compare methods will be critical in advancing our understanding of how these signals relate to the pathophysiology of PD, and refine which of the analysis approaches discussed in this review are the most robust, and most closely related to the motor and nonmotor features of the disease. The use of larger sample sizes in future rs-fMRI studies of PD will help to facilitate consistency and ensure validation across studies. This will become more plausible and cost-effective as largescale initiatives by the Michael J. Fox Foundation for Parkinson’s Research (Parkinson’s Progressive Marker Initiative; http://www.ppmi-info.org/) and the National Institute of Neurological Disorders and Stroke (Parkinson’s Disease Biomarkers Program; https://pdbp.ninds.nih.gov/) post data drawn from multisite studies. Acknowledgments This work was supported by the National Institutes of Health (R01 NS052318, R01 NS075012) and the Bachmann–Strauss Dystonia & Parkinson Foundation. Compliance with Ethics Guidelines Conflict of Interest Janey Prodoehl has received paid travel expenses from the Federation of State Boards of Physical Therapy. Roxana G. Burciu is supported by grants from the National Institutes of Health (R01 NS052318), the Bachmann–Strauss Dystonia & Parkinson Foundation, and Tyler’s Hope Foundation. David E. Vaillancourt has received grants from the National Institutes of Health (R01 NS052318, R01 NS075012), the Bachmann–Strauss Dystonia & Parkinson Foundation, and Tyler’s Hope Foundation. He has also received board membership honoraria from the National Institutes of Health as a study section member, consultancy fees from University of Texas Southwestern Medical School and the University of Illinois at Chicago, and honoraria from the University of Colorado and the University of Pittsburgh. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

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Resting state functional magnetic resonance imaging in Parkinson's disease.

Neuroimaging advances over the past several decades have provided increased understanding of the structural and functional brain changes that occur wi...
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