The study of brain functional connectivity in Parkinson's disease

Why so slow? Models of parkinsonian bradykinesia

Bradykinesia, or slowness of movement, is a defining feature of Parkinson disease (PD) and a major contributor to the negative effects on quality of life associated with this disorder and related conditions. A dominant pathophysiological model of bradykinesia in PD has existed for approximately 30 years and has been the basis for the development of several therapeutic interventions, but accumulating evidence has made this model increasingly untenable. Although more recent models have been proposed, they also appear to be flawed. In this Perspective, I consider the leading prior models of bradykinesia in PD and argue that a more functionally related model is required, one that considers changes that disrupt the fundamental process of accurate information transmission. In doing so, I review emerging evidence of network level functional connectivity changes, information transfer dysfunction and potential motor code transmission error and present a novel model of bradykinesia in PD that incorporates this evidence. I hope that this model may reconcile inconsistencies in its predecessors and encourage further development of therapeutic interventions.

The complement system in neurodegenerative and inflammatory diseases of the central nervous system

Neurodegenerative and neuroinflammatory diseases, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis, affect millions of people globally. As aging is a major risk factor for neurodegenerative diseases, the continuous increase in the elderly population across Western societies is also associated with a rising prevalence of these debilitating conditions. The complement system, a crucial component of the innate immune response, has gained increasing attention for its multifaceted involvement in the normal development of the central nervous system (CNS) and the brain but also as a pathogenic driver in several neuroinflammatory disease states. Although complement is generally understood as a liver-derived and blood or interstitial fluid operative system protecting against bloodborne pathogens or threats, recent research, particularly on the role of complement in the healthy and diseased CNS, has demonstrated the importance of locally produced and activated complement components. Here, we provide a succinct overview over the known beneficial and pathological roles of complement in the CNS with focus on local sources of complement, including a discussion on the potential importance of the recently discovered intracellularly active complement system for CNS biology and on infection-triggered neurodegeneration.

Disuse-driven plasticity in the human thalamus and putamen

Motor adaptation in cortico-striato-thalamo-cortical loops has been studied mainly in animals using invasive electrophysiology. Here, we leverage functional neuroimaging in humans to study motor circuit plasticity in the human subcortex. We employed an experimental paradigm that combined two weeks of upper-extremity immobilization with daily resting-state and motor task fMRI before, during, and after the casting period. We previously showed that limb disuse leads to decreased functional connectivity (FC) of the contralateral somatomotor cortex (SM1) with the ipsilateral somatomotor cortex, increased FC with the cingulo-opercular network (CON) as well as the emergence of high amplitude, fMRI signal pulses localized in the contralateral SM1, supplementary motor area and the cerebellum. From our prior observations, it remains unclear whether the disuse plasticity affects the thalamus and striatum. We extended our analysis to include these subcortical regions and found that both exhibit strengthened cortical FC and spontaneous fMRI signal pulses induced by limb disuse. The dorsal posterior putamen and the central thalamus, mainly CM, VLP and VIM nuclei, showed disuse pulses and FC changes that lined up with fmri task activations from the Human connectome project motor system localizer, acquired before casting for each participant. Our findings provide a novel understanding of the role of the cortico-striato-thalamo-cortical loops in human motor plasticity and a potential link with the physiology of sleep regulation. Additionally, similarities with FC observation from Parkinson Disease (PD) questions a pathophysiological link with limb disuse.

The central role of the Thalamus in psychosis, lessons from neurodegenerative diseases and psychedelics

The PD-DLB psychosis complex found in Parkinson's disease (PD) and Dementia with Lewy Bodies (DLB) includes hallucinations, Somatic Symptom/Functional Disorders, and delusions. These disorders exhibit similar presentation patterns and progression. Mechanisms at the root of these symptoms also share similarities with processes promoting altered states of consciousness found in Rapid Eye Movement sleep, psychiatric disorders, or the intake of psychedelic compounds. We propose that these mechanisms find a crucial driver and trigger in the dysregulated activity of high-order thalamic nuclei set in motion by ThalamoCortical Dysrhythmia (TCD). TCD generates the loss of finely tuned cortico-cortical modulations promoted by the thalamus and unleashes the aberrant activity of the Default Mode Network (DMN). TCD moves in parallel with altered thalamic filtering of external and internal information. The process produces an input overload to the cortex, thereby exacerbating DMN decoupling from task-positive networks. These phenomena alter the brain metastability, creating dreamlike, dissociative, or altered states of consciousness. In support of this hypothesis, mind-altering psychedelic drugs also modulate thalamic-cortical pathways. Understanding the pathophysiological background of these conditions provides a conceptual bridge between neurology and psychiatry, thereby helping to generate a promising and converging area of investigation and therapeutic efforts.

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.

From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology

With the inexorable aging of the global populace, neurodegenerative diseases (NDs) like Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) pose escalating challenges, which are underscored by their socioeconomic repercussions. A pivotal aspect in addressing these challenges lies in the elucidation and application of biomarkers for timely diagnosis, vigilant monitoring, and effective treatment modalities. This review delineates the quintessence of biomarkers in the realm of NDs, elucidating various classifications and their indispensable roles. Particularly, the quest for novel biomarkers in AD, transcending traditional markers in PD, and the frontier of biomarker research in ALS are scrutinized. Emergent susceptibility and trait markers herald a new era of personalized medicine, promising enhanced treatment initiation especially in cases of SOD1-ALS. The discourse extends to diagnostic and state markers, revolutionizing early detection and monitoring, alongside progression markers that unveil the trajectory of NDs, propelling forward the potential for tailored interventions. The synergy between burgeoning technologies and innovative techniques like -omics, histologic assessments, and imaging is spotlighted, underscoring their pivotal roles in biomarker discovery. Reflecting on the progress hitherto, the review underscores the exigent need for multidisciplinary collaborations to surmount the challenges ahead, accelerate biomarker discovery, and herald a new epoch of understanding and managing NDs. Through a panoramic lens, this article endeavors to provide a comprehensive insight into the burgeoning field of biomarkers in NDs, spotlighting the promise they hold in transforming the diagnostic landscape, enhancing disease management, and illuminating the pathway toward efficacious therapeutic interventions.

Ultra-high-field 7T MRI in Parkinson's disease: ready for clinical use?-a narrative review

Background and Objective

The maturation of ultra-high-field magnetic resonance imaging (MRI) [≥7 Tesla (7T)] has improved our capability to depict and characterise brain structures efficiently, with better signal-to-noise ratio (SNR) and spatial resolution. We evaluated whether these improvements benefit the clinical detection and management of Parkinson's disease (PD).

Methods

We performed a literature search in March 2023 in PubMed (MEDLINE), EMBASE and Google Scholar for articles on "7T MRI" AND "Parkinson*", written in English, published between inception and 1st March, 2023, which we synthesised in narrative form.

Key Content and Findings

In deep-brain stimulation (DBS) surgical planning, early studies show that 7T MRI can distinguish anatomical substructures, and that this results in reduced adverse effects. In other areas, while there is strong evidence for improved accuracy and precision of 7T MRI-based measurements for PD, there is limited evidence for meaningful clinical translation. In particular, neuromelanin-iron complex quantification and visualisation in midbrain nuclei is enhanced, enabling depiction of nigrosomes 1-5, improved morphometry and vastly improved radiological assessments; however, studies on the related clinical outcomes, diagnosis, subtyping, differentiation of atypical parkinsonisms, and monitoring of treatment response using 7T MRI are lacking. Moreover, improvements in clinical utility must be great enough to justify the additional costs.

Conclusions

Together, current evidence supports feasible future clinical implementation of 7T MRI for PD. Future impacts to clinical decision making for diagnosis, differentiation, and monitoring of progression or treatment response are likely; however, to achieve this, further longitudinal studies using 7T MRI are needed in prodromal, early-stage PD and parkinsonism cohorts focusing on clinical translational potential.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification

18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

An Adaptive Weighted Attention-Enhanced Deep Convolutional Neural Network for Classification of MRI images of Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is the second prevalent neurological diseases with a significant growth rate in incidence. Convolutional neural networks using structural magnetic resonance images (sMRI) are widely used for PD classification. However, the areas of change in the patient's MRI images are small and unfixed. Thus, capturing the features of the areas accurately where the lesions changed became a problem.

METHOD

We propose a deep learning framework that combines multi-scale attention guidance and multi-branch feature processing modules to diagnose PD by learning sMRI T2 slice features. In this scheme, firstly, to achieve effective feature transfer and gradient descent, a deep convolutional neural network framework based on dense block is designed. Next, an Adaptive Weighted Attention algorithm is proposed, whose pursers is to extract multi branch and even diverse features. Finally, Dropout layer and SoftMax layer are added to the network structure to obtain good classification results and rich and diverse feature information. The Dropout layer is used to reduce the number of intermediate features to increase the orthogonality between features of each layer. The activation function SoftMax increases the flexibility of the neural network by increasing the degree of fitting to the training set and converting linear to nonlinear.

RESULTS

The best performance of the proposed method an accuracy of 92%, a sensitivity of 94%, specificity of 90% and a F1 score of 95% respectively for identifying PD and HC.

CONCLUSION

Experiments show that the proposed method can successfully distinguish PD and NC. Good classification results were obtained in PD diagnosis classification task and compared with advanced research methods.

Temporal and spatial variability of dynamic microstate brain network in early Parkinson's disease

Changes of brain network dynamics reveal variations in macroscopic neural activity patterns in behavioral and cognitive aspects. Quantification and application of changed dynamics in brain functional connectivity networks may contribute to a better understanding of brain diseases, and ultimately provide better prognostic indicators or auxiliary diagnostic tools. At present, most studies are focused on the properties of brain functional connectivity network constructed by sliding window method. However, few studies have explored evidence-based brain network construction algorithms that reflect disease specificity. In this work, we first proposed a novel approach to characterize the spatiotemporal variability of dynamic functional connectivity networks based on electroencephalography (EEG) microstate, and then developed a classification framework for integrating spatiotemporal variability of brain networks to improve early Parkinson's disease (PD) diagnostic performance. The experimental results indicated that compared with the brain network construction method based on conventional sliding window, the proposed method significantly improved the performance of early PD recognition, demonstrating that the dynamic spatiotemporal variability of microstate-based brain networks can reflect the pathological changes in the early PD brain. Furthermore, we observed that the spatiotemporal variability of early PD brain network has a specific distribution pattern in brain regions, which can be quantified as the degree of motor and cognitive impairment, respectively. Our work offers innovative methodological support for future research on brain network, and provides deeper insights into the spatiotemporal interaction patterns of brain activity and their variabilities in early PD.

Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study

Introduction

Effective connectivity (EC), the causal influence that functional activity in a source brain location exerts over functional activity in a target brain location, has the potential to provide different information about brain network dynamics than functional connectivity (FC), which quantifies activity synchrony between locations. However, head-to-head comparisons between EC and FC from either task-based or resting-state functional MRI (fMRI) data are rare, especially in terms of how they associate with salient aspects of brain health.

Methods

In this study, 100 cognitively-healthy participants in the Bogalusa Heart Study aged 54.2 ± 4.3years completed Stroop task-based fMRI, resting-state fMRI. EC and FC among 24 regions of interest (ROIs) previously identified as involved in Stroop task execution (EC-task and FC-task) and among 33 default mode network ROIs (EC-rest and FC-rest) were calculated from task-based and resting-state fMRI using deep stacking networks and Pearson correlation. The EC and FC measures were thresholded to generate directed and undirected graphs, from which standard graph metrics were calculated. Linear regression models related graph metrics to demographic, cardiometabolic risk factors, and cognitive function measures.

Results

Women and whites (compared to men and African Americans) had better EC-task metrics, and better EC-task metrics associated with lower blood pressure, white matter hyperintensity volume, and higher vocabulary score (maximum value of p = 0.043). Women had better FC-task metrics, and better FC-task metrics associated with APOE-ε4 3–3 genotype and better hemoglobin-A1c, white matter hyperintensity volume and digit span backwards score (maximum value of p = 0.047). Better EC rest metrics associated with lower age, non-drinker status, and better BMI, white matter hyperintensity volume, logical memory II total score, and word reading score (maximum value of p = 0.044). Women and non-drinkers had better FC-rest metrics (value of p = 0.004).

Discussion

In a diverse, cognitively healthy, middle-aged community sample, EC and FC based graph metrics from task-based fMRI data, and EC based graph metrics from resting-state fMRI data, were associated with recognized indicators of brain health in differing ways. Future studies of brain health should consider taking both task-based and resting-state fMRI scans and measuring both EC and FC analyses to get a more complete picture of functional networks relevant to brain health.

Hippocampal, basal ganglia and olfactory connectivity contribute to cognitive impairments in Parkinson's disease

Cognitive impairment is increasingly recognized as a characteristic feature of Parkinson's disease (PD), yet relatively little is known about its underlying neurobiology. Previous investigations suggest that dementia in PD is associated with subcortical atrophy, but similar studies in PD with mild cognitive impairment have been mixed. Variability in cognitive phenotypes and diversity of PD symptoms suggest that a common neuropathological origin results in a multitude of impacts within the brain. These direct and indirect impacts of disease pathology can be investigated using network analysis. Functional connectivity, for instance, may be more sensitive than atrophy to decline in specific cognitive domains in the PD population. Fifty-eight participants with PD underwent a neuropsychological test battery and scanning with structural and resting state functional MRI in a comprehensive whole-brain association analysis. To investigate atrophy as a potential marker of impairment, structural gray matter atrophy was associated with cognitive scores in each cognitive domain using voxel-based morphometry. To investigate connectivity, large-scale networks were correlated with voxel time series and associated with cognitive scores using distance covariance. Structural atrophy was not associated with any cognitive domain, with the exception of visuospatial measures in primary sensory and motor cortices. In contrast, functional connectivity was associated with attention, executive function, language, learning and memory, visuospatial, and global cognition in the bilateral hippocampus, left putamen, olfactory cortex, and bilateral anterior temporal poles. These preliminary results suggest that cognitive domain-specific networks in PD are distinct from each other and could provide a network signature for different cognitive phenotypes.

Individual-level functional connectomes predict the motor symptoms of Parkinson's disease

Abnormalities in functional connectivity networks are associated with sensorimotor networks in Parkinson's disease (PD) based on group-level mapping studies, but these results are controversial. Using individual-level cortical segmentation to construct individual brain atlases can supplement the individual information covered by group-level cortical segmentation. Functional connectivity analyses at the individual level are helpful for obtaining clinically useful markers and predicting treatment response. Based on the functional connectivity of individualized regions of interest, a support vector regression model was trained to estimate the severity of motor symptoms for each subject, and a correlation analysis between the estimated scores and clinical symptom scores was performed. Forty-six PD patients aged 50-75 years were included from the Parkinson's Progression Markers Initiative database, and 63 PD patients were included from the Beijing Rehabilitation Hospital database. Only patients below Hoehn and Yahr stage III were included. The analysis showed that the severity of motor symptoms could be estimated by the individualized functional connectivity between the visual network and sensorimotor network in early-stage disease. The results reveal individual-level connectivity biomarkers related to motor symptoms and emphasize the importance of individual differences in the prediction of the treatment response of PD.

Comparative efficacy of transcranial magnetic stimulation on different targets in Parkinson's disease: A Bayesian network meta-analysis

Background/Objective

The efficacy of transcranial magnetic stimulation (TMS) on Parkinson's disease (PD) varies across the stimulation targets. This study aims to estimate the effect of different TMS targets on motor symptoms in PD.

Methods

A Bayesian hierarchical model was built to assess the effects across different TMS targets, and the rank probabilities and the surface under the cumulative ranking curve (SUCRA) values were calculated to determine the ranks of each target. The primary outcome was the Unified Parkinson's Disease Rating Scale part-III. Inconsistency between direct and indirect comparisons was assessed using the node-splitting method.

Results

Thirty-six trials with 1,122 subjects were included for analysis. The pair-wise meta-analysis results showed that TMS could significantly improve motor symptoms in PD patients. Network meta-analysis results showed that the high-frequency stimulation over bilateral M1, bilateral DLPFC, and M1+DLPFC could significantly reduce the UPDRS-III scores compared with sham conditions. The high-frequency stimulation over both M1 and DLPFC had a more significant effect when compared with other parameters, and ranked first with the highest SCURA value. There was no significant inconsistency between direct and indirect comparisons.

Conclusion

Considering all settings reported in our research, high-frequency stimulation over bilateral M1 or bilateral DLPFC has a moderate beneficial effect on the improvement of motor symptoms in PD (high confidence rating). High-frequency stimulation over M1+DLPFC has a prominent beneficial effect and appears to be the most effective TMS parameter setting for ameliorating motor symptoms of PD patients (high confidence rating).

Sexual Dimorphism in Neurodegenerative Diseases and in Brain Ischemia

Epidemiological studies and clinical observations show evidence of sexual dimorphism in brain responses to several neurological conditions. It is suggested that sex-related differences between men and women may have profound effects on disease susceptibility, pathophysiology, and progression. Sexual differences of the brain are achieved through the complex interplay of several factors contributing to this phenomenon, such as sex hormones, as well as genetic and epigenetic differences. Despite recent advances, the precise link between these factors and brain disorders is incompletely understood. This review aims to briefly outline the most relevant aspects that differ between men and women in ischemia and neurodegenerative disorders (AD, PD, HD, ALS, and SM). Recognition of disparities between both sexes could aid the development of individual approaches to ameliorate or slow the progression of intractable disorders.

Scale-Free Functional Brain Networks Exhibit Increased Connectivity, Are More Integrated and Less Segregated in Patients with Parkinson's Disease following Dopaminergic Treatment

Dopaminergic treatment (DT), the standard therapy for Parkinson's disease (PD), alters the dynamics of functional brain networks at specific time scales. Here, we explore the scale-free functional connectivity (FC) in the PD population and how it is affected by DT. We analyzed the electroencephalogram of: (i) 15 PD patients during DT (ON) and after DT washout (OFF) and (ii) 16 healthy control individuals (HC). We estimated FC using bivariate focus-based multifractal analysis, which evaluated the long-term memory ( H ( 2 ) ) and multifractal strength ( Δ H 15 ) of the connections. Subsequent analysis yielded network metrics (node degree, clustering coefficient and path length) based on FC estimated by H ( 2 ) or Δ H 15 . Cognitive performance was assessed by the Mini Mental State Examination (MMSE) and the North American Adult Reading Test (NAART). The node degrees of the Δ H 15 networks were significantly higher in ON, compared to OFF and HC, while clustering coefficient and path length significantly decreased. No alterations were observed in the H ( 2 ) networks. Significant positive correlations were also found between the metrics of H ( 2 ) networks and NAART scores in the HC group. These results demonstrate that DT alters the multifractal coupled dynamics in the brain, warranting the investigation of scale-free FC in clinical and pharmacological studies.

Update on the application of mesenchymal stem cell-derived exosomes in the treatment of Parkinson's disease: A systematic review

Parkinson's disease (PD) has become the second largest neurodegenerative disease after Alzheimer's disease, and its incidence is increasing year by year. Traditional dopamine replacement therapy and deep brain stimulation can only alleviate the clinical symptoms of patients with PD but cannot cure the disease. In recent years, stem cell therapy has been used to treat neurodegenerative diseases. Many studies have shown that stem cell transplantation has a therapeutic effect on PD. Here, we review recent studies indicating that exosomes derived from mesenchymal stem cells also have the potential to treat PD in animal models, but the exact mechanism remains unclear. This article reviews the mechanisms through which exosomes are involved in intercellular information exchange, promote neuroprotection and freely cross the blood-brain barrier in the treatment of PD. The increase in the incidence of PD and the decline in the quality of life of patients with advanced PD have placed a heavy burden on patients, families and society. Therefore, innovative therapies for PD are urgently needed. Herein, we discuss the mechanisms underlying the effects of exosomes in PD, to provide new insights into the treatment of PD. The main purpose of this article is to explore the therapeutic potential of exosomes derived from mesenchymal stem cells and future research directions for this degenerative disease.

Altered levels of variant cholinesterase transcripts contribute to the imbalanced cholinergic signaling in Alzheimer's and Parkinson's disease

Acetylcholinesterase and butyrylcholinesterase (AChE and BChE) are involved in modulating cholinergic signaling, but their roles in Alzheimer's and Parkinson's diseases (AD and PD) remain unclear. We identified a higher frequency of the functionally impaired BCHE-K variant (rs1803274) in AD and PD compared to controls and lower than in the GTEx dataset of healthy individuals (n = 651); in comparison, the prevalence of the 5'-UTR (rs1126680) and intron 2 (rs55781031) single-nucleotide polymorphisms (SNPs) of BCHE and ACHE's 3'-UTR (rs17228616) which disrupt AChE mRNA targeting by miR-608 remained unchanged. qPCR validations confirmed lower levels of the dominant splice variant encoding the "synaptic" membrane-bound ACHE-S in human post-mortem superior temporal gyrus samples from AD and in substantia nigra (but not amygdala) samples from PD patients (n = 79, n = 67) compared to controls, potentially reflecting region-specific loss of cholinergic neurons. In contradistinction, the non-dominant "readthrough" AChE-R mRNA variant encoding for soluble AChE was elevated (p < 0.05) in the AD superior temporal gyrus and the PD amygdala, but not in the neuron-deprived substantia nigra. Elevated levels of BChE (p < 0.001) were seen in AD superior temporal gyrus. Finally, all three ACHE splice variants, AChE-S, AChE-R, and N-extended AChE, were elevated in cholinergic-differentiated human neuroblastoma cells, with exposure to the oxidative stress agent paraquat strongly downregulating AChE-S and BChE, inverse to their upregulation under exposure to the antioxidant simvastatin. The multi-leveled changes in cholinesterase balance highlight the role of post-transcriptional regulation in neurodegeneration. (235).

The potential use of mesenchymal stem cells and their exosomes in Parkinson's disease treatment

Parkinson's disease (PD) is the second most predominant neurodegenerative disease worldwide. It is recognized clinically by severe complications in motor function caused by progressive degeneration of dopaminergic neurons (DAn) and dopamine depletion. As the current standard of treatment is focused on alleviating symptoms through Levodopa, developing neuroprotective techniques is critical for adopting a more pathology-oriented therapeutic approach. Regenerative cell therapy has provided us with an unrivalled platform for evaluating potentially effective novel methods for treating neurodegenerative illnesses over the last two decades. Mesenchymal stem cells (MSCs) are most promising, as they can differentiate into dopaminergic neurons and produce neurotrophic substances. The precise process by which stem cells repair neuronal injury is unknown, and MSC-derived exosomes are suggested to be responsible for a significant portion of such effects. The present review discusses the application of mesenchymal stem cells and MSC-derived exosomes in PD treatment.

Betweenness Centrality in Resting-State Functional Networks Distinguishes Parkinson's Disease

The goal of this paper is to use graph theory network measures derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson's disease (PD) patients from healthy controls (HC). EEG signals from 27 patients and 27 demographically matched controls from New Mexico were analyzed by estimating their functional networks. Data recorded from the patients during ON and OFF levodopa sessions were included in the analysis for comparison. We used betweenness centrality of estimated functional networks to classify the HC and PD groups. The classifiers were evaluated using leave-one-out cross-validation. We observed that the PD patients (on and off medication) could be distinguished from healthy controls with 89% accuracy - approximately 4% higher than the state-of-the-art on the same dataset. This work shows that brain network analysis using extracranial resting-state EEG can discover patterns of interactions indicative of PD. This approach can also be extended to other neurological disorders.

Dopaminergic Modulation of Local Non-oscillatory Activity and Global-Network Properties in Parkinson's Disease: An EEG Study

Dopaminergic medication for Parkinson's disease (PD) modulates neuronal oscillations and functional connectivity (FC) across the basal ganglia-thalamic-cortical circuit. However, the non-oscillatory component of the neuronal activity, potentially indicating a state of excitation/inhibition balance, has not yet been investigated and previous studies have shown inconsistent changes of cortico-cortical connectivity as a response to dopaminergic medication. To further elucidate changes of regional non-oscillatory component of the neuronal power spectra, FC, and to determine which aspects of network organization obtained with graph theory respond to dopaminergic medication, we analyzed a resting-state electroencephalography (EEG) dataset including 15 PD patients during OFF and ON medication conditions. We found that the spectral slope, typically used to quantify the broadband non-oscillatory component of power spectra, steepened particularly in the left central region in the ON compared to OFF condition. In addition, using lagged coherence as a FC measure, we found that the FC in the beta frequency range between centro-parietal and frontal regions was enhanced in the ON compared to the OFF condition. After applying graph theory analysis, we observed that at the lower level of topology the node degree was increased, particularly in the centro-parietal area. Yet, results showed no significant difference in global topological organization between the two conditions: either in global efficiency or clustering coefficient for measuring global and local integration, respectively. Interestingly, we found a close association between local/global spectral slope and functional network global efficiency in the OFF condition, suggesting a crucial role of local non-oscillatory dynamics in forming the functional global integration which characterizes PD. These results provide further evidence and a more complete picture for the engagement of multiple cortical regions at various levels in response to dopaminergic medication in PD.

Electroencephalographic-Based Functional Connectivity Networks of Visual Hallucinations and Visuospatial Dysfunctions in Parkinson's Disease

Visual dysfunction is an important nonmotor symptom of Parkinson's disease (PD). Visual hallucinations (VHs) and visuospatial dysfunctions (VSDs) are common visual dysfunctions in PD; however, the underlying mechanisms remain unclear. Our study aimed to evaluate neuronal synchronization between patients with PD with and without VH or VSD using electroencephalographic (EEG) coherence analysis. Twenty-four patients with sporadic PD were evaluated for the presence of VH and VSD, and were divided into VH-negative and VH-positive groups, and these groups were further subdivided by VSD status. Coherence analysis was performed on EEG data. Whole-brain and regional coherences were calculated and compared between the groups. There was a significant difference in frontal-frontal coherence between the VH+ VSD- and VH+ VSD+ groups (p = 0.026). Our findings suggest that reduced EEG coherence in frontal regions might be involved in VSD in patients with PD. Reduced neuronal synchronization between the frontal lobes may contribute to the disruption of visual processing in PD.

Antagonistic network signature of motor function in Parkinson's disease revealed by connectome-based predictive modeling

Motor impairment is a core clinical feature of Parkinson’s disease (PD). Although the decoupled brain connectivity has been widely reported in previous neuroimaging studies, how the functional connectome is involved in motor dysfunction has not been well elucidated in PD patients. Here we developed a distributed brain signature by predicting clinical motor scores of PD patients across multicenter datasets (total n = 236). We decomposed the Pearson’s correlation into accordance and discordance via a temporal discrete procedure, which can capture coupling and anti-coupling respectively. Using different profiles of functional connectivity, we trained candidate predictive models and tested them on independent and heterogeneous PD samples. We showed that the antagonistic model measured by discordance had the best sensitivity and generalizability in all validations and it was dubbed as Parkinson’s antagonistic motor signature (PAMS). The PAMS was dominated by the subcortical, somatomotor, visual, cerebellum, default-mode, and frontoparietal networks, and the motor-visual stream accounted for the most part of predictive weights among network pairs. Additional stage-specific analysis showed that the predicted scores generated from the antagonistic model tended to be higher than the observed scores in the early course of PD, indicating that the functional signature may vary more sensitively with the neurodegenerative process than clinical behaviors. Together, these findings suggest that motor dysfunction of PD is represented as antagonistic interactions within multi-level brain systems. The signature shows great potential in the early motor evaluation and developing new therapeutic approaches for PD in the clinical realm.

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.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

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.

Robot-assisted gait training with auditory and visual cues in Parkinson's disease: a randomized controlled trial

BACKGROUND

Robot-assisted gait training (RAGT) may have beneficial effects on Parkinson's disease (PD); however, the evidence to date is inconsistent.

OBJECTIVES

This study compared the effects of RAGT and treadmill training (TT) on gait speed, dual-task gait performance, and changes in resting-state brain functional connectivity in individuals with PD.

METHODS

In this prospective, single-center, randomized controlled trial with a parallel two-group design, 44 participants were randomly allocated to undergo 12 sessions (3 times per week for 4 weeks) of RAGT or TT. The primary outcome was gait speed on the 10-m walk test (10mWT) under comfortable walking conditions. Secondary outcomes included dual-task interference on gait speed, balance, disability scores, fear of falling, freezing of gait, and brain functional connectivity changes. All clinical outcomes were measured before (T0), immediately after (T1), and 1 month after treatment (T2).

RESULTS

The mean (SD) age of the participants was 68.1 (8.1) years, and mean disease duration 108.0 (61.5) months. The groups did not significantly differ on the 10mWT (T0-T1, p=0.726, Cohen's d=0.133; T0-T2, p=0.778, Cohen's d=0.121). We observed a significant time-by-group interaction (F=3.236, p=0.045) for cognitive dual-task interference, controlling for confounders. After treatment, coupling was decreased to a greater extent with RAGT than TT between the visual and dorsal attention networks (p=0.015), between bilateral fronto-parietal networks (p=0.043), and between auditory and medial temporal networks (p=0.018). Improvement in cognitive dual-task interference was positively correlated with enhanced visual and medial temporal network coupling overall (r=0.386, p=0.029) and with TT (r=0.545, p=0.024) but not RAGT (r=0.151, p=0.590).

CONCLUSIONS

RAGT was not superior to intensity-matched TT on improving gait functions in individuals with PD but may be beneficial in improving gait ability under cognitive dual-task conditions. The therapeutic mechanism and key functional connectivity changes associated with improvement may differ between treatment strategies.

Estimating Effective Connectivity by Recurrent Generative Adversarial Networks

Estimating effective connectivity from functional magnetic resonance imaging (fMRI) time series data has become a very hot topic in neuroinformatics and brain informatics. However, it is hard for the current methods to accurately estimate the effective connectivity due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for estimating effective connectivity based on recurrent generative adversarial networks, called EC-RGAN. The proposed framework employs the generator that consists of a set of effective connectivity generators based on recurrent neural networks to generate the fMRI time series of each brain region, and uses the discriminator to distinguish between the joint distributions of the real and generated fMRI time series. When the model is well-trained and generated fMRI data is similar to real fMRI data, EC-RGAN outputs the effective connectivity by means of the causal parameters of the effective connectivity generators. Experimental results on both simulated and real-world fMRI time series data demonstrate the efficacy of our proposed framework.

The amplitude of low-frequency fluctuation predicts levodopa treatment response in patients with Parkinson's disease

INTRODUCTION

Levodopa has become the main therapy for motor symptoms of Parkinson's disease (PD). This study aimed to test whether the amplitude of low-frequency fluctuation (ALFF) computed by fMRI could predict individual patient's response to levodopa treatment.

METHODS

We included 40 patients. Treatment efficacy was defined based on motor symptoms improvement from the state of medication off to medication on, as assessed by the Unified Parkinson's Disease Rating Scale score III. Two machine learning models were constructed to test the prediction ability of ALFF. First, the ensemble method was implemented to predict individual treatment responses. Second, the categorical boosting (CatBoost) classification was used to predict individual levodopa responses in patients classified as moderate and superior responders, according to the 50% threshold of improvement. The age, disease duration and treatment dose were controlled as covariates.

RESULTS

No significant difference in clinical data were observed between moderate and superior responders. Using the ensemble method, the regression model showed a significant correlation between the predicted and the observed motor symptoms improvement (r = 0.61, p < 0.01, mean absolute error = 0.11 ± 0.02), measured as a continuous variable. The use of the Catboost algorithm revealed that ALFF was able to differentiate between moderate and superior responders (area under the curve = 0.90). The mainly contributed regions for both models included the bilateral primary motor cortex, the occipital cortex, the cerebellum, and the basal ganglia.

CONCLUSION

Both continuous and binary ALFF values have the potential to serve as promising predictive markers of dopaminergic therapy response in patients with PD.

Neuronal Dynamics of Pain in Parkinson's Disease

Pain is an important non-motor symptom of Parkinson’s disease (PD). It negatively impacts the quality of life. However, the pathophysiological mechanisms underlying pain in PD remain to be elucidated. This study sought to use electroencephalographic (EEG) coherence analysis to compare neuronal synchronization in neuronal networks between patients with PD, with and without pain. Twenty-four patients with sporadic PD were evaluated for the presence of pain. Time-frequency and coherence analyses were performed on their EEG data. Whole-brain and regional coherence were calculated and compared between pain-positive and pain-negative patients. There was no significant difference in the whole-brain coherence between the pain-positive and pain-negative groups. However, temporal–temporal coherence differed significantly between the two groups (p = 0.031). Our findings indicate that aberrant synchronization of inter-temporal regions is involved in PD-related pain. This will further our understanding of the mechanisms underlying pain in PD.

Histone deacetylase in neuropathology

Neuroepigenetics, a new branch of epigenetics, plays an important role in the regulation of gene expression. Neuroepigenetics is associated with holistic neuronal function and helps in formation and maintenance of memory and learning processes. This includes neurodevelopment and neurodegenerative defects in which histone modification enzymes appear to play a crucial role. These modifications, carried out by acetyltransferases and deacetylases, regulate biologic and cellular processes such as apoptosis and autophagy, inflammatory response, mitochondrial dysfunction, cell-cycle progression and oxidative stress. Alterations in acetylation status of histone as well as non-histone substrates lead to transcriptional deregulation. Histone deacetylase decreases acetylation status and causes transcriptional repression of regulatory genes involved in neural plasticity, synaptogenesis, synaptic and neural plasticity, cognition and memory, and neural differentiation. Transcriptional deactivation in the brain results in development of neurodevelopmental and neurodegenerative disorders. Mounting evidence implicates histone deacetylase inhibitors as potential therapeutic targets to combat neurologic disorders. Recent studies have targeted naturally-occurring biomolecules and micro-RNAs to improve cognitive defects and memory. Multi-target drug ligands targeting HDAC have been developed and used in cell-culture and animal-models of neurologic disorders to ameliorate synaptic and cognitive dysfunction. Herein, we focus on the implications of histone deacetylase enzymes in neuropathology, their regulation of brain function and plausible involvement in the pathogenesis of neurologic defects.

Altered spontaneous cortical activity in mild glaucoma: A quantitative EEG study

Functional neuroimaging studies have reported alterations in cortical activity indicating glaucoma as a progressive neurodegenerative disease. Hence the current study aimed to assess the cortical activity using high-density EEG in patients with mild glaucoma during resting state. Treatment-naive 37 patients with primary open angle glaucoma (POAG), 34 patients with primary angle closure glaucoma (PACG), and 32 healthy controls were included in the study. Resting state EEG i.e., eyes closed (EC) and eyes open conditions (EO) were acquired using 128-channel for 3 min. After preprocessing, the current density of 6239 voxels of the data was estimated using sLORETA. In comparison to healthy controls, PACG had higher activity at cingulate gyri, medial and superior frontal gyri during EO only. POAG had significantly higher activity at precentral gyrus and middle frontal gyrus during EC, whereas at cingulate gyri, frontal gyri, precentral gyri, paracentral lobule, sub-gyral region, postcentral gyrus, and precuneus during EO. POAG had significantly higher activity at precuneus and cuneus compared to PACG during EO. Intraocular pressure and mean-deviation of visual fields had a positive correlation with cortical activity. Results of the study indicate physiological alterations not only at the level of retina but also at brain even in the early stages of the disease. These alterations in the cortical activity were more in POAG than PACG. Controlling the IOP alone might be insufficient in glaucoma because of widespread alterations in cortical activity. These findings might enhance the current understanding of cortical involvement in glaucoma.

Aberrant Complement System Activation in Neurological Disorders

The complement system is an assembly of proteins that collectively participate in the functions of the healthy and diseased brain. The complement system plays an important role in the maintenance of uninjured (healthy) brain homeostasis, contributing to the clearance of invading pathogens and apoptotic cells, and limiting the inflammatory immune response. However, overactivation or underregulation of the entire complement cascade within the brain may lead to neuronal damage and disturbances in brain function. During the last decade, there has been a growing interest in the role that this cascading pathway plays in the neuropathology of a diverse array of brain disorders (e.g., acute neurotraumatic insult, chronic neurodegenerative diseases, and psychiatric disturbances) in which interruption of neuronal homeostasis triggers complement activation. Dysfunction of the complement promotes a disease-specific response that may have either beneficial or detrimental effects. Despite recent advances, the explicit link between complement component regulation and brain disorders remains unclear. Therefore, a comprehensible understanding of such relationships at different stages of diseases could provide new insight into potential therapeutic targets to ameliorate or slow progression of currently intractable disorders in the nervous system. Hence, the aim of this review is to provide a summary of the literature on the emerging role of the complement system in certain brain disorders.

EEG-Derived Functional Connectivity Patterns Associated with Mild Cognitive Impairment in Parkinson's Disease

OBJECTIVE

To evaluate EEG-derived functional connectivity (FC) patterns associated with mild cognitive impairment (MCI) in Parkinson's disease (PD).

METHODS

A sample of 15 patients without cognitive impairment (PD-WCI), 15 with MCI (PD-MCI), and 26 healthy subjects were studied. The EEG was performed in the waking functional state with eyes closed, for the functional analysis it was used the synchronization likelihood (SL) and graph theory (GT).

RESULTS

PD-MCI patients showed decreased FC in frequencies alpha, in posterior regions, and delta with a generalized distribution. Patients, compared to the healthy people, presented a decrease in segregation (lower clustering coefficient in alpha p = 0.003 in PD-MCI patients) and increased integration (shorter mean path length in delta (p = 0.004) and theta (p = 0.002) in PD-MCI patients). There were no significant differences in the network topology between the parkinsonian groups. In PD-MCI patients, executive dysfunction correlated positively with global connectivity in beta (r = 0.47) and negatively with the mean path length at beta (r = -0.45); alterations in working memory were negatively correlated with the mean path length at beta r = -0.45.

CONCLUSIONS

PD patients present alterations in the FC in all frequencies, those with MCI show less connectivity in the alpha and delta frequencies. The neural networks of the patients show a random topology, with a similar organization between patients with and without MCI. In PD-MCI patients, alterations in executive function and working memory are related to beta integration.

Exposure to lutein-loaded nanoparticles attenuates Parkinson's model-induced damage in Drosophila melanogaster: Restoration of dopaminergic and cholinergic system and oxidative stress indicators

Parkinson's is a neurodegenerative disease, characterized by the loss of dopaminergic neurons, cholinergic alterations and oxidative damages. Lutein is widely known by its antioxidants properties. In the present study, we investigated whether lutein-loaded nanoparticles protects against locomotor damage and neurotoxicity induced by Parkinson's disease model in Drosophila melanogaster, as well as possible mechanisms of action. First, the nanoparticles were characterized by physicochemical methods, demonstrating that water affinity was improved by the encapsulation of lutein into the polymeric encapsulant matrix. The fruit flies of 1-4 days old were divided into four groups and exposed to a standard diet (control), a diet containing either rotenone (500 μM), lutein-loaded nanoparticles (6 μM) or rotenone (500 μM) and lutein-loaded nanoparticles (6 μM) for 7 days. The survival percentage was assessed, the flies were submitted to negative geotaxis, open field tasks and the determination of dopamine levels, tyrosine hydroxylase (TH) and acetylcholinesterase activities and oxidative stress indicators (superoxide dismutase, catalase, thiobarbituric acid reactive substances and glutathione S-transferase) were carried out. The exposure to lutein-loaded nanoparticles protected against locomotor damage and the decrease survival rate induced by rotenone, besides, it restored the dopamine levels, TH and acetylcholinesterase activities and oxidative stress indicators. These results provide evidence that lutein-loaded nanoparticles are an alternative treatment for rotenone-induced damage, and suggest the involvement of dopaminergic and cholinergic system and oxidative stress.

COVID-19 and neurocognitive disorders

PURPOSE OF REVIEW

The COVID-19 infection results in various viral-related physical and mental health problems, joined with the long-term psychological impact of the pandemic in general. However, the accompanying neurocognitive changes remain poorly understood.

RECENT FINDINGS

We synthetize the current knowledge of viral (SARS-CoV-2) induced inflammation, mechanisms to viral entry into the central nervous system and altered neurotransmitter systems to provide an informed neurobiological explanation for the rise of neurocognitive disorders (defined as per the DSM-5 criteria).

SUMMARY

The mild and major neurocognitive disorder symptoms due to the COVID-19 pandemic provide a unique opportunity to address the early changes underlying neurocognitive impairment at both clinical and molecular level. We discuss the utilization of the available evidence for their management and future novel therapeutic opportunities.

Transcranial Direct Current Stimulation to Enhance Cognitive Impairment in Parkinson's Disease: A Systematic Review and Meta-Analysis

Cognitive deficits are increasingly being recognized as a common trait in Parkinson's disease (PD). Recently, transcranial direct current stimulation (tDCS) has been shown to exert positive effects as an adjunctive therapy on motor and non-motor symptoms in PD. This systematic review and meta-analysis aims to provide an overview of reported evidence on the efficacy of tDCS interventions in the treatment of cognitive impairments in PD. A systematic literature review was conducted to examine articles that were published in the past 10 years and that study the effects of tDCS on cognitive deficits in PD patients. The PubMed, Scopus and Scielo databases were searched. Eight tDCS studies involving 168 participants were included for the analysis. Our meta-analysis results showed that anodal tDCS (atDCS) had various levels or no evidence of effectiveness. In the pre-post stimulation analysis, a strong effect was reported for executive functions (pre-post: g = 1.51, Z = 2.41, p = 0.016); non-significant effects were reported for visuospatial skills (pre-post: g = 0.27, Z = 0.69, p = 0.490); attention (pre-post: g = 0.02, Z = 0.08, p = 0.934), memory (pre-post: g = 0.01, Z = 0.03, p = 0.972) and language (pre-post: g = 0.07, Z = 0.21, p = 0.832). However, in the pre-follow-up stimulation analysis, the duration of the effect was not clear. This study highlights the potential effectiveness of atDCS to improve cognitive performance in PD patients but failed to establish a cause-effect relationship between tDCS intervention and cognitive improvement in PD. Future directions and recommendations for methodological improvements are outlined.

Altered sensorimotor fMRI directed connectivity in Parkinson's disease patients

Dopamine depletion in the axons of Parkinson's disease (PD) patients precedes depletion in cell bodies thus proposing that macroscopic connectivity can be used to understand disease mechanism. A novel multivariate functional connectivity analysis, based on high order coherence among four fMRI BOLD signals was applied on resting-state fMRI data of controls and PD patients (OFF and ON medication states) and unidirectional multiple-region pathways in the sensorimotor system were identified. Pathways were classified as "preserved" (unaffected by the disease), "damaged" (not observed in patients) and "corrected" (observed in controls and in PD-ON state). The majority of all pathways were feedforward, most of them with the pattern "S1→M1→SMA." Of these pathways, 67% were "damaged," 28% "preserved," and 5% "corrected." Prefrontal cortex (PFC) afferent and efferent pathways that corresponded to goal directed and habitual activities corresponded to recurrent circuits. Eighty-one percent of habitual afferent had internal cue (i.e., M1→S1→), of them 79% were "damaged" and the rest "preserved." All goal-directed afferent had external cue (i.e., S1→M1→) with third "damaged," third "preserved," and third "corrected." Corrected pathways were initiated in the dorsolateral PFC. Reduced connectivity of the SMA and PFC resulted from reduced sensorimotor afferent to these regions. Reduced sensorimotor internal cues to the PFC resulted with reduced habitual processes. Levodopa effects were for pathways that started in region reach with dopamine receptors. This methodology can enrich understudying of PD mechanisms in other (e.g., the default mode network) systems.

Investigation of sensorimotor dysfunction in Parkinson disease by resting-state fMRI

PURPOSE

Functional MRI has played a fundamental role in Parkinson's disease(PD) study. In this paper, we performed an independent component analysis (ICA) based on functional networks to reveal the intricate variations on the morphology and functional properties of brain. Our analysis aims at discovering the differences between PD patients with sensorimotor function impairment and normal controls(NC), thus helping to understand the coordination neurological function degeneration in PD objectively.

METHOD

We investigated the blood oxygen level dependent(BOLD) functional MRI obtained at a 3.0 T MRI scanner. 30 PD patients and 28 NC subjects underwent the scan in resting state. The signals of sensory and motor coordinative control areas in the sensorimotor, insula and cerebellum networks acquired by ICA(Independent Component Analysis)were applied to analyze the functional alterations. Specifically, intra-network analysis was performed with signals in local networks, and inter-network analysis was conducted by functional network connectivity (FNC) with signals across different networks. Two sample T test was carried out to detect the significant (p < 0.05, FDR p < 0.05) functional abnormality in PD patients.

CONCLUSION

We identified an obvious increase in bilateral posterior insula, but decrease in bilateral cerebellum hemisphere, supplementary motor area(SMA) and precentral gyrus paracentral lobule of left postcentral gyrus. Besides, we found a significantly increased connection between independent component (IC) 13 which was located in right postcentral gyrus and cerebellum. Decreased connections were detected between sensory and motor cortex in sensorimotor network and between cerebellum and insula network by FNC analysis in PD patients as well.

DISCUSSION

Parkinson's disease derives from the degeneration of the dopaminergic neurons in substantia nigra, and results in decreased secretion of inhibitory neurotransmitter. The significant differences between PD and NC groups in our research maybe explain the clinical manifestations of prominent bradykinesia and multiple extrapyramidal symptoms.

An investigation of thalamic nuclei volumes and the intrinsic thalamic structural network based on motor subtype in drug naïve patients with Parkinson's disease

INTRODUCTION

This study aimed to investigate the alterations in thalamic nuclei volumes and the intrinsic thalamic structural network in patients with de novo Parkinson's disease (PD) based on their predominant symptoms.

METHODS

We enrolled 65 patients with de novo PD (44 patients with tremor-dominant [TD] subtype and 21 patients with postural instability and gait disturbance [PIGD] subtype) and 20 healthy controls. All subjects underwent three-dimensional T1-weighted magnetic resonance imaging. The thalamic nuclei were segmented using the FreeSurfer program.

RESULTS

We obtained volumetric differences in the thalamic nuclei of each subtype of PD in comparison of healthy control. Volumes of the right and left suprageniculate nuclei were significantly increased, whereas that of the left parafascicular nucleus was decreased in patients with the TD subtype. Volumes of the right and left suprageniculate nuclei and right ventromedial nucleus were significantly increased, whereas those of the right and left parafascicular nuclei volumes were decreased in patients with the PIGD subtype. The measures of the intrinsic thalamic global network were not different between patients with TD PD and healthy controls. However, in patients with the PIGD subtype, the global and local efficiencies were significantly increased compared to healthy controls. Moreover, although there were no differences in thalamic volume and intrinsic thalamic global network between patients with the TD and PIGD variants, we identified significant differences in the intrinsic thalamic local network between the two groups.

CONCLUSIONS

Alterations in thalamic nuclei volumes and the intrinsic thalamic network in patients with PD differed based on their predominant symptoms. These findings might be related to the underlying pathogenesis and suggest that PD is a heterogeneous syndrome.

Acquisition Duration in Resting-State Arterial Spin Labeling. How Long Is Enough?

Resting-state Arterial Spin Labeling (rs-ASL) is a rather confidential method compared to resting-state BOLD. As ASL allows to quantify the cerebral blood flow, unlike BOLD, rs-ASL can lead to significant clinical subject-scaled applications. Despite directly impacting clinical practicability and functional networks estimation, there is no standard for rs-ASL regarding the acquisition duration. Our work here focuses on assessing the feasibility of ASL as an rs-fMRI method and on studying the effect of the acquisition duration on the estimation of functional networks. To this end, we acquired a long 24 min 30 s rs-ASL sequence and investigated how estimations of six typical functional brain networks evolved with respect to the acquisition duration. Our results show that, after a certain acquisition duration, the estimations of all functional networks reach their best and are stabilized. Since, for clinical application, the acquisition duration should be the shortest possible, we suggest an acquisition duration of 14 min, i.e., 240 volumes with our sequence parameters, as it covers the functional networks estimation stabilization.

Repetitive transcranial magnetic stimulation improves Parkinson's freezing of gait via normalizing brain connectivity

Robust, effective treatments for Parkinson's freezing of gait remain elusive. Our previous study revealed beneficial effects of high-frequency rTMS over the supplementary motor area. The present study aims to explore the neural mechanisms of rTMS treatments utilizing novel exploratory multivariate approaches. We first conducted a resting-state functional MRI study with a group of 40 Parkinson's disease patients with freezing of gait, 31 without freezing of gait, and 30 normal controls. A subset of 30 patients with freezing of gait (verum group: N = 20; sham group: N = 10) who participated the aforementioned rTMS study underwent another scan after the treatments. Using the baseline scans, the imaging biomarkers for freezing of gait and Parkinson's disease were developed by contrasting the connectivity profiles of patients with freezing of gait to those without freezing of gait and normal controls, respectively. These two biomarkers were then interrogated to assess the rTMS effects on connectivity patterns. Results showed that the freezing of gait biomarker was negatively correlated with Freezing of Gait Questionnaire score (r = -0.6723, p < 0.0001); while the Parkinson's disease biomarker was negatively correlated with MDS-UPDRS motor score (r = -0.7281, p < 0.0001). After the rTMS treatment, both the freezing of gait biomarker (0.326 ± 0.125 vs. 0.486 ± 0.193, p = 0.0071) and Parkinson's disease biomarker (0.313 ± 0.126 vs. 0.379 ± 0.155, p = 0.0378) were significantly improved in the verum group; whereas no significant biomarker changes were found in the sham group. Our findings indicate that high-frequency rTMS over the supplementary motor area confers the beneficial effect jointly through normalizing abnormal brain functional connectivity patterns specifically associated with freezing of gait, in addition to normalizing overall disrupted connectivity patterns seen in Parkinson's disease.

[Clinical and neurophysiological effects of dual-target high-frequency rTMS over the primary motor and prefrontal cortex in Parkinson's disease]

OBJECTIVE

To evaluate therapeutic effects of navigational dual-target high-frequency rTMS over the primary motor (M1, bilateral) and the left dorsolateral prefrontal cortex (DLPFC) on clinical dynamics of Parkinson's disease (PD) symptoms in a parallel placebo-controlled study.

MATERIAL AND METHODS

The study included 46 patients randomized into equal therapeutic and placebo rTMS groups. Navigational therapeutic and placebo10 Hz rTMS was applied over the M1 and DLPFC areas (20 daily sessions, for 3 weeks). Assessment of the dynamics of clinical symptoms was performed using the MDS UPDRS scale (Parts I-IV) before the first session, immediately after 20 sessions, and 4-6 weeks after the rTMS course. Non-motor and mental symptoms were assessed using the Hamilton Depression Rating Scale (HDRS-17), Beck depression inventory (BDI-II), Depression, Anxiety and Stress (DASS-21) scales and the Mini Mental State Examination (MMSE).

RESULTS

Significant therapeutic effects of rTMS compared to placebo were established: a greater decrease in overall score on the MDS-UPDRS scale (parts I-IV), a decrease in the severity of non-motor (part I) and motor symptoms (part III, with a large therapeutic effect for the symptoms of rigidity, bradykinesia and postural instability), as well as the severity of motor complications of dopamine replacement therapy (part IV). The effects of rTMS on motor symptoms persisted 4 weeks after the end of the stimulation course. It is also important to note significant positive dynamics in both rTMS and placebo groups in the form of comparable reduction in the severity of everyday motor symptoms (MDS-UPDRS part II), improvement of the total scores on MMSE, HDRS, BDI-II, DASS-21.

CONCLUSIONS

The dual-target high-frequency rTMS over the primary motor cortex (bilateral) and the left dorsolateral prefrontal cortex has positive therapeutic effects on the motor and affective symptoms of Parkinson's disease, which are significantly stronger than that of the placebo stimulation.

New insight into Parkinson's disease-related impairment of the automatic control of upright stance

Parkinson's disease (PD) affects the automatic control of body movements. In our study, we tested PD-related impairments in automatic postural control in quiet upright stance. Twenty PD patients (mean age: 60 ± 8 years; Hoehn and Yahr: 2.00 ± 0.32, on-drug) and twenty age-matched controls (61 ± 7 years) were recruited. We studied interrelations between center-of-pressure movements, body movements (head, neck, and lower back), eye movements and variability of pupil size. Participants performed two fixation tasks while standing, during which they looked at: (a) a cross surrounded by a white background; and (b) a cross surrounded by a structured visual background (images used: rooms in houses). PD patients exhibited stronger and weaker correlations between eye and center-of-pressure/body movement variables than age-matched controls in the white and structured fixation tasks, respectively. Partial correlations, controlling for variability of pupil size showed that PD patients used lower and greater attentional resources than age-matched controls to control their eye and center-of-pressure/body movements simultaneously in the white fixation and structured fixation tasks, respectively. In the white fixation task, PD patients used attentional resources to optimize visuomotor coupling between eye and body movements to control their posture. In the structured fixation task, the salient visual stimuli distracted PD patients' attention and that possibly affected postural control by deteriorating the automatic visuomotor coupling. In contrast, age-matched controls were able to use surrounding visual background to improve the automatic coupling between eye and center-of-pressure movements to control their posture. These results suggest that cluttered environments may distract PD patients and deteriorate their postural control.

Common and unique connectivity at the interface of motor, neuropsychiatric, and cognitive symptoms in Parkinson's disease: A commonality analysis

Parkinson's disease (PD) is characterized by overlapping motor, neuropsychiatric, and cognitive symptoms. Worse performance in one domain is associated with worse performance in the other domains. Commonality analysis (CA) is a method of variance partitioning in multiple regression, used to separate the specific and common influence of collinear predictors. We apply, for the first time, CA to the functional connectome to investigate the unique and common neural connectivity underlying the interface of the symptom domains in 74 non-demented PD subjects. Edges were modeled as a function of global motor, cognitive, and neuropsychiatric scores. CA was performed, yielding measures of the unique and common contribution of the symptom domains. Bootstrap confidence intervals were used to determine the precision of the estimates and to directly compare each commonality coefficient. The overall model identified a network with the caudate nucleus as a hub. Neuropsychiatric impairment accounted for connectivity in the caudate-dorsal anterior cingulate and caudate-right dorsolateral prefrontal-right inferior parietal circuits, while caudate-medial prefrontal connectivity reflected a unique effect of both neuropsychiatric and cognitive impairment. Caudate-precuneus connectivity was explained by both unique and shared influence of neuropsychiatric and cognitive symptoms. Lastly, posterior cortical connectivity reflected an interplay of the unique and common effects of each symptom domain. We show that CA can determine the amount of variance in the connectome that is unique and shared amongst motor, neuropsychiatric, and cognitive symptoms in PD, thereby improving our ability to interpret the data while gaining novel insight into networks at the interface of these symptom domains.

Neuropsychiatric aspects of Parkinson disease psychopharmacology: Insights from circuit dynamics

Parkinson disease (PD) is a neurodegenerative disorder with a complex pathophysiology characterized by the progressive loss of dopaminergic neurons within the substantia nigra. Persons with PD experience several motoric and neuropsychiatric symptoms. Neuropsychiatric features of PD include depression, anxiety, psychosis, impulse control disorders, and apathy. In this chapter, we will utilize the National Institutes of Mental Health Research Domain Criteria (RDoC) to frame and integrate observations from two prevailing disease constructions: neurotransmitter anomalies and circuit physiology. When there is available evidence, we posit how unified translational observations may have clinical relevance and postulate importance outside of PD. Finally, we review the limited evidence available for pharmacologic management of these symptoms.

Impaired functional connectivity of sensorimotor network predicts recovery in drug-induced parkinsonism

OBJECTIVE

In a substantial portion of patients with drug-induced parkinsonism (DIP), parkinsonism may persist for long periods after discontinuation of offending drugs, suggesting subtle underlying neurodegeneration. We hypothesized that patients with DIP have impaired functional connectivity (FC) of brain networks, which may determine the reversibility of parkinsonism.

METHODS

In this case-control study, we consecutively recruited 60 patients with DIP and 32 healthy controls. We used independent component analysis and dual regression of functional magnetic resonance imaging data to identify seven resting-state networks and compared FC of the networks between the DIP and control groups. Among regions where the two groups showed a significant difference in the FC with sensorimotor network, we compared the FC between patients who had completely recovered (n = 21) and those who had partially recovered (n = 39) within 3 months of cessation of the offending drugs.

RESULTS

Patients with DIP had decreased FC between the sensorimotor network and widespread brain regions, when compared to healthy controls. FC in the prefrontal regions was negatively correlated with parkinsonian motor score. Patients who partially recovered had a significantly lower FC in the prefrontal and cerebellar regions than those who recovered completely, providing a useful predictor of recovery status.

CONCLUSIONS

Patients with DIP had decreased FC of the sensorimotor network, which correlated with the severity of parkinsonism and predicted the recovery status after cessation of offending drugs. Impaired FC of the sensorimotor network can be used as a biomarker to evaluate the severity and prognosis of DIP.

ALFF and ReHo Mapping Reveals Different Functional Patterns in Early- and Late-Onset Parkinson's Disease

Heterogeneity between late-onset Parkinson's disease (LOPD) and early-onset Parkinson's disease (EOPD) is mainly reflected in the following aspects including genetics, disease progression, drug response, clinical manifestation, and neuropathological change. Although many studies have investigated these differences in relation to clinical significance, the functional processing circuits and underlying neural mechanisms have not been entirely understood. In this study, regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) maps were used to explore different spontaneous brain activity patterns in EOPD and LOPD patients. Abnormal synchronizations were found in the motor and emotional circuits of the EOPD group, as well as in the motor, emotional, and visual circuits of the LOPD group. EOPD patients showed functional activity change in the visual, emotional and motor circuits, and LOPD patients only showed increased functional activity in the emotional circuits. In summary, the desynchronization process in the LOPD group was relatively strengthened, and the brain areas with changed functional activity in the EOPD group were relatively widespread. The results might point out different impairments in the synchronization and functional activity for EOPD and LOPD patients.

Analysis of brain subnetworks within the context of their whole-brain networks

Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole-brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed-effects modeling framework that allows analyzing subnetworks within the context of their whole-brain networks, and show that it can better disentangle global (whole-brain) and local (subnetwork) differences when compared to standard t-test analyses. T-test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system-level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model-quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks-but for local, subnetwork-level hypotheses.