Distinct alterations in Parkinson's medication-state and disease-state connectivity

Modulatory effect of levodopa on the basal ganglia-cerebellum connectivity in Parkinson's disease

Long-term levodopa use in Parkinson's disease is associated with declining efficacy and motor complications. Understanding its effects on brain reorganisation is vital for optimizing therapy. Using data from Parkinson's Progression Marker Initiative, we investigated levodopa's impact on resting-state functional connectivity within the cortico-basal ganglia-cerebellum system in 29 patients, under drug-naïve and levodopa-medicated conditions. Univariate comparisons of inter-regional connectivity between conditions were conducted, and multivariate combinations of connections within and between networks were assessed. No significant effect of levodopa was found using the univariate seed-based approach. However, the network connectivity pattern between basal ganglia and cerebellum showed robust classification power. Eigenvector Centrality Mapping (ECM) further identified functional hubs, with cerebellar hubs being the only ones within the cortico-basal ganglia-cerebellum system affected by medication. Our study provides further insight into the importance of inter-network functional connectivity in Parkinson's and the impact of levodopa medication, highlighting the often-overlooked role of the cerebellum.

Spatial and functional profiles distinguish target sets of Parkinson's disease and antipsychotic drugs with different clinical effects

Several studies have examined the genetic factors shared between Parkinson's disease (PD) and schizophrenia (SZ), but the biological themes underlying their clinical relationships remain less explored. We employed systematic transcriptomic and network analyses to examine the genes targeted by two sets of antipsychotic drugs (APDs) - first-generation APDs inducing Parkinsonism and second-generation APDs typically effective against psychotic symptoms in PD - and two sets of PD drugs, one at risk of psychosis and the other with a lower risk of psychosis. Although global brain expression patterns did not effectively differentiate between the targets of the two sets of APDs, they did differentiate the targets of the two PD drug sets. However, both APD and PD target sets showed differences in mean expression levels in specific brain regions. Moreover, they showed significant enrichment for genes highly expressed in distinct adult and prenatal brain structures relative to the overall distribution of such genes among all brain-expressed genes. Specific neurotransmitter systems, either individually or in combinations, appeared to underlie the clinically informed drug categories, indicating their differential roles in inducing or not inducing PD and psychosis. Additionally, the target sets formed distinct network modules representing different biological mechanisms and exhibited differential proximity to putative PD and SZ risk genes in the human interactome. In summary, our study identified specific spatial and functional features that distinguish the target sets of PD and antipsychotic drugs with different clinical effects.

Tangent space functional reconfigurations in individuals at risk for alcohol use disorder

Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are, nevertheless, scarce. Here, we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST). We apply tangent space projection (a Riemannian geometry mapping technique) to transform the functional connectomes (FCs) of 54 participants and quantify functional reconfiguration using the correlation distance of the resulting tangent-FCs. Our goal was to compare functional reconfigurations in individuals at risk for alcohol use disorder (AUD). We hypothesized that functional reconfigurations when transitioning to/from a task would be influenced by family history of AUD (FHA) and other AUD risk factors. Multilinear regression models showed that engaging and disengaging functional reconfiguration were associated with FHA and recent drinking. When engaging in the SST after a rest condition, functional reconfiguration was negatively associated with recent drinking, while functional reconfiguration when disengaging from the SST was negatively associated with FHA. In both models, several other factors contributed to the functional reconfiguration. This study demonstrates that tangent-FCs can characterize task-induced functional reconfiguration and that it is related to AUD risk.

Functional brain controllability in Parkinson's disease and its association with motor outcomes after deep brain stimulation

Introduction

Considering the high economic burden and risks of deep brain stimulation (DBS) surgical failure, predicting the motor outcomes of DBS in Parkinson's disease (PD) is of significant importance in clinical decision-making. Functional controllability provides a rationale for combining the abnormal connections of the cortico-striato-thalamic-cortical (CSTC) motor loops and dynamic changes after medication in DBS outcome prediction.

Methods

In this study, we analyzed the association between preoperative delta functional controllability after medication within CSTC loops and motor outcomes of subthalamic nucleus DBS (STN-DBS) and globus pallidus interna DBS (GPi-DBS) and predicted motor outcomes in a Support Vector Regression (SVR) model using the delta controllability of focal regions.

Results

While the STN-DBS motor outcomes were associated with the delta functional controllability of the thalamus, the GPi-DBS motor outcomes were related to the delta functional controllability of the caudate nucleus and postcentral gyrus. In the SVR model, the predicted and actual motor outcomes were positively correlated, with p = 0.020 and R = 0.514 in the STN-DBS group, and p = 0.011 and R = 0.705 in the GPi- DBS group.

Discussion

Our findings indicate that different focal regions within the CSTC motor loops are involved in STN-DBS and GPi-DBS and support the feasibility of functional controllability in predicting DBS motor outcomes for PD in clinical decision-making.

Tangent space functional reconfigurations in individuals at risk for alcohol use disorder

Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are nevertheless scarce. Here we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST). We apply tangent space projection (a Riemannian geometry mapping technique) to transform functional connectomes (FCs) of 54 participants and quantify functional reconfiguration using the correlation distance of the resulting tangent-FCs. Our goal was to compare functional reconfigurations in individuals at risk for alcohol use disorder (AUD). We hypothesized that functional reconfigurations when transitioning to/from a task would be influenced by family history of alcohol use disorder (FHA) and other AUD risk factors. Multilinear regression models showed that engaging and disengaging functional reconfiguration were associated with FHA and recent drinking. When engaging in the SST after a rest condition, functional reconfiguration was negatively associated with recent drinking, while functional reconfiguration when disengaging from the SST was negatively associated with FHA. In both models, several other factors contributed to the functional reconfiguration. This study demonstrates that tangent-FCs can characterize task-induced functional reconfiguration, and that it is related to AUD risk.

Distinct patterns of electrophysiologic-neuroimaging correlations between Parkinson's disease and multiple system atrophy

Due to a high degree of symptom overlap in the early stages, with movement disorders predominating, Parkinson's disease (PD) and multiple system atrophy (MSA) may exhibit a similar decline in motor areas, yet they differ in their spread throughout the brain, ultimately resulting in two distinct diseases. Drawing upon neuroimaging analyses and altered motor cortex excitability, potential diffusion mechanisms were delved into, and comparisons of correlations across distinct disease groups were conducted in a bid to uncover significant pathological disparities. We recruited thirty-five PD, thirty-seven MSA, and twenty-eight matched controls to conduct clinical assessments, electromyographic recording, and magnetic resonance imaging scanning during the "on medication" state. Patients with neurodegeneration displayed a widespread decrease in electrophysiology in bilateral M1. Brain function in early PD was still in the self-compensatory phase and there was no significant change. MSA patients demonstrated an increase in intra-hemispheric function coupled with a decrease in diffusivity, indicating a reduction in the spread of neural signals. The level of resting motor threshold in healthy aged showed broad correlations with both clinical manifestations and brain circuits related to left M1, which was absent in disease states. Besides, ICF exhibited distinct correlations with functional connections between right M1 and left middle temporal gyrus in all groups. The present study identified subtle differences in the functioning of PD and MSA related to bilateral M1. By combining clinical information, cortical excitability, and neuroimaging intuitively, we attempt to bring light on the potential mechanisms that may underlie the development of neurodegenerative disease.

Multiple biomarkers improve diagnostic accuracy across Lewy body and Alzheimer's disease spectra

OBJECTIVE

More than half of neurodegenerative disease patients have multiple pathologies at autopsy; however, most receive one diagnosis during life. We used the α-synuclein seed amplification assay (αSyn-SAA) and CSF biomarkers for amyloidosis and Alzheimer's disease (AD) neuropathological change (ADNC) to determine the frequency of co-pathologies in participants clinically diagnosed with Lewy body (LB) disease or AD.

METHODS

Using receiver operating characteristic analyses on retrospective CSF samples from 150 participants determined αSyn-SAA accuracy, sensitivity, and specificity for identifying clinically defined LB disease and predicting future change in clinical diagnosis. CSF biomarkers helped determine the frequency of concomitant Lewy body pathology, ADNC, and/or amyloidosis in participants with LB disease and AD, across clinical spectra.

RESULTS

Following a decade-long follow-up, the clinically or autopsy-defined diagnosis changed for nine participants. αSyn-SAA demonstrated improved accuracy (91.3%), sensitivity (89.3%), and specificity (93.3%) for identifying LB disease compared to all non-LB disease, highlighting the limitations of clinical diagnosis alone. When examining biomarkers of co-pathology, amyloidosis was present in 18%, 48%, and 71% (χ2(2) = 13.56, p = 0.001) and AD biomarkers were present in 0%, 8.7%, and 42.9% (χ2(2) = 18.44, p < 0.001) of LB disease participants with different stages of cognitive impairment respectively. Co-occurring biomarkers for αSyn-SAA and amyloidosis were present in 12% and 14% of AD compared to 43% and 57% LB disease participants with different stages of cognitive impairment (χ2(3) = 13.87, p = 0.003).

INTERPRETATION

Our study shows that using a combination of αSyn-SAA and AD biomarkers can identify people with αSyn, ADNC, and co-pathology better and earlier than traditional clinical diagnostic criteria alone.

Consistent abnormal activity in the putamen by dopamine modulation in Parkinson's disease: A resting-state neuroimaging meta-analysis

OBJECTIVE

This study aimed to elucidate brain areas mediated by oral anti-parkinsonian medicine that consistently show abnormal resting-state activation in PD and to reveal their functional connectivity profiles using meta-analytic approaches.

METHODS

Searches of the PubMed, Web of Science databases identified 78 neuroimaging studies including PD OFF state (PD-OFF) versus (vs.) PD ON state (PD-ON) or PD-ON versus healthy controls (HCs) or PD-OFF versus HCs data. Coordinate-based meta-analysis and functional meta-analytic connectivity modeling (MACM) were performed using the activation likelihood estimation algorithm.

RESULTS

Brain activation in PD-OFF vs. PD-ON was significantly changed in the right putamen and left inferior parietal lobule (IPL). Contrast analysis indicated that PD-OFF vs. HCs had more consistent activation in the right paracentral lobule, right middle frontal gyrus, right thalamus, left superior parietal lobule and right putamen, whereas PD-ON vs. HCs elicited more consistent activation in the bilateral middle temporal gyrus, left occipital gyrus, right inferior frontal gyrus and right caudate. MACM revealed coactivation of the right putamen in the direct contrast of PD-OFF vs. PD-ON. Subtraction analysis of significant coactivation clusters for PD-OFF vs. PD-ON with the medium of HCs showed effects in the sensorimotor, top-down control, and visual networks. By overlapping the MACM maps of the two analytical strategies, we demonstrated that the coactivated brain region focused on the right putamen.

CONCLUSIONS

The convergence of local brain regions and co-activation neural networks are involved the putamen, suggesting its potential as a specific imaging biomarker to monitor treatment efficacy.

SYSTEMATIC REVIEW REGISTRATION

[https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD CRD42022304150].

Comprehensive proteomics of CSF, plasma, and urine identify DDC and other biomarkers of early Parkinson's disease

Parkinson's disease (PD) starts at the molecular and cellular level long before motor symptoms appear, yet there are no early-stage molecular biomarkers for diagnosis, prognosis prediction, or monitoring therapeutic response. This lack of biomarkers greatly impedes patient care and translational research-L-DOPA remains the standard of care more than 50 years after its introduction. Here, we performed a large-scale, multi-tissue, and multi-platform proteomics study to identify new biomarkers for early diagnosis and disease monitoring in PD. We analyzed 4877 cerebrospinal fluid, blood plasma, and urine samples from participants across seven cohorts using three orthogonal proteomics methods: Olink proximity extension assay, SomaScan aptamer precipitation assay, and liquid chromatography-mass spectrometry proteomics. We discovered that hundreds of proteins were upregulated in the CSF, blood, or urine of PD patients, prodromal PD patients with DAT deficit and REM sleep behavior disorder or anosmia, and non-manifesting genetic carriers of LRRK2 and GBA mutations. We nominate multiple novel hits across our analyses as promising markers of early PD, including DOPA decarboxylase (DDC), also known as L-aromatic acid decarboxylase (AADC), sulfatase-modifying factor 1 (SUMF1), dipeptidyl peptidase 2/7 (DPP7), glutamyl aminopeptidase (ENPEP), WAP four-disulfide core domain 2 (WFDC2), and others. DDC, which catalyzes the final step in dopamine synthesis, particularly stands out as a novel hit with a compelling mechanistic link to PD pathogenesis. DDC is consistently upregulated in the CSF and urine of treatment-naïve PD, prodromal PD, and GBA or LRRK2 carrier participants by all three proteomics methods. We show that CSF DDC levels correlate with clinical symptom severity in treatment-naïve PD patients and can be used to accurately diagnose PD and prodromal PD. This suggests that urine and CSF DDC could be a promising diagnostic and prognostic marker with utility in both clinical care and translational research.

Does the brain behave like a (complex) network? I. Dynamics

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.

Longitudinal prognosis of Parkinson's outcomes using causal connectivity

Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson's disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

Alterations in Resting-State MR Functional Connectivity of the Central Autonomic Network in Multiple System Atrophy and Relationship with Disease Severity

BACKGROUND

The central autonomic network (CAN) plays a critical role in the body's sympathetic and parasympathetic control. However, functional connectivity (FC) changes of the CAN in patients with multiple system atrophy (MSA) remain unknown.

PURPOSE

To investigate FC alterations of CAN in MSA patients.

STUDY TYPE

Prospective.

POPULATION

Eighty-two subjects (47 patients with MSA [44.7% female, 60.5 ± 6.9 years], 35 age- and sex-matched healthy controls [HC] [57.1% female, 62.5 ± 6.6 years]).

FIELD STRENGTH/SEQUENCE

3-T, resting-state functional magnetic resonance imaging (rs-fMRI) using gradient echo-planar imaging (EPI), T1-weighted three-dimensional magnetization-prepared rapid gradient echo (3D MPRAGE) structural MRI.

ASSESSMENT

FC alterations were explored by using core modulatory regions of CAN as seeds, including midcingulate cortex, insula, amygdala, and ventromedial prefrontal cortex. Bartlett factor score (BFS) derived from a factor analysis of clinical assessments on disease severity was used as a grouping factor for moderate MSA (mMSA: BFS < 0) and severe MSA (sMSA: BFS > 0).

STATISTICAL TESTS

For FC analysis, the one-way ANCOVA with cluster-level family-wise error correction (statistical significance level of P < 0.025), and post hoc t-testing with Bonferroni correction or Tamhane's T2 correction (statistical significance level of adjusted-P < 0.05) were adopted. Correlation was assessed using Pearson correlation or Spearman correlation (statistical significance level of P < 0.05).

RESULTS

Compared with HC, patients with MSA exhibited significant FC aberrances between the CAN and brain areas of sensorimotor control, limbic network, putamen, and cerebellum. For MSA patients, most FC alterations of CAN, especially concerning FC between the right anterior insula and right primary sensorimotor cortices, were found to be significantly correlated with disease severity. FC changes were found to be more significant in sMSA group than in mMSA group when compared with HCs.

DATA CONCLUSION

MSA shows widespread FC changes of CAN, suggesting that abnormal functional integration of CAN may be involved in disease pathogenesis of MSA.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 3.

Tangent functional connectomes uncover more unique phenotypic traits

Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the "fingerprint gradient" (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).

Primary motor area-related injury of anterior central gyrus in Parkinson's disease with dyskinesia: a study based on MRS and Q-Space

INTRODUCTION

By using magnetic resonance spectroscopy (MRS) and Q-Space imaging technology, this research analyzes the imaging characteristics of white matter fibers in the primary motor cortex and posterior limbs of the subcortical internal capsule in parkinsonian patients with motor disorders. The correlation among the changes in axonal function and structure in the cerebral cortex and subcortical cortex and motor disorder is further revealed.

METHODS

First, motor function and clinical condition of 20 patients with Parkinson's disease is assessed the third section of the Unified Parkinson's Scale and H&Y Parkinson's Clinical Staging Scale. Magnetic resonance (MR) scanning is performed with 1H-MRS. Secondly, the range maps of N-acetylaspartic acid (NAA), Choline (Cho), and Creatine (Cr) in the region of interest (the primary motor area of anterior central cortex gyrus, i.e. M1 region) are obtained, and the ratios of NAA/Cr and Cho are calculated. Third, Q-Space MR diffusion imaging technique is used to collect Q-Space images, and a Dsi-studio workstation is used to post-process the images. The fraction anisotropic (FA), generalized fraction anisotropic (GFA), and apparent diffusion coefficient (ADC) parameters of Q-Space in the primary motor cortex and the region of interest in the posterior limb of the internal capsule are obtained. Finally, the parameters of MRS and Q-Space in the experimental group and the control group are further analyzed by SPSS statistical software.

RESULTS

After assessing with Parkinson's score scale, there is obvious motor dysfunction in the experimental group. The average clinical stage of H&Y is 3.0±0.31. In the analysis of MRS data, the ratio of NAA/Cr in the primary motor area of the anterior central gyrus in the experimental group is significantly lower than that in the control group (P<0.05). In the ADC map obtained by Q-Space imaging technique, the ADC value in the primary motor area of the anterior central gyrus in the experimental group is higher than that in the control group (P<0.05), and the difference is statistically significant (P<0.05). There is no significant difference between the experimental group and the control group (P>0.05) in FA and GFA values of the posterior limb of capsule to characterize the characteristics of white matter fibers.

CONCLUSIONS

In parkinsonian patients with motor dysfunction, there are apparent functional and structural changes in the primary motor area neurons and peripheral white matter of the anterior central gyrus, and no obvious damage to the axonal structure of the descending fibers in the cortex.

Neuroimaging approaches to cognition in Parkinson's disease

While direct visualization of Lewy body accumulation within the brain is not yet possible in living Parkinson's disease patients, brain imaging studies offer insights into how the buildup of Lewy body pathology impacts different regions of the brain. Unlike biological biomarkers and purely behavioral research, these brain imaging studies therefore offer a unique opportunity to relate brain localization to cognitive function and dysfunction in living patients. Magnetic resonance imaging studies can reveal physical changes in brain structure as they relate to different cognitive domains and task specific impairments. Functional imaging studies use a combination of task and resting state magnetic resonance imaging, as well as positron emission tomography and single photon emission computed tomography, and can be used to determine changes in blood flow, neuronal activation and neurochemical changes in the brain associated with PD cognition and cognitive impairments. Other unique advantages to brain imaging studies are the ability to monitor changes in brain structure and function longitudinally as patients progress and the ability to study changes in brain function when patients are exposed to different pharmacological manipulations. This is particularly true when assessing the effects of dopaminergic replacement therapy on cognitive function in Parkinson's disease patients. Together, this chapter will describe imaging studies that have helped identify structural and functional brain changes associated with cognition, cognitive impairment, and dementia in Parkinson's disease.

Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders

Neurodegenerative and neuropsychiatric disorders (ND-NPs) are multifactorial, polygenic and complex behavioral phenotypes caused by brain abnormalities. Large-scale collaborative efforts have tried to identify the genetic architecture of these conditions. However, the specific and shared underlying molecular pathobiology of brain illnesses is not clear. Here, we examine transcriptome-wide characterization of eight conditions, using a total of 2,633 post-mortem brain samples from patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), Progressive Supranuclear Palsy (PSP), Pathological Aging (PA), Autism Spectrum Disorder (ASD), Schizophrenia (Scz), Major Depressive Disorder (MDD), and Bipolar Disorder (BP)–in comparison with 2,078 brain samples from matched control subjects.

Similar transcriptome alterations were observed between NDs and NPs with the top correlations obtained between Scz-BP, ASD-PD, AD-PD, and Scz-ASD. Region-specific comparisons also revealed shared transcriptome alterations in frontal and temporal lobes across NPs and NDs. Co-expression network analysis identified coordinated dysregulations of cell-type-specific modules across NDs and NPs. This study provides a transcriptomic framework to understand the molecular alterations of NPs and NDs through their shared- and specific gene expression in the brain.

Frequency-Specific Effects of Galvanic Vestibular Stimulation on Response-Time Performance in Parkinson's Disease

Background: Galvanic vestibular stimulation (GVS) is being increasingly explored as a non-invasive brain stimulation technique to treat symptoms in Parkinson's disease (PD). To date, behavioral GVS effects in PD have been explored with only two stimulus types, direct current and random noise (RN). The interaction between GVS effects and anti-parkinsonian medication is unknown. In the present study, we designed multisine (ms) stimuli and investigated the effects of ms and RN GVS on motor response time. In comparison to the RN stimulus, the ms stimuli contained sinusoidal components only at a set of desired frequencies and the phases were optimized to improve participants' comfort. We hypothesized GVS motor effects were a function of stimulation frequency, and specifically, that band-limited ms-GVS would result in better motor performance than conventionally used broadband RN-GVS.

Materials and Methods: Eighteen PD patients (PDMOFF/PDMON: off-/on-levodopa medication) and 20 healthy controls (HC) performed a simple reaction time task while receiving sub-threshold GVS. Each participant underwent nine stimulation conditions: off-stimulation, RN (4–200 Hz), ms-θ (4–8 Hz), ms-α (8–13 Hz), ms-β (13–30 Hz), ms-γ (30–50 Hz), ms-h1 (50–100 Hz), ms-h2 (100–150 Hz), and ms-h3 (150–200 Hz).

Results: The ms-γ resulted in shorter response time (RPT) in both PDMOFF and HC groups compared with the RN. In addition, the RPT of the PDMOFF group decreased during the ms-β while the RPT of the HC group decreased during the ms-α, ms-h1, ms-h2, and ms-h3. There was considerable inter-subject variability in the optimum stimulus type, although the frequency range tended to fall within 8–100 Hz. Levodopa medication significantly reduced the baseline RPT of the PD patients. In contrast to the off-medication state, GVS did not significantly change RPT of the PD patients in the on-medication state.

Conclusions: Using band-limited ms-GVS, we demonstrated that the GVS frequency for the best RPT varied considerably across participants and was >30 Hz for half of the PDMOFF patients. Moreover, dopaminergic medication was found to influence GVS effects in PD patients. Our results indicate the common “one-size-fits-all” RN approach is suboptimal for PD, and therefore personalized stimuli aiming to address this variability is warranted to improve GVS effects.

Qualitative Deficits in Verbal Fluency in Parkinson's Disease with Mild Cognitive Impairment: A Clinical and Neuroimaging Study

BACKGROUND

Mild cognitive impairment (MCI) in Parkinson's disease (PD) is considered a risk factor for PD with dementia (PDD). Verbal fluency tasks are widely used to assess executive function in PDD. However, in cases of PD with MCI (PD-MCI), the relative diagnostic accuracy of different qualitative verbal fluency measures and their related neural mechanisms remain unknown.

OBJECTIVE

This study aimed to investigate the relative diagnostic accuracy of qualitative (clustering and switching) verbal fluency strategies and their correlates with functional imaging in PD-MCI.

METHODS

Forty-five patients with PD (26 with MCI and 19 without MCI) and 25 healthy controls underwent comprehensive neurocognitive testing and resting-state functional magnetic resonance imaging. MCI in patients with PD was diagnosed according to established clinical criteria. The diagnostic accuracy of verbal fluency measures was determined via receiver operating characteristic analysis. Changes in brain functional connectivity between groups and across clinical measures were assessed using seed-to-voxel analyses.

RESULTS

Patients with PD-MCI generated fewer words and switched less frequently in semantic and phonemic fluency tasks compared to other groups. Switching in semantic fluency showed high diagnostic accuracy for PD-MCI and was associated with reduced functional connectivity in the salience network.

CONCLUSION

Our results indicate that reduced switching in semantic fluency tasks is a sensitive and specific marker for PD-MCI. Qualitative verbal fluency deficits and salience network dysfunction represent early clinical changes observed in PD-MCI.

Functional Connectome in Parkinson's Disease and Parkinsonism

PURPOSE OF REVIEW

There has been an exponential growth in functional connectomics research in neurodegenerative disorders. This review summarizes the recent findings and limitations of the field in Parkinson's disease (PD) and atypical parkinsonian syndromes.

RECENT FINDINGS

Increasingly more sophisticated methods ranging from seed-based to network and whole-brain dynamic functional connectivity have been used. Results regarding the disruption in the functional connectome vary considerably based on disease severity and phenotypes, and treatment status in PD. Non-motor symptoms of PD also link to the dysfunction in heterogeneous networks. Studies in atypical parkinsonian syndromes are relatively scarce. An important clinical goal of functional connectomics in neurodegenerative disorders is to establish the presence of pathology, track disease progression, predict outcomes, and monitor treatment response. The obstacles of reliability and reproducibility in the field need to be addressed to improve the potential of the functional connectome as a biomarker for these purposes in PD and atypical parkinsonian syndromes.

Cognitive impairment in Parkinson's disease is associated with Default Mode Network subsystem connectivity and cerebrospinal fluid Aβ

INTRODUCTION

To identify clinically implementable biomarkers of cognitive impairment in Parkinson's Disease (PD) derived from resting state-functional MRI (rs-fMRI) and CSF protein analysis.

METHODS

In this single-center longitudinal cohort study, we analyzed rs-fMRI and CSF biomarkers from 50 PD patients (23 cognitively normal, 18 mild cognitive impairment, 9 dementia) and 19 controls, who completed comprehensive neuropsychological testing. A subgroup of participants returned for follow-up cognitive assessments three years later. From rs-fMRI, we studied the connectivity within two distinct Default Mode Network subsystems: left-to-right hippocampus (LHC-RHC) and medial prefrontal cortex-to-posterior cingulate cortex (mPFC-PCC). We used regression analyses to determine whether imaging (LHC-RHC, mPFC-PCC), clinical (CSF Aβ-42:40, disease duration), and demographic (age, sex, education) variables were associated with global and domain-specific cognitive impairments.

RESULTS

LHC-RHC (F_3,67 = 3.41,p=0.023) and CSF Aβ-42:40 (χ²(3) = 8.77,p = 0.033) were reduced across more cognitively impaired PD groups. Notably, LHC-RHC connectivity was significantly associated with all global and domain-specific cognitive impairments (attention/executive, episodic memory, visuospatial, and language) at the baseline visit. In an exploratory longitudinal analysis, mPFC-PCC was associated with future global and episodic memory impairment.

CONCLUSION

We used biomarker techniques that are readily available in clinical and research facilities to shed light on the pathophysiologic basis of cognitive impairment in PD. Our findings suggest that there is a functionally distinct role of the hippocampal subsystem within the DMN resting state network, and that intrinsic connectivity between the hippocampi is critically related to a broad range of cognitive functions in PD.

Alterations of regional homogeneity in Parkinson's disease with "pure" apathy: A resting-state fMRI study

BACKGROUND

Apathy is a prevalent and debilitating neuropsychiatric syndrome in Parkinson's disease (PD). However, its neural mechanisms are still unclear.

METHODS

Forty-six de novo, drug-naïve, non-demented PD patients without depressive or anxious symptoms, of whom 26 were apathetic (PD-A) and 20 were not (PD-NA) according to the Apathy Scale (AS), and 23 matched healthy control (HC) subjects were enrolled in this study. The regional homogeneity (ReHo) approach based on resting-state functional MRI on a 3-T MR system was used to investigate apathy related local brain activity.

RESULTS

Compared with both patients with PD-NA and HC subjects, patients with PD-A showed significantly lower ReHo values in the dorsal anterior cingulate cortex (ACC) and right caudate. Both the PD-A and PD-NA groups also demonstrated lower ReHo values in the right putamen compared to the HC group. Further correlation analyses revealed that AS scores were negatively correlated with the ReHo values in the dorsal ACC and right caudate in the pooled patients with PD.

LIMITATIONS

The present results are preliminary due to the small sample size in the study.

CONCLUSIONS

This study used ReHo for the first time to characterize "pure" apathy related regional spontaneous brain function within the frontostriatal circuits in PD. Our findings suggest that abnormal brain activity in the dorsal ACC and caudate may involve the pathological mechanisms of apathy in PD.

White Matter Hyperintensities Related to Parkinson's Disease Executive Function

BACKGROUND

People with Parkinson's disease (PD) can develop multidomain cognitive impairments; however, it is unclear whether different pathologies underlie domain-specific cognitive dysfunction.

OBJECTIVES

We investigated the contribution of vascular copathology severity and location, as measured by MRI white matter hyperintensities (WMHs), to domain-specific cognitive impairment in PD.

METHODS

We studied 85 PD (66.6 ± 9.2 years) and 18 control (65.9 ± 6.6) participants. Using the Fazekas scale for rating the severity of WMH, we subdivided PD into 14 PD-WMH+ and 71 PD-WMH-. Participants underwent global, executive, visuospatial, episodic memory, and language testing. We performed nonparametric permutation testing to create WMH probability maps based on PD-WMH group and cognitive test performance.

RESULTS

The PD-WMH+ group showed worse global and executive cognitive performance than the PD-WMH- group. On individual tests, the PD-WMH+ group showed worse Montreal Cognitive Assessment (MoCA), Stroop, Symbol Digit Modalities Test (SDMT), and Digit Span scores. WMH probability maps showed that in the PD-WMH+ group, worse Stroop was associated with lesions centered around the corticospinal tract (CST), forceps major, inferior-fronto-occipital fasciculus, and superior longitudinal fasciculus; worse SDMT with lesions around the CST, forceps major, and posterior corona radiata; worse Digit Span with lesions around the posterior corona radiata; and worse MoCA with lesions around the CST.

CONCLUSIONS

We found that WMH severity was associated with PD executive dysfunction, including worse attention, working memory, and processing speed. Disruption of key white matter tracts in proximity to vascular lesions could contribute to these specific cognitive impairments. Early treatment of vascular disease might mitigate some executive dysfunction in a subset of patients with PD.

MRI biomarkers of motor and non-motor symptoms in Parkinson's disease

Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.

Cortico-basal white matter alterations occurring in Parkinson's disease

Magnetic resonance imaging studies typically use standard anatomical atlases for identification and analyses of (patho-)physiological effects on specific brain areas; these atlases often fail to incorporate neuroanatomical alterations that may occur with both age and disease. The present study utilizes Parkinson's disease and age-specific anatomical atlases of the subthalamic nucleus for diffusion tractography, assessing tracts that run between the subthalamic nucleus and a-priori defined cortical areas known to be affected by Parkinson's disease. The results show that the strength of white matter fiber tracts appear to remain structurally unaffected by disease. Contrary to that, Fractional Anisotropy values were shown to decrease in Parkinson's disease patients for connections between the subthalamic nucleus and the pars opercularis of the inferior frontal gyrus, anterior cingulate cortex, the dorsolateral prefrontal cortex and the pre-supplementary motor, collectively involved in preparatory motor control, decision making and task monitoring. While the biological underpinnings of fractional anisotropy alterations remain elusive, they may nonetheless be used as an index of Parkinson's disease. Moreover, we find that failing to account for structural changes occurring in the subthalamic nucleus with age and disease reduce the accuracy and influence the results of tractography, highlighting the importance of using appropriate atlases for tractography.

Towards understanding neural network signatures of motor skill learning in Parkinson's disease and healthy aging

In the past decade, neurorehabilitation has been shown to be an effective therapeutic supplement for patients with Parkinson's disease (PD). However, patients still experience severe problems with the consolidation of learned motor skills. Knowledge on the neural correlates underlying this process is thus essential to optimize rehabilitation for PD. This review investigates the existing studies on neural network connectivity changes in relation to motor learning in healthy aging and PD and critically evaluates the imaging methods used from a methodological point of view. The results indicate that despite neurodegeneration there is still potential to modify connectivity within and between motor and cognitive networks in response to motor training, although these alterations largely bypass the most affected regions in PD. However, so far training-related changes are inferred and possible relationships are not substantiated by brain-behavior correlations. Furthermore, the studies included suffer from many methodological drawbacks. This review also highlights the potential for using neural network measures as predictors for the response to rehabilitation, mainly based on work in young healthy adults. We speculate that future approaches, including graph theory and multimodal neuroimaging, may be more sensitive than brain activation patterns and model-based connectivity maps to capture the effects of motor learning. Overall, this review suggests that methodological developments in neuroimaging will eventually provide more detailed knowledge on how neural networks are modified by training, thereby paving the way for optimized neurorehabilitation for patients.

Hippocampal CA1 subfield predicts episodic memory impairment in Parkinson's disease

OBJECTIVE

Parkinson's disease (PD) episodic memory impairments are common; however, it is not known whether these impairments are due to hippocampal pathology. Hippocampal Lewy-bodies emerge by Braak stage 4, but are not uniformly distributed. For instance, hippocampal CA1 Lewy-body pathology has been specifically associated with pre-mortem episodic memory performance in demented patients. By contrast, the dentate gyrus (DG) is relatively free of Lewy-body pathology. In this study, we used ultra-high field 7-Tesla to measure hippocampal subfields in vivo and determine if these measures predict episodic memory impairment in PD during life.

METHODS

We studied 29 participants with PD (age 65.5 ± 7.8 years; disease duration 4.5 ± 3.0 years) and 8 matched-healthy controls (age 67.9 ± 6.8 years), who completed comprehensive neuropsychological testing and MRI. With 7-Tesla MRI, we used validated segmentation techniques to estimate CA1 stratum pyramidale (CA1-SP) and stratum radiatum lacunosum moleculare (CA1-SRLM) thickness, dentate gyrus/CA3 (DG/CA3) area, and whole hippocampus area. We used linear regression, which included imaging and clinical measures (age, duration, education, gender, and CSF), to determine the best predictors of episodic memory impairment in PD.

RESULTS

In our cohort, 62.1% of participants with PD had normal cognition, 27.6% had mild cognitive impairment, and 10.3% had dementia. Using 7-Tesla MRI, we found that smaller CA1-SP thickness was significantly associated with poorer immediate memory, delayed memory, and delayed cued memory; by contrast, whole hippocampus area, DG/CA3 area, and CA1-SRLM thickness did not significantly predict memory. Age-adjusted linear regression models revealed that CA1-SP predicted immediate memory (beta[standard error]10.895[4.215], p < .05), delayed memory (12.740[5.014], p < .05), and delayed cued memory (12.801[3.991], p < .05). In the fully-adjusted models, which included all five clinical measures as covariates, only CA1-SP remained a significant predictor of delayed cued memory (13.436[4.651], p < .05).

CONCLUSIONS

In PD, we found hippocampal CA1-SP subfield thickness estimated on 7-Tesla MRI scans was the best predictor of episodic memory impairment, even when controlling for confounding clinical measures. Our results imply that ultra-high field imaging could be a sensitive measure to identify changes in hippocampal subfields and thus probe the neuroanatomical underpinnings of episodic memory impairments in patients with PD.

Benchmarking functional connectome-based predictive models for resting-state fMRI

Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices. We systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence). The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning. For each step we benchmark typical choices: 8 different ways of defining regions -either pre-defined or generated from the rest-fMRI data- 3 measures to build functional connectomes from the extracted time-series, and 10 classification models to compare functional interactions across subjects. Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations in the populations and sites. We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations and partial correlation; and that simple linear predictors such as a logistic regression give the best predictions. Our work is a step forward to establishing reproducible imaging-based biomarkers for clinical settings.

Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level

Background

Recent studies using resting-state functional connectivity and machine-learning to distinguish patients with neurodegenerative diseases from other groups of subjects show promising results. This approach has not been tested to discriminate between Parkinson's disease (PD) and multiple system atrophy (MSA) patients.

Objectives

Our first aim is to characterize possible abnormalities in resting-state functional connectivity between the cerebellum and a set of intrinsic-connectivity brain networks and between the cerebellum and different regions of the striatum in PD and MSA. The second objective of this study is to assess the potential of cerebellar connectivity measures to distinguish between PD and MSA patients at the single-patient level.

Methods

Fifty-nine healthy controls, 62 PD patients, and 30 MSA patients underwent resting-state functional MRI with a 3T scanner. Independent component analysis and dual regression were used to define seven resting-state networks of interest. To assess striatal connectivity, a seed-to-voxel approach was used after dividing the striatum into six regions bilaterally. Measures of cerebellar-brain network and cerebellar-striatal connectivity were then used as features in a support vector machine to discriminate between PD and MSA patients.

Results

MSA patients displayed reduced cerebellar connectivity with different brain networks and with the striatum compared with PD patients and with controls. The classification procedure achieved an overall accuracy of 77.17% with 83.33% of the MSA subjects and 74.19% of the PD patients correctly classified.

Conclusion

Our findings suggest that measures of cerebellar functional connectivity have the potential to distinguish between PD and MSA patients.

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Reduced cerebellar functional connectivity in MSA compared with healthy controls.

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Reduced cerebellar-striatal functional connectivity in MSA compared with PD.

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Reduced connectivity between cerebellum and brain networks in MSA compared with PD.

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Cerebellar connectivity might help discriminate between MSA and PD patients.

Resting-state Functional MRI in Parkinsonian Syndromes

Background

Functional MRI (fMRI) has been widely used to study abnormal patterns of functional connectivity at rest in patients with movement disorders such as idiopathic Parkinson's disease (PD) and atypical parkinsonisms.

Methods

This manuscript provides an educational review of the current use of resting-state fMRI in the field of parkinsonian syndromes.

Results

Resting-state fMRI studies have improved the current knowledge about the mechanisms underlying motor and non-motor symptom development and progression in movement disorders. Even if its inclusion in clinical practice is still far away, resting-state fMRI has the potential to be a promising biomarker for early disease detection and prediction. It may also aid in differential diagnosis and monitoring brain responses to therapeutic agents and neurorehabilitation strategies in different movement disorders.

Conclusions

There is urgent need to identify and validate prodromal biomarkers in PD patients, to perform further studies assessing both overlapping and disease-specific fMRI abnormalities among parkinsonian syndromes, and to continue technical advances to fully realize the potential of fMRI as a tool to monitor the efficacy of chronic therapies.

Active information storage in Parkinson's disease: a resting state fMRI study over the sensorimotor cortex

Parkinson's disease (PD), the second most frequent neurodegenerative disease, affects significantly life quality by a combination of motor and cognitive disturbances. Although it is traditionally associated with basal ganglia dysfunction, cortical alterations are also involved in disease symptoms. Our objective is to evaluate the alterations in brain dynamics in de novo and recently treated PD subjects using a nonlinear method known as Active Information Storage. In the current research, Active Information Storage (AIS) was used to study the complex dynamics in motor cortex spontaneous activity captured using resting state functional Magnetic Resonance Imaging (rs-fMRI) at early-stage in non-medicated and recently medicated PD subjects. Supplementary to AIS, the fractional Amplitude of Low Frequency Fluctuation (fALFF), which is a better-established technique of analysis of rs-fMRI signals, was also evaluated. Compared to healthy subjects, the AIS values were significantly reduced in PD patients over the analyzed motor cortex regions; differences were also found at less extent using the fALFF measure. Correlations between AIS and fALFF values showed that the measures seem to capture similar neuronal phenomena in rs-fMRI data. The highest sensitivity when detecting group differences revealed by AIS, and not captured by traditional linear approaches, suggests that this measure is a promising tool for the analysis of rs-fMRI neural data in PD.

Functional MRI in Idiopathic Parkinson's Disease

Functional MRI (fMRI) has been widely used to study abnormal patterns of brain connectivity at rest and activation during a variety of tasks in patients with idiopathic Parkinson's disease (PD). fMRI studies in PD have led to a better understanding of many aspects of the disease including both motor and non-motor symptoms. Although its translation into clinical practice is still at an early stage, fMRI measures hold promise for multiple clinical applications in PD, including the early detection, predicting future change in clinical status, and as a marker of alterations in brain physiology related to neurotherapeutic agents and neurorehabilitative strategies.

Dopamine-related dissociation of cortical and subcortical brain activations in cognitively unimpaired Parkinson's disease patients OFF and ON medications

Background:

Despite dopaminergic depletion that is severe enough to cause the motor symptoms of Parkinson’s disease (PD), m any patients remain cognitively unimpaired. Little is known about brain mechanism s underlying such preserved cognitive abilities and their alteration by dopaminergic medications.

Objectives:

We investigated brain activations underlying dopamine-related differences in cognitive function using a unique experimental design with PD patients off and on dopaminergic medications. We tested the dopamine overdose hypothesis, which posits that the excess of exogenous dopamine in the frontal cortical regions can impair cognition.

Methods:

We used a two-choice forced response Choice Reaction Time (CRT) task to probe cognitive processes underlying response selection and execution. Functional magnetic resonance imaging data Were acquired from 16 cognitively unimpaired (Level-II) PD participants and 15 Well-matched healthy controls (HC). We compared task performance (i.e. reaction time and accuracy) and brain activation of PD participants off dopaminergic medications (PD_OFF) in comparison with HC, and PD_OFF participants with those on dopaminergic medications (PD_ON).

Results:

PD_OFF and PD_ON groups did not differ from each other, or from the HC group, in reaction time or accuracy. Compared to HC, PD_OFF activated the bilateral putamen less, and this w as compensated by higher activation of the anterior insula. No such differences Were observed in the PD_ON group, Compared to HC. Compared to both HC and PD_OFF, PD_ON participants showed dopamine-related hyperactivation in the frontal cortical regions and hypoactivation in the amygdala.

Conclusion:

Our data provide further evidence that PD_OFF and PD_ON participants engage different cortical and subcortical systems to achieve similar levels of cognitive performance as HC. Crucially, our findings demonstrate dopamine-related dissociation in brain activation between cortical and subcortical regions, and provide novel support for the dopamine overdose hypothesis.

Resting State fMRI: A Valuable Tool for Studying Cognitive Dysfunction in PD

Cognitive impairment is a common disabling symptom in PD. Unlike motor symptoms, the mechanism underlying cognitive dysfunction in Parkinson's disease (PD) remains unclear and may involve multiple pathophysiological processes. Resting state functional magnetic resonance imaging (rs-fMRI) is a fast-developing research field, and its application in cognitive impairments in PD is rapidly growing. In this review, we summarize rs-fMRI studies on cognitive function in PD and discuss the strong potential of rs-fMRI in this area. rs-fMRI can help reveal the pathophysiology of cognitive symptoms in PD, facilitate early identification of PD patients with cognitive impairment, distinguish PD dementia from dementia with Lewy bodies, and monitor and guide treatment for cognitive impairment in PD. In particular, ongoing and future longitudinal studies would enhance the ability of rs-fMRI in predicting PD dementia. In combination with other modalities such as positron emission tomography, rs-fMRI could give us more information on the underlying mechanism of cognitive deficits in PD.