The reorganization of functional architecture in the early-stages of Parkinson's disease

Cerebellar involvement in Parkinson's disease: Pathophysiology and neuroimaging

ABSTRACT

Parkinson's disease (PD) is a neurodegenerative disease characterized by various motor and non-motor symptoms. The complexity of its symptoms suggests that PD is a heterogeneous neurological disorder. Its pathological changes are not limited to the substantia nigra-striatal system, but gradually extending to other regions including the cerebellum. The cerebellum is connected to a wide range of central nervous system regions that form essential neural circuits affected by PD. In addition, altered dopaminergic activity and α-synuclein pathology are found in the cerebellum, further suggesting its role in the PD progression. Furthermore, an increasing evidence obtained from imaging studies has demonstrated that cerebellar structure, functional connectivity, and neural metabolism are altered in PD when compared to healthy controls, as well as among different PD subtypes. This review provides a comprehensive summary of the cerebellar pathophysiology and results from neuroimaging studies related to both motor and non-motor symptoms of PD, highlighting the potential significance of cerebellar assessment in PD diagnosis, differential diagnosis, and disease monitoring.

Adoption of deep learning-based magnetic resonance image information diagnosis in brain function network analysis of Parkinson's disease patients with end-of-dose wearing-off

OBJECTIVE

this study was to analyze the brain functional network of end-of-dose wearing-off (EODWO) in patients with Parkinson's disease (PD) using a convolutional neural network (CNN)-based functional magnetic resonance imaging (fMRI) data classification model.

METHODS

one hundred PD patients were recruited and assigned to control (Ctrl) group (39 cases without EODWO) and experimental (Exp) group (61 cases with EODWO). The data classification model based on a CNN was employed to assist the analysis of the changes in brain functional network structure in the two groups. The CNN-based fMRI data classification model was primarily based on a CNN architecture, with improvements made to the initialization of convolutional kernel parameters. Firstly, a structure based on restricted Boltzmann machine (RBM) was constructed, followed by the initialization of convolutional kernel parameters. Subsequently, the model underwent training. Utilizing the data analysis module within the GRETNA toolbox, extracted feature sets were analyzed, including local measures such as betweenness centrality (BC) and degree centrality (DC), as well as global measures such as global efficiency (Eg) and local efficiency (Eloc).

RESULTS

as sparsity increased, there was a gradual upward trend observed in Eg; however, the values of Eg in both brain functional networks remained relatively stable within the range of 0.2-0.5. The Eg value of the Ctrl group's whole-brain functional network was 0.17 ± 0.02, while that of the Exp group's whole-brain functional network was 0.17 ± 0.03, with no significant difference between them (P>0.05). The functional DC value of the superior frontal gyrus in the Exp group (8.71 ± 2.56) was significantly lower than that of the Ctrl group (13.32 ± 3.22), whereas the functional DC value of the anterior cingulate gyrus in the Exp group (19.33 ± 4.78) was significantly higher than that of the Ctrl group (15.21 ± 4.02) (P<0.05). There was no significant correlation observed between the functional DC value and levodopa or dopamine agonist therapy (DDT) in the Exp group, whereas the Ctrl group exhibited a significant positive correlation.

CONCLUSION

analysis conducted via a CNN-based fMRI data classification model revealed a correlation between the occurrence of EODWO in PD patients and functional impairments in the left precuneus. Additionally, the occurrence of EODWO may potentially diminish the plasticity of the central prefrontal dopamine.

Increased prevalence of vertebrobasilar dolichoectasia in Parkinson's disease and its effect on white matter microstructure and network

This study aimed to investigate the prevalence of vertebrobasilar dolichoectasia (VBD) in Parkinson's disease (PD) patients and analyze its role in gray matter changes, white matter (WM) microstructure and network alterations in PD. This is a cross-sectional study including 341 PD patients. Prevalence of VBD in these PD patients was compared with general population. Diffusion tensor imaging and T1-weighted imaging analysis were performed among 174 PD patients with or without VBD. Voxel-based morphometry analysis was used to estimate gray matter volume changes. Tract-based spatial statistics and region of interest-based analysis were used to evaluate WM microstructure changes. WM network analysis was also performed. Significantly higher prevalence of VBD in PD patients was identified compared with general population. Lower fractional anisotropy and higher diffusivity, without significant gray matter involvement, were found in PD patients with VBD in widespread areas. Decreased global and local efficiency, increased hierarchy, decreased degree centrality at left Rolandic operculum, increased betweenness centrality at left postcentral gyrus and decreased average connectivity strength between and within several modules were identified in PD patients with VBD. VBD is more prevalent in PD patients than general population. Widespread impairments in WM microstructure and WM network involving various motor and nonmotor PD symptom-related areas are more prominent in PD patients with VBD compared with PD patients without VBD.

Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease

BACKGROUND

Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits.

METHODS

We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities.

RESULTS

Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected).

CONCLUSIONS

Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.

Brain network centrality and connectivity are associated with clinical subtypes and disease progression in Parkinson's disease

To investigate brain network centrality and connectivity alterations in different Parkinson's disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD.

The efficacy of low frequency repetitive transcial magnetic stimulation for treating auditory verbal hallucinations in schizophrenia: Insights from functional gradient analyses

Background

Auditory Verbal Hallucinations (AVH) constitute a prominent feature of schizophrenia. Although low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated therapeutic benefits in ameliorating AVH, the underlying mechanisms of its efficacy necessitate further elucidation.

Objective

This study investigated the cortical gradient characteristics and their associations with clinical responses in schizophrenia patients with AVH, mediated through 1 Hz rTMS targeting the left temporoparietal junction.

Method

Functional gradient metrics were employed to examine the hierarchy patterns of cortical organization, capturing whole-brain functional connectivity profiles in patients and controls.

Results

The 1 Hz rTMS treatment effectively ameliorated the positive symptoms in patients, specifically targeting AVH. Initial evaluations revealed expanded global gradient distribution patterns and specific principal gradient variations in certain brain regions in patients at baseline compared to a control cohort. Following treatment, these divergent global and local patterns showed signs of normalizing. Furthermore, there was observed a closer alignment in between-network dispersion among various networks after treatment, including the somatomotor, attention, and limbic networks, indicating a potential harmonization of brain functionality.

Conclusion

Low-frequency rTMS induces alternations in principal functional gradient patterns, may serve as imaging markers to elucidate the mechanisms underpinning the therapeutic efficacy of rTMS on AVH in schizophrenia.

Functional alterations in resting-state networks for Theory of Mind in Parkinson's disease

In Parkinson's disease (PD), impairment of Theory of Mind (ToM) has recently attracted an increasing number of neuroscientific investigations. If and how functional connectivity of the ToM network is altered in PD is still an open question. First, we explored whether ToM network connectivity shows potential PD-specific functional alterations when compared to healthy controls (HC). Second, we tested the role of the duration of PD in the evolution of functional alterations in the ToM network. Between-group connectivity alterations were computed adopting resting-state functional magnetic resonance imaging (rs-fMRI) data of four groups: PD patients with short disease duration (PD-1, n = 72); PD patients with long disease duration (PD-2, n = 22); healthy controls for PD-1 (HC-1, n = 69); healthy controls for PD-2 (HC-2, n = 22). We explored connectivity differences in the ToM network within and between its three subnetworks: Affective, Cognitive and Core. PD-1 presented a global pattern of decreased functional connectivity within the ToM network, compared to HC-1. The alterations mainly involved the Cognitive and Affective ToM subnetworks and their reciprocal connections. PD-2-those with longer disease duration-showed an increased connectivity spanning the entire ToM network, albeit less consistently in the Core ToM network, compared to both the PD-1 and the HC-2 groups. Functional connectivity within the ToM network is altered in PD. The alterations follow a graded pattern, with decreased connectivity at short disease duration, which broadens to a generalized increase with longer disease duration. The alterations involve both the Cognitive and Affective subnetworks of ToM.

The cerebellum directly modulates the substantia nigra dopaminergic activity

Evidence of direct reciprocal connections between the cerebellum and basal ganglia has challenged the long-held notion that these structures function independently. While anatomical studies have suggested the presence of cerebellar projections to the substantia nigra pars compacta (SNc), the nature and function of these connections (Cb-SNc) is unknown. Here we show, in mice, that Cb-SNc projections form monosynaptic glutamatergic synapses with dopaminergic and non-dopaminergic neurons in the SNc. Optogenetic activation of Cb-SNc axons in the SNc is associated with increased SNc activity, elevated striatal dopamine levels and increased locomotion. During behavior, Cb-SNc projections are bilaterally activated before ambulation and unilateral lever manipulation. Cb-SNc projections show prominent activation for water reward and higher activation for sweet water, suggesting that the pathway also encodes reward value. Thus, the cerebellum directly, rapidly and effectively modulates basal ganglia dopamine levels and conveys information related to movement initiation, vigor and reward processing.

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.

Connectome-based fingerprint of motor impairment is stable along the course of Parkinson's disease

Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.

Cerebellar functional connectivity change is associated with motor and neuropsychological function in early stage drug-naïve patients with Parkinson's disease

Introduction

Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting both motor and cognitive function. Previous neuroimaging studies have reported altered functional connectivity (FC) in distributed functional networks. However, most neuroimaging studies focused on patients at an advanced stage and with antiparkinsonian medication. This study aims to conduct a cross-sectional study on cerebellar FC changes in early-stage drug-naïve PD patients and its association with motor and cognitive function.

Methods

Twenty-nine early-stage drug-naïve PD patients and 20 healthy controls (HCs) with resting-state fMRI data and motor UPDRS and neuropsychological cognitive data were extracted from the Parkinson's Progression Markers Initiative (PPMI) archives. We used seed-based resting-state fMRI (rs-fMRI) FC analysis and the cerebellar seeds were defined based on the hierarchical parcellation of the cerebellum (AAL atlas) and its topological function mapping (motor cerebellum and non-motor cerebellum).

Results

The early stage drug-naïve PD patients had significant differences in cerebellar FC when compared with HCs. Our findings include: (1) Increased intra-cerebellar FC within motor cerebellum, (2) increase motor cerebellar FC in inferior temporal gyrus and lateral occipital gyrus within ventral visual pathway and decreased motor-cerebellar FC in cuneus and dorsal posterior precuneus within dorsal visual pathway, (3) increased non-motor cerebellar FC in attention, language, and visual cortical networks, (4) increased vermal FC in somatomotor cortical network, and (5) decreased non-motor and vermal FC within brainstem, thalamus and hippocampus. Enhanced FC within motor cerebellum is positively associated with the MDS-UPDRS motor score and enhanced non-motor FC and vermal FC is negatively associated with cognitive function test scores of SDM and SFT.

Conclusion

These findings provide support for the involvement of cerebellum at an early stage and prior to clinical presentation of non-motor features of the disease in PD patients.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity

The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.

The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression

INTRODUCTION

Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome.

AREAS COVERED

The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years.

EXPERT OPINION

Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.

Sensorimotor network connectivity correlates with motor improvement after repetitive transcranial magnetic stimulation in patients with Parkinson's disease

BACKGROUND

Emerging evidence suggests that repetitive transcranial magnetic stimulation (rTMS) generally improves Parkinson's disease (PD) motor symptoms. However, personal responses to rTMS might be different. In this study, we explore the connectivity changes in PD patients with different responses to rTMS.

METHODS

Among PD patients, 25 were treated with 10Hz-rTMS and seven were with sham rTMS over the supplementary motor area for 10 days. Resting-state functional connectivity magnetic resonance imaging (rs-fMRI) was performed in PD patients before and after rTMS stimulation. Neuropsychological scales such as Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) were collected synchronously with rs-fMRI. To explore the connectivity changes after rTMS, degree centrality was calculated.

RESULTS

13 out of 25 participants were responsive to 10Hz rTMS. Degree centrality patterns in the left sensorimotor regions are primarily responsible for the differences between responsive and non-responsive individuals. Improvement in motor symptoms was substantially related to the baseline degree centrality in the left PreCG and the left PoCG. The performance in distinguishing non-responders from responders was further validated by the ROC analysis utilizing DC characteristics. Lastly, we found that connectivity increased in left PreCG and PoCG in patients with a better response to the rTMS.

CONCLUSION

Taken together, these results suggest that the sensorimotor network is involved in the motor improvement following rTMS treatment, with patients with lower sensorimotor connectivity showing a tendency for greater motor improvement to HF-rTMS.

Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease

AIMS

This study aimed to characterize the topological alterations and classification performance of high-order functional connectivity (HOFC) networks in cognitively preserved patients with Parkinson's disease (PD), relative to low-order FC (LOFC) networks.

METHODS

The topological metrics of the constructed networks (LOFC and HOFC) obtained from fifty-one cognitively normal patients with PD and 60 matched healthy control subjects were analyzed. The discriminative abilities were evaluated using machine learning approach.

RESULTS

The HOFC networks in the PD group showed decreased segregation and integration. The normalized clustering coefficient and small-worldness in the HOFC networks were correlated to motor performance. The altered nodal centralities (distributed in the precuneus, putamen, lingual gyrus, supramarginal gyrus, motor area, postcentral gyrus and inferior occipital gyrus) and intermodular FC (frontoparietal and visual networks, sensorimotor and subcortical networks) were specific to HOFC networks. Several highly connected nodes (thalamus, paracentral lobule, calcarine fissure and precuneus) and improved classification performance were found based on HOFC profiles.

CONCLUSION

This study identified disrupted topology of functional interactions at a high level with extensive alterations in topological properties and improved differentiation ability in patients with PD prior to clinical symptoms of cognitive impairment, providing complementary insights into complex neurodegeneration in PD.

Brain functional network abnormalities in parkinson's disease with mild cognitive impairment

Mild cognitive impairment in Parkinson's disease (PD-M) is related to a high risk of dementia. This study explored the whole-brain functional networks in early-stage PD-M. Forty-one patients with PD classified as cognitively normal (PD-N, n = 17) and PD-M (n = 24) and 24 demographically matched healthy controls (HC) underwent clinical and neuropsychological evaluations and resting-state functional magnetic resonance imaging. The global, regional, and modular topological characteristics were assessed in the brain functional networks, and their relationships to cognitive scores were tested. At the global level, PD-M and PD-N exhibited higher characteristic path length and lower clustering coefficient, local and global efficiency relative to HC. At the regional level, PD-M and PD-N showed lower nodal centrality in sensorimotor regions relative to HC. At the modular level, PD-M showed lower intramodular connectivity in default mode and cerebellum modules, and lower intermodular connectivity between default mode and frontoparietal modules than PD-N, correlated with Montreal Cognitive Assessment scores. Early-stage PD patients showed weaker small-worldization of brain networks. Modular connectivity alterations were mainly observed in patients with PD-M. These findings highlight the shared and distinct brain functional network dysfunctions in PD-M and PD-N, and yield insight into the neurobiology of cognitive decline in PD.

Impaired Brain Information Transmission Efficiency and Flexibility in Parkinson’s Disease and Rapid Eye Movement Sleep Behavior Disorder: Evidence from Functional Connectivity and Functional Dynamics

Parkinson’s disease (PD) is a common neurodegenerative disorder. Rapid eye movement sleep behavior disorder (RBD) is one of the prodromal symptoms of PD. Studies have shown that brain information transmission is affected in PD patients. Consequently, we hypothesized that brain information transmission is impaired in RBD and PD. To prove our hypothesis, we performed functional connectivity (FC) and functional dynamics analysis of three aspects—based on the whole brain, within the resting-state network (RSN), and the interaction between RSNs—using normal control (NC) (n = 21), RBD (n = 24), and PD (n = 45) resting-state functional magnetic resonance imaging (rs-fMRI) data sets. Furthermore, we tested the explanatory power of FC and functional dynamics for the clinical features. Our results found that the global functional dynamics and FC of RBD and PD were impaired. Within RSN, the impairment concentrated in the visual network (VIS) and sensorimotor network (SMN), and the impaired degree of SMN in RBD was higher than that in PD. On the interaction between RSNs, RBD showed a widespread decrease, and PD showed a focal decrease which concentrated in SMN and VIS. Finally, we proved FC and functional dynamics were related to clinical features. These differences confirmed that brain information transmission efficiency and flexibility are impaired in RBD and PD, and these impairments are associated with the clinical features of patients.

Imaging the Limbic System in Parkinson's Disease-A Review of Limbic Pathology and Clinical Symptoms

The limbic system describes a complex of brain structures central for memory, learning, as well as goal directed and emotional behavior. In addition to pathological studies, recent findings using in vivo structural and functional imaging of the brain pinpoint the vulnerability of limbic structures to neurodegeneration in Parkinson's disease (PD) throughout the disease course. Accordingly, dysfunction of the limbic system is critically related to the symptom complex which characterizes PD, including neuropsychiatric, vegetative, and motor symptoms, and their heterogeneity in patients with PD. The aim of this systematic review was to put the spotlight on neuroimaging of the limbic system in PD and to give an overview of the most important structures affected by the disease, their function, disease related alterations, and corresponding clinical manifestations. PubMed was searched in order to identify the most recent studies that investigate the limbic system in PD with the help of neuroimaging methods. First, PD related neuropathological changes and corresponding clinical symptoms of each limbic system region are reviewed, and, finally, a network integration of the limbic system within the complex of PD pathology is discussed.

Static and Dynamic Functional Connectivity Alterations in Alzheimer's Disease and Neuropsychiatric Diseases

To date, numerous studies have documented various alterations in resting brain activity in Alzheimer's disease (AD) and other neuropsychiatric diseases. In particular, disease-related alterations of functional connectivity (FC) in the resting state networks (RSN) have been documented. Altered FC in RSN is useful not only for interpreting the phenotype of diseases but also for diagnosing the diseases. More recently, several studies proposed the dynamics of resting-brain activity as a useful marker for detecting altered RSNs related to AD and other diseases. In contrast to previous studies, which focused on FC calculated using an entire fMRI scan (static FC), these newer studies focused the on temporal dynamics of FC within the scan (dynamic FC) to provide more sensitive measures to characterize RSNs. However, despite the increasing popularity of dFC, several studies cautioned that the results obtained in commonly used analyses for dFC require careful interpretation. In this mini-review, we review recent studies exploring alterations of static and dynamic functional connectivity in AD and other neuropsychiatric diseases. We then discuss how to utilize and interpret dFC for studying resting brain activity in diseases.

Parkinson's Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data

Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the l_2,p norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson's Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.

Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach

To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.

Functional Brain Dysconnectivity in Parkinson's Disease: A 5-Year Longitudinal Study

Background

Tracking longitudinal functional brain dysconnectivity in Parkinson's disease (PD) is a key element to decoding the underlying physiopathology and understanding PD progression.

Objectives

The objectives of this follow‐up study were to explore, for the first time, the longitudinal changes in the functional brain networks of PD patients over 5 years and to associate them with their cognitive performance and the lateralization of motor symptoms.

Methods

We used a 5‐year longitudinal cohort of PD patients (n = 35) who completed motor and non‐motor assessments and sequent resting state (RS) high‐density electroencephalography (HD‐EEG) recordings at three timepoints: baseline (BL), 3 years follow‐up (3YFU) and 5 years follow‐up (5YFU). We assessed disruptions in frequency‐dependent functional networks over the course of the disease and explored their relation to clinical symptomatology.

Results

In contrast with HC (n = 32), PD patients showed a gradual connectivity impairment in α2 (10‐13 Hz) and β (13–30 Hz) frequency bands. The deterioration in the global cognitive assessment was strongly correlated with the disconnected networks. These disconnected networks were also associated with the lateralization of motor symptoms, revealing a dominance of the right hemisphere in terms of impaired connections in the left‐affected PD patients in contrast to dominance of the left hemisphere in the right‐affected PD patients.

Conclusions

Taken together, our findings suggest that with disease progression, dysconnectivity in the brain networks in PD can reflect the deterioration of global cognitive deficits and the lateralization of motor symptoms. RS HD‐EEG may be an early biomarker of PD motor and non‐motor progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

Large-scale network dysfunction in α-Synucleinopathy: A meta-analysis of resting-state functional connectivity

Background

Although dysfunction of large-scale brain networks has been frequently demonstrated in patients with α-Synucleinopathy (α-Syn, i.e., Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy), a consistent pattern of dysfunction remains unclear. We aim to investigate network dysfunction in patients with α-Syn through a meta-analysis.

Methods

Whole-brain seed-based resting-state functional connectivity studies (published before September 1st, 2020 in English) comparing α-Syn patients with healthy controls (HC) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE). Seeds from each study were categorized into networks by their location within a priori functional networks. Seed-based effect size mapping with Permutation of Subject Images analysis of between-group effects identified the network systems in which α-Syn was associated with hyperconnectivity (increased connectivity in α-Syn vs. HC) or hypoconnectivity (decreased connectivity in α-Syn vs. HC) within and between each seed-network. This study was registered on PROSPERO (CRD42020210133).

Findings

In total, 136 seed-based voxel-wise resting-state functional connectivity datasets from 72 publications (3093 α-Syn patients and 3331 HC) were included in the meta-analysis. We found that α-Syn patients demonstrated imbalanced connectivity among subcortical network, cerebellum, and frontal parietal networks that involved in motor functioning and executive control. The patient group was associated with hypoconnectivity in default mode network and ventral attention network that involved in cognition and attention. Additionally, the patient group exhibited hyperconnectivity between neural systems involved in top-down emotion regulation and hypoconnectivity between networks involved in bottom-up emotion processing.

Interpretation

These findings supported neurocognitive models in which network dysfunction is tightly linked to motor, cognitive and psychiatric symptoms observed in α-Syn patients.

Effects of Exercise on Parkinson’s Disease: A Meta-Analysis of Brain Imaging Studies

Background

Exercise is increasingly recognized as a key component of Parkinson’s disease (PD) treatment strategies, but the underlying mechanism of how exercise affects PD is not yet fully understood.

Objective

The activation likelihood estimation (ALE) method is used to study the mechanism of exercise affecting PD, providing a theoretical basis for studying exercise and PD, and promoting the health of patients with PD.

Methods

Relevant keywords were searched on the PubMed, Cochrane Library, and Web of Science databases. Seven articles were finally included according to the screening criteria, with a total sample size of 97 individuals. Using the GingerALE 3.0.2 software, an ALE meta-analysis was performed using seven studies that met the requirements, and the probability of the cross-experiment activation of each voxel was calculated.

Results

The meta-analysis produced seven clusters, and major activations were found in the cerebellum, occipital lobe, parietal lobe, and frontal lobe brain regions.

Conclusion

Exercise for PD mainly results in the enhanced activation of the cerebellum, occipital lobe, parietal lobe, and frontal lobe. Exercise for PD does not cause a change in the activation of a single brain area, and the observed improvement may result from coordinated changes in multiple brain areas.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Neurogenetic traits outline vulnerability to cortical disruption in Parkinson's disease

The genetic traits that underlie vulnerability to neuronal damage across specific brain circuits in Parkinson's disease (PD) remain to be elucidated. In this study, we characterized the brain topological intersection between propagating connectivity networks in controls and PD participants and gene expression patterns across the human cortex - such as the SNCA gene. We observed that brain connectivity originated from PD-related pathology epicenters in the brainstem recapitulated the anatomical distribution of alpha-synuclein histopathology in postmortem data. We also discovered that the gene set most related to cortical propagation patterns of PD-related pathology was primarily involved in microtubule cellular components. Thus, this study sheds light on new avenues for enhancing detection of PD neuronal vulnerability via an evaluation of in vivo connectivity trajectories across the human brain and successful integration of neuroimaging-genetic strategies.

White matter changes in drug-naïve Parkinson's disease patients with impulse control & probable REM sleep behavior disorders

BACKGROUND

According to epidemiological studies, Parkinson's disease (PD) patients with probable REM sleep behavior disorder (pRBD) are more prone to develop impulse control disorders (ICDs), which is shown to be present in drug-naïve PD patients, and vice versa.

OBJECTIVES

To investigate white-matter integrity differences, with and without comorbid pRBD and ICDs.

METHODS

149 de-novo PD patients and 30 age- and gender-matched controls from the Parkinson's Progression Markers Initiative were studied. PD subjects were categorized into four groups with and without these comorbidities. We investigated the white matter integrity differences between these groups.

RESULTS

PDs with only ICDs manifested greater fractional anisotropy (FA) and lower mean diffusivity (MD) in ipsilateral cerebellar connections when compared to controls and to Parkinson's with both comorbid disorders. In contrast, significantly lower FA and higher MD in the ipsilateral fornix-stria-terminalis was observed in PDs with only pRBD compared to controls and to PDs without either comorbid disorder. Also, PDs with only pRBD manifested greater FA in contralateral putamen when compared to controls.

CONCLUSIONS

Our results suggest the presence of an underlying neural network in PDs with ICDs, particularly involving cerebellar connections, which makes the subjects susceptible to pRBD. Lower white-matter integrity in the fornix of PDs with only pRBD suggests a neuropathological pathway specific to sleep behavior disorder, independent of impulse control disorders. Greater white-matter integrity observed in PDs without comorbid ICDs, regardless of their comorbid pRBD status, might reflect compensatory mechanisms. Targeted therapies for this particular neuropathology may help prevent these comorbidities.

Disrupted dynamic pattern of regional neural activity in early-stage cognitively normal Parkinson's disease

BACKGROUND

Neuroimaging studies on Parkinson's disease (PD) mainly focus on static neural activity. However, the dynamic pattern of regional brain activity in early-stage cognitively normal PD has rarely been elucidated.

PURPOSE

To identify altered dynamic amplitude of low-frequency fluctuation (dALFF) in PD before the onset of cognitive impairment and verify its differentiating ability between patients with PD and healthy controls (HC).

MATERIAL AND METHODS

dALFF and static ALFF (sALFF) derived from functional magnetic resonance imaging data of 51 patients with PD and 50 matched HCs were analyzed. The correlations between aberrant regions and clinical performance were investigated using Spearman correlation analysis. Multivariate pattern analysis was conducted to detect the differentiating ability of both ALFF features.

RESULTS

Compared with HCs, patients with PD demonstrated reduced dALFF variance in bilateral lingual gyrus, left middle occipital gyrus, left postcentral gyrus (PcG), and right supplementary motor area (SMA); and increased dALFF variability in bilateral parahippocampal gyrus. Besides overlapping with these distributions of altered dALFF, the aberrant regions of sALFF were more extensive with decreased sALFF in the right middle temporal gyrus and right PcG, and increased sALFF in the left inferior temporal gyrus and left thalamus were observed in patients with PD. dALFF values in right SMA and left PcG were correlated with UPDRS-III scores (ρ =  -0.29, P = 0.041; ρ =  -0.33, P = 0.018, respectively).

CONCLUSION

This study provides novel insights into the neural basis underlying PD as well as the potential role of dynamic neural activity in the diagnosis and prediction of the disease.

A novel approach to understanding Parkinsonian cognitive decline using minimum spanning trees, edge cutting, and magnetoencephalography

Graph theory-based approaches are efficient tools for detecting clustering and group-wise differences in high-dimensional data across a wide range of fields, such as gene expression analysis and neural connectivity. Here, we examine data from a cross-sectional, resting-state magnetoencephalography study of 89 Parkinson’s disease patients, and use minimum-spanning tree (MST) methods to relate severity of Parkinsonian cognitive impairment to neural connectivity changes. In particular, we implement the two-sample multivariate-runs test of Friedman and Rafsky (Ann Stat 7(4):697–717, 1979) and find it to be a powerful paradigm for distinguishing highly significant deviations from the null distribution in high-dimensional data. We also generalize this test for use with greater than two classes, and show its ability to localize significance to particular sub-classes. We observe multiple indications of altered connectivity in Parkinsonian dementia that may be of future use in diagnosis and prediction.

Longitudinal brain connectivity changes and clinical evolution in Parkinson's disease

Longitudinal connectivity studies might guide our understanding of the underlying neurodegenerative processes. We report the results of a longitudinal study in patients at different stages of Parkinson's disease (PD), who performed motor and non-motor evaluations and serial resting state (RS) functional MRI (fMRI). Cluster analysis was applied to demographic and clinical data of 146 PD patients to define disease subtypes. Brain network functional alterations were assessed at baseline in PD relative to 60 healthy controls and every year for a maximum of 4 years in PD groups. Progression of brain network changes were compared between patient clusters using RS fMRI. The contribution of network changes in predicting clinical deterioration was explored. Two main PD clusters were identified: mild PD (86 patients) and moderate-to-severe PD (60 patients), with the latter group being older and having earlier onset, longer PD duration, more severe motor, non-motor and cognitive deficits. Within the mild patient cluster, two clinical subtypes were further identified: mild motor-predominant (43) and mild-diffuse (43), with the latter being older and having more frequent non-motor symptoms. Longitudinal functional connectivity changes vary across patients in different disease stages with the coexistence of hypo- and hyper-connectivity in all subtypes. RS fMRI changes were associated with motor, cognitive and non-motor evolution in PD patients. Baseline RS fMRI presaged clinical and cognitive evolution. Our network perspective was able to define trajectories of functional architecture changes according to PD stages and prognosis. RS fMRI may be an early biomarker of PD motor and non-motor progression.

Structural network topology and microstructural alterations of the anterior insula associate with cognitive and affective impairment in Parkinson's disease

The aim of the current study was to assess the structural centrality and microstructural integrity of the cortical hubs of the salience network, the anterior insular cortex (AIC) subregions and anterior cingulate cortex (ACC), and their relationship to cognitive and affective impairment in PD. MRI of 53 PD patients and 15 age-matched controls included 3D-T1 for anatomical registration, and diffusion tensor imaging for probabilistic tractography. Network topological measures of eigenvector and betweenness centrality were calculated for ventral (vAI) and dorsal (dAI) AIC. Microstructural tract integrity between vAI, dAI and the ACC was quantified with fractional anisotrophy (FA) and mean diffusivity (MD). Structural integrity and connectivity were related to cognitive and affective scores. The dAI had significantly higher eigenvector centrality in PD than controls (p < 0.01), associated with higher depression scores (left dAI only, r_s = 0.28, p < 0.05). Tracts between dAI and ACC showed lower FA and higher MD in PD (p < 0.05), and associated with lower semantic fluency, working memory and executive functioning, and higher anxiety scores (range 0.002 < p < 0.05). This study provides evidence for clinically relevant structural damage to the cortical hubs of the salience network in PD, possibly due to extensive local neuropathology and loss of interconnecting AIC-ACC tracts.

Global efficiency of the motor network is decreased in Parkinson's disease in comparison with essential tremor and healthy controls

BACKGROUND

Graph theory (GT) is a mathematical field that analyses complex networks that can be applied to neuroimaging to quantify brain's functional systems in Parkinson's disease (PD) and essential tremor (ET).

OBJECTIVES

To evaluate the functional connectivity (FC) measured by the global efficiency (GE) of the motor network in PD and compare it to ET and healthy controls (HC), and correlate it to clinical parameters.

METHODS

103 subjects (54PD, 18ET, 31HC) were submitted to structural and functional MRI. A network was designed with regions of interest (ROIs) involved in motor function, and GT was applied to determine its GE. Clinical parameters were analyzed as covariates to estimate the impact of disease severity and medication on GE.

RESULTS

GE of the motor circuit was reduced in PD in comparison with HC (p .042). Areas that most contributed to it were left supplementary motor area (SMA) and bilateral postcentral gyrus. Tremor scores correlated positively with GE of the motor network in PD subgroups. For ET, there was an increase in the connectivity of the anterior cerebellar network to the other ROIs of the motor circuit in comparison with PD.

CONCLUSIONS

FC measured by the GE of the motor network is diminished in PD in comparison with HC, especially due to decreased connectivity of left SMA and bilateral postcentral gyrus. This finding supports the theory that there is a global impairment of the motor network in PD, and it does not affect just the basal ganglia, but also areas associated with movement modulation. The ET group presented an increased connectivity of the anterior cerebellar network to the other ROIs of the motor circuit when compared to PD, which reinforces what it is known about its role in this pathology.

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis

This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

Neuro-Immune Cross-Talk in the Striatum: From Basal Ganglia Physiology to Circuit Dysfunction

The basal ganglia network is represented by an interconnected group of subcortical nuclei traditionally thought to play a crucial role in motor learning and movement execution. During the last decades, knowledge about basal ganglia physiology significantly evolved and this network is now considered as a key regulator of important cognitive and emotional processes. Accordingly, the disruption of basal ganglia network dynamics represents a crucial pathogenic factor in many neurological and psychiatric disorders. The striatum is the input station of the circuit. Thanks to the synaptic properties of striatal medium spiny neurons (MSNs) and their ability to express synaptic plasticity, the striatum exerts a fundamental integrative and filtering role in the basal ganglia network, influencing the functional output of the whole circuit. Although it is currently established that the immune system is able to regulate neuronal transmission and plasticity in specific cortical areas, the role played by immune molecules and immune/glial cells in the modulation of intra-striatal connections and basal ganglia activity still needs to be clarified. In this manuscript, we review the available evidence of immune-based regulation of synaptic activity in the striatum, also discussing how an abnormal immune activation in this region could be involved in the pathogenesis of inflammatory and degenerative central nervous system (CNS) diseases.

Current and Emerging MR Methods and Outcome in Rodent Models of Parkinson's Disease: A Review

Parkinson’s disease (PD) is a major neurodegenerative disease characterized by massive degeneration of the dopaminergic neurons in the substantia nigra pars compacta, α-synuclein-containing Lewy bodies, and neuroinflammation. Magnetic resonance (MR) imaging plays a crucial role in the diagnosis and monitoring of disease progression and treatment. A variety of MR methods are available to characterize neurodegeneration and other disease features such as iron accumulation and metabolic changes in animal models of PD. This review aims at giving an overview of how those physiopathological features of PD have been investigated using various MR methods in rodent models. Toxin-based and genetic-based models of PD are first described. MR methods for neurodegeneration evaluation, iron load, and metabolism alterations are then detailed, and the main findings are provided in those models. Ultimately, future directions are suggested for neuroinflammation and neuromelanin evaluations in new animal models.

Prefrontal network dysfunctions in rapid eye movement sleep behavior disorder

INTRODUCTION

Resting-state functional connectivity magnetic resonance imaging (rsfcMRI) of rapid eye movement (REM) sleep behavior disorder (RBD) may provide an early biomarker of α-synucleinopathy. However, few rsfcMRI studies have examined cognitive networks. To elucidate brain network changes in RBD, we performed rsfcMRI in patients with polysomnography-confirmed RBD and healthy controls (HCs), with a sufficiently large sample size in each group.

METHODS

We analyzed rsfcMRI data from 50 RBD patients and 70 age-matched HCs. Although RBD patients showed no motor signs, some exhibited autonomic and cognitive problems. Several resting-state functional networks were extracted by group independent component analysis from HCs, including the executive-control (ECN), default-mode (DMN), basal ganglia (BGN), and sensory-motor (SMN) networks. Functional connectivity (FC) was compared between groups using dual regression analysis. In the RBD group, correlation analysis was performed between FC and clinical/cognitive scales.

RESULTS

Patients with RBD showed reduced striatal-prefrontal FC in ECN, consistent with executive dysfunctions. No abnormalities were found in DMN. In the motor networks, we identified reduced midbrain-pallidum FC in BGN and reduced motor and somatosensory cortex FC in SMN.

CONCLUSION

We found abnormal ECN and normal DMN as a possible hallmark of cognitive dysfunctions in early α-synucleinopathies. We replicated abnormalities in BGN and SMN corresponding to subclinical movement disorder of RBD. RsfcMRI may provide an early biomarker of both cognitive and motor network dysfunctions of α-synucleinopathies.

Is Attention-Deficit/Hyperactivity Disorder a Risk Syndrome for Parkinson's Disease?

Recent epidemiological evidence indicates that diagnosis of attention-deficit/hyperactivity disorder (ADHD) is associated with increased risk for diseases of the basal ganglia and cerebellum, including Parkinson's disease (PD). The evidence reviewed here indicates that deficits in striatal dopamine are a shared component of the causal chains that produce these disorders. Neuropsychological studies of adult ADHD, prodromal PD, and early-stage PD reveal similar deficits in executive functions, memory, attention, and inhibition that are mediated by similar neural substrates. These and other findings are consistent with the possibility that ADHD may be part of the PD prodrome. The mechanisms that may mediate the association between PD and ADHD include neurotoxic effects of stimulants, other environmental exposures, and Lewy pathology. Understanding the nature of the association between PD and ADHD may provide insight into the etiology and pathogenesis of both disorders. The possible contribution of stimulants to this association may have important clinical and public health implications.

Functional Connectomics and Disease Progression in Drug-Naïve Parkinson's Disease Patients

BACKGROUND

Functional brain connectivity alterations may be detectable even before the occurrence of brain atrophy, indicating their potential as early markers of pathological processes.

OBJECTIVE

We aimed to determine the whole-brain network topologic organization of the functional connectome in a large cohort of drug-naïve Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging and to explore whether baseline connectivity changes may predict clinical progression.

METHODS

One hundred and forty-seven drug-naïve, cognitively unimpaired PD patients were enrolled in the study at baseline and compared to 38 age- and gender-matched controls. Non-hierarchical cluster analysis using motor and non-motor data was applied to stratify PD patients into two subtypes: 77 early/mild and 70 early/severe. Graph theory analysis and connectomics were used to assess global and local topological network properties and regional functional connectivity at baseline. Stepwise multivariate regression analysis investigated whether baseline functional imaging data were predictors of clinical progression over 2 years.

RESULTS

At baseline, widespread functional connectivity abnormalities were detected in the basal ganglia, sensorimotor, frontal, and occipital networks in PD patients compared to controls. Decreased regional functional connectivity involving mostly striato-frontal, temporal, occipital, and limbic connections differentiated early/mild from early/severe PD patients. Connectivity changes were found to be independent predictors of cognitive progression at 2-year follow-up.

CONCLUSIONS

Our findings revealed that functional reorganization of the brain connectome occurs early in PD and underlies crucial involvement of striatal projections. Connectomic measures may be helpful to identify a specific PD patient subtype, characterized by severe motor and non-motor clinical burden as well as widespread functional connectivity abnormalities. © 2021 International Parkinson and Movement Disorder Society.

The neural correlates of gait improvement by rhythmic sound stimulation in adults with Parkinson's disease - A functional magnetic resonance imaging study

INTRODUCTION

Adults with Parkinson's disease (PD) experience gait disturbances that can sometimes be improved with rhythmic auditory stimulation (RAS); however, the underlying physiological mechanism for this improvement is not well understood. We investigated brain activation patterns in adults with PD and healthy controls (HC) using functional magnetic resonance imaging (fMRI) while participants imagined gait with or without RAS.

METHODS

Twenty-seven adults with PD who could walk independently and walked more smoothly with rhythmic auditory cueing than without it, and 25 age-matched HC participated in this study. Participants imagined gait in the presence of RAS or white noise (WN) during fMRI.

RESULTS

In the PD group, gait imagery with RAS activated cortical motor areas, including supplementary motor areas and the cerebellum, while gait imagery with WN additionally recruited the left parietal operculum. In HC, the induced activation was limited to cortical motor areas and the cerebellum for both the RAS and WN conditions. Within- and between-group analyses demonstrated that RAS reduced the activity of the left parietal operculum in the PD group but not in the HC group (condition-by-group interaction by repeated measures analysis of variance, p < 0.05).

CONCLUSION

During gait imagery in adults with PD, the left parietal operculum was less activated by RAS than by WN, while no change was observed in HC, suggesting that rhythmic auditory stimulation may support the sensory-motor networks involved in gait, thus alleviating the overload of the parietal operculum and compensating for its dysfunction in these patients.

Mild Motor Signs Matter in Typical Brain Aging: The Value of the UPDRS Score Within a Functionally Intact Cohort of Older Adults

Objectives: To characterize the clinical correlates of subclinical Parkinsonian signs, including longitudinal cognitive and neural (via functional connectivity) outcomes, among functionally normal older adults.

Methods: Participants included 737 functionally intact community-dwelling older adults who performed prospective comprehensive evaluations at ~15-months intervals for an average of 4.8 years (standard deviation 3.2 years). As part of these evaluations, participants completed the Unified Parkinson's Disease Rating Scale (UPDRS) longitudinally and measures of processing speed, executive functioning and verbal episodic memory. T1-weighted structural scans and task-free functional MRI scans were acquired on 330 participants. We conducted linear mixed-effects models to determine the relationship between changes in UPDRS with cognitive and neural changes, using age, sex, and education as covariates.

Results: Cognitive outcomes were processing speed, executive functioning, and episodic memory. Greater within-person increases in UPDRS were associated with more cognitive slowing over time. Although higher average UPDRS scores were significantly associated with overall poorer executive functions, there was no association between UPDRS and executive functioning longitudinally. UPDRS scores did not significantly relate to longitudinal memory performances. Regarding neural correlates, greater increases in UPDRS scores were associated with reduced intra-subcortical network connectivity over time. There were no relationships with intra-frontoparietal or inter-subcortical-frontoparietal connectivity.

Conclusions: Our findings add to the aging literature by indicating that mild motor changes are negatively associated with cognition and network connectivity in functionally intact adults.

Dynamics of Top-Down Control and Motor Networks in Parkinson's Disease

BACKGROUND

Motor symptoms in Parkinson's disease (PD) patients might be related to high-level task-control deficits. We aimed at investigating the dynamics between sensorimotor network and top-down control networks (frontal-parietal, cingulo-opercular, and cerebellar) in PD and at determining the effects of levodopa on the dynamics of these networks.

METHODS

We investigated dynamic functional connectivity (dFC), during resting state functional magnetic resonance imaging, between sensorimotor network and top-down control networks in 36 PD patients (OFF medication, PD-OFF) and 36 healthy volunteers. We further assessed the effect of medication on dFC in18 PD patients who were also scanned ON medication.

RESULTS

The dFC analyses identified three discrete states: State I (35.68%) characterized by connections between the cerebellum and sensorimotor network, State II (34.17%) with connections between the sensorimotor and frontal-parietal network, and State III (30.15%) with connection between the sensorimotor and cingulo-opercular network. PD patients have significantly fewer occurrences and overall spent less time (shorter dwell time) in State II compared to healthy controls. After levodopa intake, dwell time improved toward normal. The change in dwell time before and after taking levodopa was negatively related to the respective changes in Unified Parkinson's Disease Rating Scale, Part III. PD-OFF showed significantly decreased connectivity between sensorimotor and control networks and increased connectivity within control networks. These changes were partially improved after levodopa intake.

CONCLUSIONS

Dopamine depletion in PD is associated with abnormalities in temporal and spatial properties between cognitive control and sensorimotor network, possibly contributing to clinical deficits. Levodopa partially restores the network function toward the values observed in healthy volunteers. © 2021 International Parkinson and Movement Disorder Society.

Aberrant Amplitude of Low-Frequency Fluctuations in Different Frequency Bands in Patients With Parkinson's Disease

Previous studies reported abnormal spontaneous neural activity in Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging (R-fMRI). However, the frequency-dependent neural activity in PD is largely unknown. Here, 35 PD patients and 35 age- and education-matched healthy controls (HCs) underwent R-fMRI scanning to investigate abnormal spontaneous neural activity of PD using the amplitude of low-frequency fluctuation (ALFF) approach within the conventional band (typical band: 0.01-0.08 Hz) and specific frequency bands (slow-5: 0.010-0.027 Hz and slow-4: 0.027-0.073 Hz). Compared with HCs, PD patients exhibited increased ALFF in the parieto-temporo-occipital regions, such as the bilateral inferior temporal gyrus/fusiform gyrus (ITG/FG) and left angular gyrus/posterior middle temporal gyrus (AG/pMTG), and displayed decreased ALFF in the left cerebellum, right precuneus, and left postcentral gyrus/supramarginal gyrus (PostC/SMG) in the typical band. PD patients showed greater increased ALFF in the left caudate/putamen, left anterior cingulate cortex/medial superior frontal gyrus (ACC/mSFG), left middle cingulate cortex (MCC), right ITG, and left hippocampus, along with greater decreased ALFF in the left pallidum in the slow-5 band, whereas greater increased ALFF in the left ITG/FG/hippocampus accompanied by greater decreased ALFF in the precentral gyrus/PostC was found in the slow-4 band (uncorrected). Additionally, the left caudate/putamen was positively correlated with levodopa equivalent daily dose (LEDD), Hoehn and Yahr (HY) stage, and disease duration. Our results suggest that PD is related to widespread abnormal brain activities and that the abnormalities of ALFF in PD are associated with specific frequency bands. Future studies should take frequency band effects into account when examining spontaneous neural activity in PD.

Infuse-as-you-go convective delivery to enhance coverage of elongated brain targets: technical note

OBJECTIVE

To develop and assess a convective delivery technique that enhances the effectiveness of drug delivery to nonspherical brain nuclei, the authors developed an occipital "infuse-as-you-go" approach to the putamen and compared it to the currently used transfrontal approach.

METHODS

Eleven nonhuman primates received a bilateral putamen injection of adeno-associated virus with 2 mM gadolinium-DTPA by real-time MR-guided convective perfusion via either a transfrontal (n = 5) or occipital infuse-as-you-go (n = 6) approach.

RESULTS

MRI provided contemporaneous assessment and monitoring of putaminal infusions for transfrontal (2 to 3 infusion deposits) and occipital infuse-as-you-go (stepwise infusions) putaminal approaches. The infuse-as-you-go technique was more efficient than the transfrontal approach (mean 35 ± 1.1 vs 88 ± 8.3 minutes [SEM; p < 0.001]). More effective perfusion of the postcommissural and total putamen was achieved with the infuse-as-you-go versus transfronatal approaches (100-µl infusion volumes; mean posterior commissural coverage 76.2% ± 5.0% vs 32.8% ± 2.9% [p < 0.001]; and mean total coverage 53.5% ± 3.0% vs 38.9% ± 2.3% [p < 0.01]).

CONCLUSIONS

The infuse-as-you-go approach, paralleling the longitudinal axis of the target structure, provides a more effective and efficient method for convective infusate coverage of elongated, irregularly shaped subcortical brain nuclei.

Functional Connectivity Signatures of Parkinson's Disease

 Resting-state functional magnetic resonance imaging (RS-fMRI) studies have been extensively applied to analyze the pathophysiology of neurodegenerative disorders such as Parkinson’s disease (PD). In the present narrative review, we attempt to summarize the most recent RS-fMRI findings highlighting the role of brain networks re-organization and adaptation in the course of PD. We also discuss limitations and potential definition of early functional connectivity signatures to track and predict future PD progression. Understanding the neural correlates and potential predisposing factors of clinical progression and complication will be crucial to guide novel clinical trials and to foster preventive strategies.