Resting state functional connectivity of the striatum in Parkinson's disease

A stacking classifier for distinguishing stages of Alzheimer's disease from a subnetwork perspective

Accurately distinguishing stages of Alzheimer's disease (AD) is crucial for diagnosis and treatment. In this paper, we introduce a stacking classifier method that combines six single classifiers into a stacking classifier. Using brain network models and network metrics, we employ t-tests to identify abnormal brain regions, from which we construct a subnetwork and extract its features to form the training dataset. Our method is then applied to the ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets, categorizing the stages into four categories: Alzheimer's disease, mild cognitive impairment (MCI), mixed Alzheimer's mild cognitive impairment (ADMCI), and healthy controls (HCs). We investigate four classification groups: AD-HCs, AD-MCI, HCs-ADMCI, and HCs-MCI. Finally, we compare the classification accuracy between a single classifier and our stacking classifier, demonstrating superior accuracy with our stacking classifier from a subnetwork-based viewpoint.

Resting-state functional MRI metrics to detect freezing of gait in Parkinson's disease: a machine learning approach

Among the symptoms that can occur in Parkinson's disease (PD), Freezing of Gait (FOG) is a disabling phenomenon affecting a large proportion of patients, and it remains not fully understood. Accurate classification of FOG in PD is crucial for tailoring effective interventions and is necessary for a better understanding of its underlying mechanisms. In the present work, we applied four Machine Learning (ML) classifiers (Decision Tree - DT, Random Forest - RF, Multilayer Perceptron - MLP, Logistic Regression - LOG) to different four metrics derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data processing to assess their accuracy in automatically classifying PD patients based on the presence or absence of Freezing of Gait (FOG). To validate our approach, we applied the same methodologies to distinguish PD patients from a group of Healthy Subject (HS). The performance of the four ML algorithms was validated by repeated k-fold cross-validation on randomly selected independent training and validation subsets. The results showed that when discriminating PD from HS, the best performance was achieved using RF applied to fractional Amplitude of Low-Frequency Fluctuations (fALFF) data (AUC 96.8 ± 2 %). Similarly, when discriminating PD-FOG from PD-nFOG, the RF algorithm was again the best performer on all four metrics, with AUCs above 90 %. Finally, trying to unbox how AI system black-box choices were made, we extracted features' importance scores for the best-performing method(s) and discussed them based on the results obtained to date in rs-fMRI studies on FOG in PD and, more generally, in PD. In summary, regions that were more frequently selected when differentiating both PD from HS and PD-FOG from PD-nFOG patients were mainly relevant to the extrapyramidal system, as well as visual and default mode networks. In addition, the salience network and the supplementary motor area played an additional major role in differentiating PD-FOG from PD-nFOG patients.

Basal ganglia connectivity and network asymmetry in Parkinson's disease: A resting-state fMRI study

This study investigates the impact of basal ganglia network asymmetry on motor function in Parkinson's Disease (PD). Using resting-state functional magnetic resonance imaging (rs-fMRI), functional connectivity and network asymmetry were analyzed in 15 non-demented PD patients and 15 healthy controls. Sixteen basal ganglia substructures, including the caudate, putamen, and globus pallidus, were selected for a unified analysis of variance framework to evaluate inter-hemispheric connectivity differences. After spatial preprocessing, regions of interest were defined, and time-series data were extracted for functional connectivity and network asymmetry analysis. The results revealed significant alterations in the functional connectivity of the caudate, putamen, and nucleus accumbens (NAc) in PD patients. Notably, the absence of intra-network asymmetry in the left NAc and bilateral amygdala correlated with motor dysfunction, likely due to overactivity of the inhibitory indirect pathway. Furthermore, pronounced asymmetry in the left putamen and right frontal gyrus suggested a compensatory neural mechanism supporting motor performance. These findings highlight the critical role of basal ganglia network asymmetry in the pathophysiology of PD. The identified asymmetry characteristics may serve as potential biomarkers for early diagnosis and disease progression monitoring, offering new directions for targeted therapeutic interventions.

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.

Recent advances of transcranial electrical stimulation in healthy aging and Parkinson's disease: Effects on dual tasking

Dual tasking involves the simultaneous execution of two actions. In the context of healthy aging and neurodegenerative disorders, such as Parkinson's disease (PD) engagement in dual tasking frequently results in impaired gait or upper limb performance, thereby affecting functional independence. Transcranial electrical stimulation is a non-invasive technique able to modulate brain activity, which might represent a potential tool for reducing dual task interference. The goal of this review is to provide a comprehensive summary of the most recent findings about the use of transcranial electrical stimulation in improving dual tasking in the elderly and people with PD, including considerations about the optimal stimulation parameters. Differences in terms of stimulation protocols emerged across the included studies. Among transcranial electrical stimulation techniques, transcranial direct current stimulation (tDCS) was the most frequently employed. Currently, using tDCS to target dorsolateral prefrontal cortex either alone or in a multi-site fashion, along with a concurrent complex task, appears to be the most promising method for reducing dual task interference. Nevertheless, the lack of control over interindividual variability, the heterogeneity in outcome measures assessing dual tasking, and the variations in protocol elements like the frequency and the number of sessions prevented us from drawing definitive conclusions about the best paradigm.

Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks

The striatum, a crucial part of the basal ganglia, plays a key role in various brain functions through its interactions with the cortex. The complex structural and functional diversity across subdivisions within the striatum highlights the necessity for precise striatal segmentation. In this study, we introduce a novel deep clustering pipeline for automated, fine-scale parcellation of the striatum using diffusion MRI (dMRI) tractography. Initially, we employ a voxel-based probabilistic fiber tractography algorithm combined with a fiber-tract embedding technique to capture intricate dMRI connectivity patterns. To maintain critical inter-voxel relationships, our approach employs Graph Neural Networks (GNNs) to create accurate graph representations of the striatum. This involves encoding probabilistic fiber bundle characteristics as node attributes and refining edge weights using activation functions to enhance the graph's interpretability and accuracy. The methodology incorporates a Transformer-based GraphConv autoencoder in the pre-training phase to extract critical spatial features while minimizing reconstruction loss. In the fine-tuning phase, a novel joint loss mechanism markedly improves segmentation precision and anatomical fidelity. Integration of traditional clustering techniques with multi-head self-attention mechanisms further elevates the accuracy and robustness of our segmentation approach. This methodology provides new insights into the striatum's role in cognition and behavior and offers potential clinical applications for neurological disorders.

Radiomics-based Modelling Unveils Cerebellar Involvement in Parkinson's Disease

Emerging pathological and neurophysiological evidence has highlighted the cerebellum's involvement in Parkinson's disease (PD). This study aimed to explore the potential of cerebellum-derived magnetic resonance imaging (MRI) radiomics in distinguishing PD patients from healthy controls (HC). A retrospective analysis was conducted using three-dimensional-T1 MRI data (n= 374) from the Parkinson's Progression Markers Initiative (PPMI) dataset (n= 204) and an independent in-house cohort (n= 170). Radiomic features (n= 883) were extracted from the cerebellar gray and white matter of each individual. Three machine learning models were developed: a cerebellar gray matter model, a cerebellar white matter model, and a combined gray and white matter model, to classify PD patients and HC. The results showed that the cerebellar gray matter model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.931 in the training set, with a sensitivity of 60.8% and specificity of 97.1%, while in the testing set, it obtained an AUC of 0.874, with a sensitivity of 86.1% and specificity of 62.6%. The white matter-based model demonstrated an AUC of 0.846 (sensitivity, 59.8%; specificity, 87.3%) in the training set and an AUC of 0.868 (sensitivity, 81.0%; specificity, 75.8%) in the testing set. Notably, the combined gray and white matter model exhibited superior performance, achieving an AUC of 0.936 (sensitivity, 65.7%; specificity, 96.1%) in the training set and an AUC of 0.881 (sensitivity, 82.3%; specificity, 63.7%) in the testing set. Key radiomic features contributing to PD classification included Gray-level Dependence Matrix, Gray-level Co-occurrence Matrix and First-Order from gray matter, as well as Gray-level Size Zone Matrix from white matter, highlighting significant radiomics changes in the cerebellum associated with PD. In conclusion, this study demonstrates that MRI radiomics of cerebellar gray and white matter can effectively differentiate PD patients from HC, supporting the cerebellum's pivotal role in PD pathology and its potential as an imaging biomarker for PD.

Individualized brain radiomics-based network tracks distinct subtypes and abnormal patterns in prodromal Parkinson's disease

Individuals in the prodromal phase of Parkinson's disease (PD) exhibit significant heterogeneity and can be divided into distinct subtypes based on clinical symptoms, pathological mechanisms, and brain network patterns. However, little has been done regarding the valid subtyping of prodromal PD, which hinders the early diagnosis of PD. Therefore, we aimed to identify the subtypes of prodromal PD using the brain radiomics-based network and examine the unique patterns linked to the clinical presentations of each subtype. Individualized brain radiomics-based network was constructed for normal controls (NC; N = 110), prodromal PD patients (N = 262), and PD patients (N = 108). A data-driven clustering approach using the radiomics-based network was carried out to cluster prodromal PD patients into higher-/lower-risk subtypes. Then, the dissociated patterns of clinical manifestations, anatomical structure alterations, and gene expression between these two subtypes were evaluated. Clustering findings indicated that one prodromal PD subtype closely resembled the pattern of NCs (N-P; N = 159), while the other was similar to the pattern of PD (P-P; N = 103). Significant differences were observed between the subtypes in terms of multiple clinical measurements, neuroimaging for morphological changes, and gene enrichment for synaptic transmission. Identification of prodromal PD subtypes based on brain connectomes and a full understanding of heterogeneity at this phase could inform early and accurate PD diagnosis and effective neuroprotective interventions.

Striatal-cortical dysconnectivity underlies somatosensory deficits in Parkinson's disease: Insights from rhythmic auditory-motor training

Evidence indicates that neurodegenerative diseases spread through distinct brain networks. For Parkinson's disease (PD), somatosensory abnormalities may accompany motor dysfunction in early disease stages when dopaminergic degeneration is limited to the basal ganglia. It remains unclear whether, based on the network-spread account, these abnormalities emanated from aberrant functional connectivity with the basal ganglia, and whether interventions normalizing this connectivity could reverse these abnormalities. Here, we employed functional MRI to record brain responses to tactile stimuli in patients with idiopathic PD and healthy controls before and after three-week rhythmic auditory stimulation-assisted gait (RASg) training. Consistent with the presence of striatal degeneration, patients showed right posterior putamen (pPut) hypoactivation when detecting tactile stimuli of their left leg. They also exhibited reduced functional connectivity from the right pPut to the right parietal somatosensory region (inferior parietal lobule, IPL), whose hypoactivation reflected patients' impaired tactile detectability. Importantly, this dysconnectivity predicted right IPL hypoactivation, indicating that pPut-IPL dysconnectivity underlay patients' impaired tactile detectability. Intriguingly, RASg training normalized patients' tactile detectability, which was mirrored by normalization of right IPL activation and pPut-IPL connectivity. Training-induced changes in pPut-IPL connectivity predicted changes in IPL activation during tactile detection, reinforcing the role of pPut-IPL connectivity in patients' tactile detectability. These findings suggest that somatosensory abnormalities in PD may arise from the spread of striatal pathology to relevant cortical regions. Rhythmic auditory-motor training acts to recover striatal connectivity, improving PD patients' somatosensory deficits.

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.

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.

Cerebellar transcranial magnetic stimulation improves motor function in Parkinson's disease

OBJECTIVE

To determine whether an accelerated protocol of 48 Hz cerebellar repetitive transcranial magnetic stimulation results in improved motor function in individuals with Parkinson's disease.

METHODS

In this double-blind randomized sham-controlled study, 35 individuals with Parkinson's disease and stable medical treatment were randomized to either sham or verum transcranial magnetic stimulation. The stimulation was applied bilaterally and medial over the cerebellum and comprised a novel accelerated protocol encompassing two sessions per day on 5 consecutive days. Patients were assessed at baseline, on day 5 after the last stimulation and 1 month post intervention. Measurements included dynamic posturography, UPDRS III, 8-Meter walk test, and Timed Up and Go test.

RESULTS

The accelerated protocol was safe and feasible in an outpatient setting. Patients in the verum group showed significant improvement (p < 0.001) of motor symptoms as measured in the UPDRS III. Improvement was mainly carried by the domains rigor, bradykinesia, and gait and persisted after 1 month (p = 0.009), whereas tremor remained unchanged.

INTERPRETATION

The effect of a high-dose transcranial magnetic stimulation in patients with Parkinson's disease is encouraging and comparable to other studies using much longer stimulation protocols. This short-term intervention of 5 days facilitates the future application in an outpatient setting. Reduction in hospitalization rates directly benefits patients with motor impairment.

Aberrant dynamic functional network connectivity in progressive supranuclear palsy

BACKGROUND

The clinical symptoms of progressive supranuclear palsy (PSP) may be mediated by aberrant dynamic functional network connectivity (dFNC). While earlier research has found altered functional network connections in PSP patients, the majority of those studies have concentrated on static functional connectivity. Nevertheless, in this study, we sought to evaluate the modifications in dynamic characteristics and establish the correlation between these disease-related changes and clinical variables.

METHODS

In our study, we conducted a study on 53 PSP patients and 65 normal controls. Initially, we employed a group independent component analysis (ICA) to derive resting-state networks (RSNs), while employing a sliding window correlation approach to produce dFNC matrices. The K-means algorithm was used to cluster these matrices into distinct dynamic states, and then state analysis was subsequently employed to analyze the dFNC and temporal metrics between the two groups. Finally, we made a correlation analysis.

RESULTS

PSP patients showed increased connectivity strength between medulla oblongata (MO) and visual network (VN) /cerebellum network (CBN) and decreased connections were found between default mode network (DMN) and VN/CBN, subcortical cortex network (SCN) and CBN. In addition, PSP patients spend less fraction time and shorter dwell time in a diffused state, especially the MO and SCN. Finally, the fraction time and mean dwell time in the distributed connectivity state (state 2) is negatively correlated with duration, bulbar and oculomotor symptoms.

DISCUSSION

Our findings were that the altered connectivity was mostly concentrated in the CBN and MO. In addition, PSP patients had different temporal dynamics, which were associated with bulbar and oculomotor symptoms in PSPRS. It suggest that variations in dynamic functional network connectivity properties may represent an essential neurological mechanism in PSP.

The functional brain connectome in isolated rapid eye movement sleep behavior disorder and Parkinson's disease

BACKGROUND

Isolated rapid-eye-movement behavior disorder (iRBD) often precedes the development of alpha-synucleinopathies such as Parkinson's disease (PD). Magnetic resonance imaging (MRI) studies have revealed structural brain alterations in iRBD partially resembling those observed in PD. However, relatively little is known about whole-brain functional brain alterations in iRBD. Here, we characterize the functional brain connectome of iRBD compared with PD patients and healthy controls (HC) using resting-state functional MRI (rs-fMRI).

METHODS

Eighteen iRBD subjects (67.3 ± 6.6 years), 18 subjects with PD (65.4 ± 5.8 years), and 39 age- and sex-matched HC (64.4 ± 9.2 years) underwent rs-fMRI at 3 T. We applied a graph theoretical approach to analyze the brain functional connectome at the global and regional levels. Data were analyzed using both frequentist and Bayesian statistics.

RESULTS

Global connectivity was largely preserved in iRBD and PD individuals. In contrast, both disease groups displayed altered local connectivity mainly in the motor network, temporal cortical regions including the limbic system, and the visual system. There were some group specific alterations, and connectivity changes were pronounced in PD individuals. Overall, however, there was a good agreement of the connectome changes observed in both disease groups.

CONCLUSIONS

This study provides evidence for widespread functional brain connectivity alterations in iRBD, including motor circuitry, despite normal motor function. Connectome alterations showed substantial resemblance with those observed in PD, underlining a close pathophysiological relationship of iRBD and PD.

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].

Microstructural white matter changes underlying speech deficits in Parkinson's disease

Speech impairments are one of the common symptoms of individuals with Parkinson's disease (PD). However, little is known about the underlying neuroanatomical structural deficits specifically in the basal ganglia-thalamocortical (BGTC) loop in the speech deficits of PD. Here we investigated white matter differences in PD using probabilistic tractography. Diffusion tensor imaging data were downloaded from the Parkinson's Progression Markers Initiative database. We included three groups of participants: 20 PD individuals with speech deficits, 20 PD individuals without speech deficits, and 20 age- and gender-matched control participants. Overall, PD individuals with speech deficits had higher mean diffusivity in the BGTC pathway in the left hemisphere compared with PD individuals without speech deficits. The present study exhibits that there may be a distinct pathophysiological profile of white matter for speech deficits in PD.

How should we be using biomarkers in trials of disease modification in Parkinson's disease?

The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.

Bradykinesia and rigidity modulated by functional connectivity between the primary motor cortex and globus pallidus in Parkinson's disease

The mechanisms underlying motor fluctuations in patients with Parkinson's disease (PD) are currently unclear. Regional brain stimulation reported the changing of motor symptoms, but the correlation with functional connectivity (FC) in the brain network is not fully understood. Hence, our study aimed to explore the relationship between motor symptom severity and FC using resting-state functional magnetic resonance imaging (rsfMRI) in the "on" and "off" states of PD. In 26 patients with sporadic PD, FC was assessed using rsfMRI, and clinical severity was analyzed using the motor part of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS Part III) in the on and off states. Correlations between FC values and MDS-UPDRS Part III scores were assessed using Pearson's correlation coefficient. The correlation between FC and motor symptoms differed in the on and off states. FC between the ipsilateral precentral gyrus (PreCG) and globus pallidus (GP) correlated with the total MDS-UPDRS Part III scores and those for bradykinesia/rigidity in the off state. Lateralization analysis indicated that FC between the PreCG and GP correlated with the contralateral total MDS-UPDRS Part III scores and those for bradykinesia/rigidity in the off state. Aberrant FC in cortico-striatal circuits correlated with the severity of motor symptoms in PD. Cortico-striatal hyperconnectivity, particularly in motor pathways involving PreCG and GP, is related to motor impairments in PD. These findings may facilitate our understanding of the mechanisms underlying motor symptoms in PD and aid in developing treatment strategies such as brain stimulation for motor impairment.

Motor networks, but also non-motor networks predict motor signs in Parkinson's disease

OBJECTIVE

Investigate the brain functional networks associated with motor impairment in people with Parkinson's disease (PD).

BACKGROUND

PD is primarily characterized by motor dysfunction. Resting-state functional connectivity (RsFC) offers a unique opportunity to non-invasively characterize brain function. In this study, we hypothesized that the motor dysfunction observed in people with PD involves atypical connectivity not only in motor but also in higher-level attention networks. Understanding the interaction between motor and non-motor RsFC that are related to the motor signs could provide insights into PD pathophysiology.

METHODS

We used data from 88 people with PD (mean age: 68.2(SD:10), 55 M/33F) coming from 2 cohorts. Motor severity was assessed in practical OFF-medication state, using MDS-UPDRS Part-III motor scores (mean: 49 (SD:10)). RsFC was characterized using an atlas of 384 regions that were grouped into 13 functional networks. Associations between RsFC and motor severity were assessed independently for each RsFC using predictive modeling.

RESULTS

The top 5 % models that predicted the MDS-UPDRS-III motor scores with effect size >0.5 were the connectivity between (1) the somatomotor and Subcortical-Basal-ganglia, (2) somatomotor and Visual and (3) CinguloOpercular (CiO) and language/Ventral attention (Lan/VeA) network pairs.

DISCUSSION

Our findings suggest that, along with motor networks, visual- and attention-related cortical networks are also associated with the motor symptoms of PD. Non-motor networks may be involved indirectly in motor-coordination. When people with PD have deficits in motor networks, more attention may be needed to carry out formerly automatic motor functions, consistent with compensatory mechanisms in parkinsonian movement disorders.

Linking the cerebellum to Parkinson disease: an update

Parkinson disease (PD) is characterized by heterogeneous motor and non-motor symptoms, resulting from neurodegeneration involving various parts of the central nervous system. Although PD pathology predominantly involves the nigral-striatal system, growing evidence suggests that pathological changes extend beyond the basal ganglia into other parts of the brain, including the cerebellum. In addition to a primary involvement in motor control, the cerebellum is now known to also have an important role in cognitive, sleep and affective processes. Over the past decade, an accumulating body of research has provided clinical, pathological, neurophysiological, structural and functional neuroimaging findings that clearly establish a link between the cerebellum and PD. This Review presents an overview and update on the involvement of the cerebellum in the clinical features and pathogenesis of PD, which could provide a novel framework for a better understanding the heterogeneity of the disease.

＜Editors' Choice＞ Pattern of THK 5351 retention in normal aging involves core regions of resting state networks associated with higher cognitive function

We aimed to elucidate the distribution pattern of the positron emission tomography probe [18F]THK 5351, a marker for astrogliosis and tau accumulation, in healthy aging. We also assessed the relationship between THK5351 retention and resting state networks. We enrolled 62 healthy participants in this study. All participants underwent magnetic resonance imaging/positron emission tomography scanning consisting of T1-weighted images, resting state functional magnetic resonance imaging, Pittsburgh Compound-B and THK positron emission tomography. The preprocessed THK images were entered into a scaled subprofile modeling/principal component analysis to extract THK distribution patterns. Using the most significant THK pattern, we generated regions of interest, and performed seed-based functional connectivity analyses. We also evaluated the functional connectivity overlap ratio to identify regions with high between-network connectivity. The most significant THK distributions were observed in the medial prefrontal cortex and bilateral putamen. The seed regions of interest in the medial prefrontal cortex had a functional connectivity map that significantly overlapped with regions of the dorsal default mode network. The seed regions of interest in the putamen showed strong overlap with the basal ganglia and anterior salience networks. The functional connectivity overlap ratio also showed that three peak regions had the characteristics of connector hubs. We have identified an age-related spatial distribution of THK in the medial prefrontal cortex and basal ganglia in normal aging. Interestingly, the distribution's peaks are located in regions of connector hubs that are strongly connected to large-scale resting state networks associated with higher cognitive function.

Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

Homogeneous-Multiset-CCA-Based Brain Covariation and Contravariance Connectivity Network Modeling

Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to provide a new strategy for regional activity representation and brain connectivity modeling, a novel homogeneous multiset canonical correlation analysis (HMCCA) model is proposed, which enforces sign constraints on the weights of voxels to guarantee homogeneity within each brain region. It is capable of obtaining regional representative signals and constructing covariation and contravariance networks simultaneously, at both group and subject levels. Validations on two sessions of fMRI data verified its reproducibility and reliability when dealing with brain connectivity networks. Further experiments on subjects with and without Parkinson's disease (PD) revealed significant alterations in brain connectivity patterns, which were further associated with clinical scores and demonstrated superior prediction ability, indicating its potential in clinical practice.

Parkinson's disease progression: Increasing expression of an invariant common core subnetwork

Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.

Continuous theta burst stimulation over right cerebellum for speech impairment in Parkinson's disease: study protocol for a randomized, sham-controlled, clinical trial

Background

Speech impairment is a common symptom of Parkinson's disease (PD) that worsens with disease progression and affects communication and quality of life. Current pharmacological and surgical treatments for PD have inconsistent effects on speech impairment. The cerebellum is an essential part of sensorimotor network that regulates speech production and becomes dysfunctional in PD. Continuous theta-burst stimulation (cTBS) is a non-invasive brain stimulation technique that can modulate the cerebellum and its connections with other brain regions.

Objective

To investigate whether cTBS over the right cerebellum coupled with speech-language therapy (SLT) can improve speech impairment in PD.

Methods

In this randomized controlled trial (RCT), 40 patients with PD will be recruited and assigned to either an experimental group (EG) or a control group (CG). Both groups will receive 10 sessions of standard SLT. The EG will receive real cTBS over the right cerebellum, while the CG will receive sham stimulation. Blinded assessors will evaluate the treatment outcome at three time points: pre-intervention, post-intervention, and at a 12-week follow-up. The primary outcome measures are voice/speech quality and neurobehavioral parameters of auditory-vocal integration. The secondary outcome measures are cognitive function, quality of life, and functional connectivity determined by resting-state functional magnetic resonance imaging (fMRI).

Significance

This trial will provide evidence for the efficacy and safety of cerebellar cTBS for the treatment of speech impairment in PD and shed light on the neural mechanism of this intervention. It will also have implications for other speech impairment attributed to cerebellar dysfunctions.

Clinical trial registration

www.chictr.org.cn, identifier ChiCTR2100050543.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

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.

Targeted neurostimulation reverses a spatiotemporal biomarker of treatment-resistant depression

Major depressive disorder (MDD) is widely hypothesized to result from disordered communication across brain-wide networks. Yet, prior resting-state-functional MRI (rs-fMRI) studies of MDD have studied zero-lag temporal synchrony (functional connectivity) in brain activity absent directional information. We utilize the recent discovery of stereotyped brain-wide directed signaling patterns in humans to investigate the relationship between directed rs-fMRI activity, MDD, and treatment response to FDA-approved neurostimulation paradigm termed Stanford neuromodulation therapy (SNT). We find that SNT over the left dorsolateral prefrontal cortex (DLPFC) induces directed signaling shifts in the left DLPFC and bilateral anterior cingulate cortex (ACC). Directional signaling shifts in the ACC, but not the DLPFC, predict improvement in depression symptoms, and moreover, pretreatment ACC signaling predicts both depression severity and the likelihood of SNT treatment response. Taken together, our findings suggest that ACC-based directed signaling patterns in rs-fMRI are a potential biomarker of MDD.

Altered Spinal Cord Functional Connectivity Associated with Parkinson's Disease Progression

BACKGROUND

Parkinson's disease (PD) has traditionally been viewed as an α-synucleinopathy brain pathology. Yet evidence based on postmortem human and animal experimental models indicates that the spinal cord may also be affected.

OBJECTIVE

Functional magnetic resonance imaging (fMRI) seems to be a promising candidate to better characterize spinal cord functional organization in PD patients.

METHODS

Resting-state spinal fMRI was performed in 70 PD patients and 24 age-matched healthy controls, the patients being divided into three groups based on their motor symptom severity: PD_low (n = 24), PD_med (n = 22), and PD_adv (n = 24) groups. A combination of independent component analysis (ICA) and a seed-based approach was applied.

RESULTS

When pooling all participants, the ICA revealed distinct ventral and dorsal components distributed along the rostro-caudal axis. This organization was highly reproducible within subgroups of patients and controls. PD severity, assessed by Unified Parkinson's Disease Rating Scale (UPDRS) scores, was associated with a decrease in spinal functional connectivity (FC). Notably, we observed a reduced intersegmental correlation in PD as compared to controls, the latter being negatively associated with patients' upper-limb UPDRS scores (P = 0.0085). This negative association between FC and upper-limb UPDRS scores was significant between adjacent C4-C5 (P = 0.015) and C5-C6 (P = 0.20) cervical segments, levels associated with upper-limb functions.

CONCLUSIONS

The present study provides the first evidence of spinal cord FC changes in PD and opens new avenues for the effective diagnosis and therapeutic strategies in PD. This underscores how spinal cord fMRI can serve as a powerful tool to characterize, in vivo, spinal circuits for a variety of neurological diseases. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, as differences can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

Resting-state fMRI study on drug-naïve early-stage patients with Parkinson's disease and with fatigue

INTRODUCTION

Fatigue is one of the most common and debilitating non-motor symptoms in patients with Parkinson's disease (PD), which could manifest during the early stage of the disease and persist through the disease course. However, the treatment options for fatigue remain limited for patients with PD.

METHODS

Using seed-based resting-state functional magnetic resonance imaging, we explored the fatigue-related functional deficiencies in the anterior caudate nucleus, anterior putamen, and posterior putamen in a cohort of early-stage drug-naïve patients with PD. Thirty-eight patients with PD, 19 with and 19 without fatigue, and 31 matched healthy controls were selected. The fatigue status was defined based on the score obtained from the fatigue severity scale (FSS).

RESULTS

Patients with PD with fatigue exhibited a decreased connectivity in the cerebellar-striatal, cortico-striatal, and mesolimbic-striatal loops. No increased functional connectivity was observed. The abnormal connections of the dorsal striatum subdivisions overlapped to extensive brain regions, including the cerebellum, inferior frontal gyrus, inferior temporal gyrus, lingual gyrus, rolandic operculum, insular, and hippocampus.

CONCLUSIONS

Our findings revealed that the widespread functional deficiency in the striatal-cerebellar-cerebral cortical network may be critical to the pathology underlying fatigue in the early-stage PD. The key feature of fatigue-related connectivity was observed between the caudate nucleus and the cerebellum, which could serve as a potential biomarker or treatment target for fatigue in early-stage patients with PD in future studies.

Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment

The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source-reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross-validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.

Topological patterns of motor networks in Parkinson’s disease with different sides of onset: A resting-state-informed structural connectome study

Parkinson’s disease (PD) has a characteristically unilateral pattern of symptoms at onset and in the early stages; this lateralization is considered a diagnostically important diagnosis feature. We aimed to compare the graph-theoretical properties of whole-brain networks generated by using resting-state functional MRI (rs-fMRI), diffusion tensor imaging (DTI), and the resting-state-informed structural connectome (rsSC) in patients with left-onset PD (LPD), right-onset PD (RPD), and healthy controls (HCs). We recruited 26 patients with PD (13 with LPD and 13 with RPD) as well as 13 age- and sex-matched HCs. Rs-fMRI and DTI were performed in all subjects. Graph-theoretical analysis was used to calculate the local and global efficiency of a whole-brain network generated by rs-fMRI, DTI, and rsSC. Two-sample t-tests and Pearson correlation analysis were conducted. Significantly decreased global and local efficiency were revealed specifically in LPD patients compared with HCs when the rsSC network was used; no significant intergroup difference was found by using rs-fMRI or DTI alone. For rsSC network analysis, multiple network metrics were found to be abnormal in LPD. The degree centrality of the left precuneus was significantly correlated with the Unified Parkinson’s Disease Rating Scale (UPDRS) score and disease duration (p = 0.030, r = 0.599; p = 0.037, r = 0.582). The topological properties of motor-related brain networks can differentiate LPD and RPD. Nodal metrics may serve as important structural features for PD diagnosis and monitoring of disease progression. Collectively, these findings may provide neurobiological insights into the lateralization of PD onset.

Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson's disease

INTRODUCTION. Deep brain stimulation (DBS) is a life-changing treatment for patients with Parkinson's disease (PD) and gives the unique opportunity to directly explore how basal ganglia work. Despite the rapid technological innovation of the last years, the untapped potential of DBS is still high.

AREAS COVERED. This review summarizes the developments in the mechanistic understanding of DBS and the potential clinical applications of cutting-edge technological advances. Rather than a univocal local mechanism, DBS exerts its therapeutic effects through several multimodal mechanisms and involving both local and network-wide structures, although crucial questions remain unexplained. Nonetheless, new insights in mechanistic understanding of DBS in PD have provided solid bases for advances in preoperative selection phase, prediction of motor and non-motor outcomes, leads placement and postoperative stimulation programming.

EXPERT OPINION. DBS has not only strong evidence of clinical effectiveness in PD treatment, but technological advancements are revamping its role of neuromodulation of brain circuits and key to better understanding PD pathophysiology. In the next few years, the worldwide use of new technologies in clinical practice will provide large data to elucidate their role and to expand their applications for PD patients, providing useful insights to personalize DBS treatment and follow-up.

Multifunctional role of natural products for the treatment of Parkinson's disease: At a glance

Natural substances originating from plants have long been used to treat neurodegenerative disorders (NDs). Parkinson's disease (PD) is a ND. The deterioration and subsequent cognitive impairments of the midbrain nigral dopaminergic neurons distinguish by this characteristic. Various pathogenic mechanisms and critical components have been reported, despite the fact that the origin is unknown, such as protein aggregation, iron buildup, mitochondrial dysfunction, neuroinflammation and oxidative stress. Anti-Parkinson drugs like dopamine (DA) agonists, levodopa, carbidopa, monoamine oxidase type B inhibitors and anticholinergics are used to replace DA in the current treatment model. Surgery is advised in cases where drug therapy is ineffective. Unfortunately, the current conventional treatments for PD have a number of harmful side effects and are expensive. As a result, new therapeutic strategies that control the mechanisms that contribute to neuronal death and dysfunction must be addressed. Natural resources have long been a useful source of possible treatments. PD can be treated with a variety of natural therapies made from medicinal herbs, fruits, and vegetables. In addition to their well-known anti-oxidative and anti-inflammatory capabilities, these natural products also play inhibitory roles in iron buildup, protein misfolding, the maintenance of proteasomal breakdown, mitochondrial homeostasis, and other neuroprotective processes. The goal of this research is to systematically characterize the currently available medications for Parkinson's and their therapeutic effects, which target diverse pathways. Overall, this analysis looks at the kinds of natural things that could be used in the future to treat PD in new ways or as supplements to existing treatments. We looked at the medicinal plants that can be used to treat PD. The use of natural remedies, especially those derived from plants, to treat PD has been on the rise. This article examines the fundamental characteristics of medicinal plants and the bioactive substances found in them that may be utilized to treat PD.

Connectivity impairment of cerebellar and sensorimotor connector hubs in Parkinson’s disease

Cognitive and movement processes involved integration of several large-scale brain networks. Central to these integrative processes are connector hubs, brain regions characterized by strong connections with multiple networks. Growing evidence suggests that many neurodegenerative and psychiatric disorders are associated with connector hub dysfunctions. Using a network metric called functional connectivity overlap ratio, we investigated connector hub alterations in Parkinson’s disease. Resting-state functional MRI data from 99 patients (male/female = 44/55) and 99 age- and sex-matched healthy controls (male/female = 39/60) participating in our cross-sectional study were used in the analysis. We have identified two sets of connector hubs, mainly located in the sensorimotor cortex and cerebellum, with significant connectivity alterations with multiple resting-state networks. Sensorimotor connector hubs have impaired connections primarily with primary processing (sensorimotor, visual), visuospatial, and basal ganglia networks, whereas cerebellar connector hubs have impaired connections with basal ganglia and executive control networks. These connectivity alterations correlated with patients’ motor symptoms. Specifically, values of the functional connectivity overlap ratio of the cerebellar connector hubs were associated with tremor score, whereas that of the sensorimotor connector hubs with postural instability and gait disturbance score, suggesting potential association of each set of connector hubs with the disorder’s two predominant forms, the akinesia/rigidity and resting tremor subtypes. In addition, values of the functional connectivity overlap ratio of the sensorimotor connector hubs were highly predictive in classifying patients from controls with an accuracy of 75.76%. These findings suggest that, together with the basal ganglia, cerebellar and sensorimotor connector hubs are significantly involved in Parkinson’s disease with their connectivity dysfunction potentially driving the clinical manifestations typically observed in this disorder.

Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease

AIMS

To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction.

METHODS

77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen.

CONCLUSION

The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.

Default Mode Network quantitative diffusion and resting-state functional magnetic resonance imaging correlates in sporadic Creutzfeldt-Jakob disease

Grey matter involvement is a well-known feature in sporadic Creutzfeldt-Jakob disease (sCJD), yet precise anatomy-based quantification of reduced diffusivity is still not fully understood. Default Mode Network (DMN) areas have been recently demonstrated as selectively involved in sCJD, and functional connectivity has never been investigated in prion diseases. We analyzed the grey matter involvement using a quantitatively multi-parametric MRI approach. Specifically, grey matter mean diffusivity of 37 subjects with sCJD was compared with that of 30 age-matched healthy controls with a group-wise approach. Differences in mean diffusivity were also examined between the cortical (MM(V)1, MM(V)2C, and VV1) and subcortical (VV2 and MV2K) subgroups of sCJD for those with autopsy data available (n = 27, 73%). We also assessed resting-state functional connectivity of both ventral and dorsal components of DMN in a subset of subject with a rs-fMRI dataset available (n = 17). Decreased diffusivity was predominantly present in posterior cortical regions of the DMN, but also outside of the DMN in temporal areas and in a few limbic and frontal areas, in addition to extensive deep nuclei involvement. Both subcortical and cortical sCJD subgroups showed decreased diffusivity subcortically, whereas only the cortical type expressed significantly decreased diffusivity cortically, mainly in parietal, occipital, and medial-inferior temporal cortices bilaterally. Interestingly, we found abnormally increased connectivity in both dorsal and ventral components of the DMN in sCJD subjects compared with healthy controls. The significance and possible utility of functional imaging as a biomarker for tracking disease progression in prion disease needs to be explored further.

Modulation effect of substantia nigra iron deposition and functional connectivity on putamen glucose metabolism in Parkinson's disease

Neurodegeneration of the substantia nigra affects putamen activity in Parkinson's disease (PD), yet in vivo evidence of how the substantia nigra modulates putamen glucose metabolism in humans is missing. We aimed to investigate how substantia nigra modulates the putamen glucose metabolism using a cross-sectional design. Resting-state fMRI, susceptibility-weighted imaging, and [18 F]-fluorodeoxyglucose-PET (FDG-PET) data were acquired. Forty-two PD patients and 25 healthy controls (HCs) were recruited for simultaneous PET/MRI scanning. The main measurements of the current study were R 2 * images representing iron deposition (28 PD and 25 HCs), standardized uptake value ratio (SUVr) images representing FDG-uptake (33 PD and 25 HCs), and resting state functional connectivity maps from resting state fMRI (34 PD and 25 HCs). An interaction term based on the general linear model was used to investigate the joint modulation effect of nigral iron deposition and nigral-putamen functional connectivity on putamen FDG-uptake. Compared with HCs, we found increased iron deposition in the substantia nigra (p = .007), increased FDG-uptake in the putamen (left: PFWE  < 0.001; right: PFWE  < 0.001), and decreased functional connectivity between the substantia nigra and the anterior putamen (left PFWE  < 0.001, right: PFWE  = 0.007). We then identified significant interaction effect of nigral iron deposition and nigral-putamen connectivity on FDG-uptake in the putamen (p = .004). The current study demonstrated joint modulation effect of the substantia nigra iron deposition and nigral-putamen functional connectivity on putamen glucose metabolic distribution, thereby revealing in vivo pathological mechanism of nigrostriatal neurodegeneration of PD.

Walking exercise alters pedunculopontine nucleus connectivity in Parkinson’s disease in a dose-dependent manner

Background

Gait disturbances are critical motor symptoms in Parkinson’s disease (PD). The mechanisms of gait impairment in PD are not entirely understood but likely involve changes in the Pedunculopontine Nucleus (PPN), a critical locomotion center, and its associated connections. Exercise is universally accepted as helpful in PD, but the extent and intensity of exercise required for plastic changes are unclear.

Methods

Twenty-seven PD subjects participated in a 3-month gait training intervention. Clinical assessments and resting-state functional magnetic resonance imaging were performed at baseline and 3 months after exercise. Functional connectivity of PPN was assessed by combining the methods of partial least squares, conditional dependence and partial correlation. In addition, paired t-tests were used to examine the effect of exercise on PPN functional connectivity and clinical measures, and Pearson’s correlation was used to assess the association between altered PPN functional connectivity and clinical measures.

Results

Exercise significantly improved Unified Parkinson’s Disease Rating Scale-III (UPDRS-III). A significant increase in right PPN functional connectivity was observed after exercise, which did not correlate with motor improvement. However, the decrease in left PPN functional connectivity significantly correlated with the improvement in UPDRS-III and was linearly related to both number of walks and the duration of walks. In addition, exercise induced a significant increase in the laterality of PPN connectivity strength, which correlated with motor improvement.

Conclusion

PPN functional connectivity is modifiable by walking exercise in both a dose-independent (right PPN and laterality of PPN connectivity strength) and dose-dependent (left PPN) manner. The PPN may contribute to pathological and compensatory processes in PD gait control. The observed gait improvement by walking exercise is most likely due to the reversal of the maladaptive compensatory mechanism. Altered PPN functional connectivity can be a marker for exercise-induced motor improvement in PD.

Alteration in Asymmetry of White Matter Network of Parkinson's Disease

Parkinson's disease (PD) is manifest clinically by an asymmetrical presentation of motor dysfunction. A large number of previous neuroimaging research studies have stated the alteration in the hemispheric asymmetry of morphological features in PD disease. Diffusion Magnetic Resonance Imaging (MRI), which is noninvasive, has been widely used to quantify the white matter network in the human brain of both healthy subjects and patients. Besides, graph theory analysis is widely used to quantify the topological architecture of the human brain network. Lately, researchers have discovered that the topological architecture of the white matter network significantly differs in PD compared with healthy controls (HC). Nevertheless, the asymmetry of the topological architecture of the white matter network for PD patients remains unclear. To clarify this, the diffusion-weighted images and tractography technique were used to reconstruct the hemispherical white matter networks for 22 bilateral PD patients and 18 HC subjects. Network-based statistical analysis and graph theory analysis approaches were employed to estimate the asymmetry at both the connectivity level and the hemispheric topological level for PD patients. We found that the PD group showed atypically right-higher-than-left asymmetry in hemispheric brain global and local efficiencies. The detected right-higher-than-left asymmetry was driven by the atypically topological changes in the left hemispheric brain in the PD group. Findings from these studies might provide new insights into the asymmetric features of hemispheric disconnectivity and emphasize that the topological asymmetry of the hemispheric brain could be used as a biomarker to identify PD individuals.

Brain network analysis between Parkinson's Disease and Health Control based on edge functional connectivity

Parkinson's Disease (PD) is the second largest neurodegenerative disease. Brain functional connectivity (FC) studies for PD were useful. In this study, we employed a novel brain network construction method, edge functional connectivity (eFC), to explore FC differences between healthy control (HC) subjects and PD patients. The data used in this study included 34 HCs and 47 PDs from Huashan Hospital, Fudan University, China. Resting state functional magnetic resonance imaging (rsfMRI) and clinical information were selected. Firstly, we constructed eFC brain network and calculated network matrix for the HC and PD groups. Then, we compared brain network matrix between eFC and the traditional nodal functional connectivity (nFC) method. Receiver operating characteristic curve (ROC) analysis was applied to validate the efficiency of the eFC brain network. The results showed that both nFC and eFC brain networks could identify significantly different characteristics between the HC and PD groups. Important hubs were mainly concentrated in visual network, sensorimotor network, subcortex and cerebellum. In addition, new hubs in basal ganglia and cerebellum regions were found in eFC. Furthermore, eFC achieved better classification results (AUC=0.985) than nFC (AUC=0.861) in discriminating PD from CN subjects.

Longitudinal trimodal imaging of midbrain-associated network degeneration in Parkinson's disease

The prevailing network perspective of Parkinson’s disease (PD) emerges not least from the ascending neuropathology traceable in histological studies. However, whether longitudinal in vivo correlates of network degeneration in PD can be observed remains unresolved. Here, we applied a trimodal imaging protocol combining 18F-fluorodeoxyglucose (FDG)- and 18F-fluoro-L-Dopa- (FDOPA)-PET with resting-state functional MRI to assess longitudinal changes in midbrain metabolism, striatal dopamine depletion and striatocortical dysconnectivity in 17 well-characterized PD patients. Whole-brain (un)paired-t-tests with focus on midbrain or striatum were performed between visits and in relation to 14 healthy controls (HC) in PET modalities. Resulting clusters of FDOPA-PET comparisons provided volumes for seed-based functional connectivity (FC) analyses between visits and in relation to HC. FDG metabolism in the left midbrain decreased compared to baseline along with caudatal FDOPA-uptake. This caudate cluster exhibited a longitudinal FC decrease to sensorimotor and frontal areas. Compared to healthy subjects, dopamine-depleted putamina indicated stronger decline in striatocortical FC at follow-up with respect to baseline. Increasing nigrostriatal deficits and striatocortical decoupling were associated with deterioration in motor scores between visits in repeated-measures correlations. In summary, our results demonstrate the feasibility of in-vivo tracking of progressive network degeneration using a multimodal imaging approach. Specifically, our data suggest advancing striatal and widespread striatocortical dysfunction via an anterior-posterior gradient originating from a hypometabolic midbrain cluster within a well-characterized and only mild to moderately affected PD cohort during a relatively short period.

Is Resting State Functional MRI Effective Connectivity in Movement Disorders Helpful? A Focused Review Across Lifespan and Disease

In the evolving modern era of neuromodulation for movement disorders in adults and children, much progress has been made recently characterizing the human motor network (MN) with potentially important treatment implications. Herein is a focused review of relevant resting state fMRI functional and effective connectivity of the human motor network across the lifespan in health and disease. The goal is to examine how the transition from functional connectivity to dynamic effective connectivity may be especially informative of network-targeted movement disorder therapies, with hopeful implications for children.

Network Reconfiguration Among Cerebellar Visual, and Motor Regions Affects Movement Function in Spinocerebellar Ataxia Type 3

Background

Spinocerebellar ataxia type 3 (SCA3) is a rare movement disorder characterized with ataxia. Previous studies on movement disorders show that the whole-brain functional network tends to be more regular, and these reconfigurations correlate with genetic and clinical variables.

Methods

To test whether the brain network in patients with SCA3 follows a similar reconfiguration course to other movement disorders, we recruited 41 patients with SCA3 (mean age = 40.51 ± 12.13 years; 23 male) and 41 age and sex-matched healthy individuals (age = 40.10 ± 11.56 years; 24 male). In both groups, the whole-brain network topology of resting-state functional magnetic resonance imaging (rs-fMRI) was conducted using graph theory, and the relationships among network topologies, cytosine-adenine-guanine (CAG) repeats, clinical symptoms, and functional connectivity were explored in SCA3 patients using partial correlation analysis, controlling for age and sex.

Results

The brain networks tended to be more regular with a higher clustering coefficient, local efficiency, and modularity in patients with SCA3. Hubs in SCA3 patients were reorganized as the number of hubs increased in motor-related areas and decreased in cognitive areas. At the global level, small-worldness and normalized clustering coefficients were significantly positively correlated with clinical motor symptoms. At the nodal level, the clustering coefficient and local efficiency increased significantly in the visual (bilateral cuneus) and sensorimotor (right cerebellar lobules IV, V, VI) networks and decreased in the cognitive areas (right middle frontal gyrus). The clustering coefficient and local efficiency in the bilateral cuneus gyrus were negatively correlated with clinical motor symptoms. The functional connectivity between right caudate nucleus and bilateral calcarine gyrus were negatively correlated with disease duration, while connectivity between right posterior cingulum gyrus and left cerebellar lobule III, left inferior occipital gyrus and right cerebellar lobule IX was positively correlated.

Conclusion

Our results demonstrate that a more regular brain network occurred in SCA3 patients, with motor and visual-related regions, such as, cerebellar lobules and cuneus gyrus, both forayed neighbor nodes as “resource predators” to compensate for normal function, with motor and visual function having the higher priority comparing with other high-order functions. This study provides new information about the neurological mechanisms underlying SCA3 network topology impairments in the resting state, which give a potential guideline for future clinical treatments.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [ChiCTR1800019901].

Motor learning in hemi-Parkinson using VR-manipulated sensory feedback

AIMS

Modalities for rehabilitation of the neurologically affected upper-limb (UL) are generally of limited benefit. The majority of patients seriously affected by UL paresis remain with severe motor disability, despite all rehabilitation efforts. Consequently, extensive clinical research is dedicated to develop novel strategies aimed to improve the functional outcome of the affected UL. We have developed a novel virtual-reality training tool that exploits the voluntary control of one hand and provides real-time movement-based manipulated sensory feedback as if the other hand is the one that moves. The aim of this study was to expand our previous results, obtained in healthy subjects, to examine the utility of this training setup in the context of neuro-rehabilitation.

METHODS

We tested the training setup in patient LA, a young man with significant unilateral UL dysfunction stemming from hemi-parkinsonism. LA underwent daily intervention in which he intensively trained the non-affected upper limb, while receiving online sensory feedback that created an illusory perception of control over the affected limb. Neural changes were assessed using functional magnetic resonance imaging (fMRI) scans before and after training.

RESULTS

Training-induced behavioral gains were accompanied by enhanced activation in the pre-frontal cortex and a widespread increase in resting-state functional connectivity.

DISCUSSION

Our combination of cutting edge technologies, insights gained from basic motor neuroscience in healthy subjects and well-known clinical treatments, hold promise for the pursuit of finding novel and more efficient rehabilitation schemes for patients suffering from hemiplegia.Implications for rehabilitationAssistive devices used in hospitals to support patients with hemiparesis require expensive equipment and trained personnel - constraining the amount of training that a given patient can receive. The setup we describe is simple and can be easily used at home with the assistance of an untrained caregiver/family member. Once installed at the patient's home, the setup is lightweight, mobile, and can be used with minimal maintenance . Building on advances in machine learning, our software can be adapted to personal use at homes. Our findings can be translated into practice with relatively few adjustments, and our experimental design may be used as an important adjuvant to standard clinical care for upper limb hemiparesis.

Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability.

Aberrant Volume-Wise and Voxel-Wise Concordance Among Dynamic Intrinsic Brain Activity Indices in Parkinson's Disease: A Resting-State fMRI Study

Researches using resting-state functional magnetic resonance imaging (rs-fMRI) have applied different regional measurements to study the intrinsic brain activity (IBA) of patients with Parkinson's disease (PD). Most previous studies have only examined the static characteristics of IBA in patients with PD, neglecting the dynamic features. We sought to explore the concordance between the dynamics of different rs-fMRI regional indices. This study included 31 healthy controls (HCs) and 57 PD patients to calculate the volume-wise (across voxels) and voxel-wise (across periods) concordance using a sliding time window approach. This allowed us to compare the concordance of dynamic alterations in frequently used metrics such as degree centrality (DC), global signal connectivity (GSC), voxel-mirrored heterotopic connectivity (VMHC), the amplitude of low-frequency fluctuations (ALFF), and regional homogeneity (ReHo). We analyzed the changes of concordance indices in the PD patients and investigated the relationship between aberrant concordance values and clinical/neuropsychological assessments in the PD patients. We found that, compared with the HCs, the PD patients had lower volume concordance in the whole brain and lower voxel-wise concordance in the posterior cerebellar lobe, cerebellar tonsils, superior temporal gyrus, and supplementary motor region. We also found negative correlations between these concordance alterations and patients' age. The exploratory results contribute to a better understanding of IBA alterations and pathophysiological mechanisms in PD.

Increased Basal Ganglia Modulatory Effective Connectivity Observed in Resting-State fMRI in Individuals With Parkinson's Disease

Alterations to interactions between networked brain regions underlie cognitive impairment in many neurodegenerative diseases, providing an important physiological link between brain structure and cognitive function. Previous attempts to characterize the effects of Parkinson's disease (PD) on network functioning using resting-state functional magnetic resonance imaging (rs-fMRI), however, have yielded inconsistent and contradictory results. Potential problems with prior work arise in the specifics of how the area targeted by the diseases (the basal ganglia) interacts with other brain regions. Specifically, current computational models point to the fact that the basal ganglia contributions should be captured with modulatory (i.e., second-order) rather than direct (i.e., first-order) functional connectivity measures. Following this hypothesis, a principled but manageable large-scale brain architecture, the Common Model of Cognition, was used to identify differences in basal ganglia connectivity in PD by analyzing resting-state fMRI data from 111 participants (70 patients with PD; 41 healthy controls) using Dynamic Causal Modeling (DCM). Specifically, the functional connectivity of the basal ganglia was modeled as two second-level, modulatory connections that control projections from sensory cortices to the prefrontal cortex, and from the hippocampus and medial temporal lobe to the prefrontal cortex. We then examined group differences between patients with PD and healthy controls in estimated modulatory effective connectivity in these connections. The Modulatory variant of the Common Model of Cognition outperformed the Direct model across all subjects. It was also found that these second-level modulatory connections had higher estimates of effective connectivity in the PD group compared to the control group, and that differences in effective connectivity were observed for all direct connections between the PD and control groups.We make the case that accounting for modulatory effective connectivity better captures the effects of PD on network functioning and influences the interpretation of the directionality of the between-group results. Limitations include that the PD group was scanned on dopaminergic medication, results were derived from a reasonable but small number of individuals and the ratio of PD to healthy control participants was relatively unbalanced. Future research will examine if the observed effect holds for individuals with PD scanned off their typical dopaminergic medications.