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

Measures of the coupling between fluctuating brain network organization and heartbeat dynamics

In recent years, there has been an increasing interest in studying brain-heart interactions. Methodological advancements have been proposed to investigate how the brain and the heart communicate, leading to new insights into some neural functions. However, most frameworks look at the interaction of only one brain region with heartbeat dynamics, overlooking that the brain has functional networks that change dynamically in response to internal and external demands. We propose a new framework for assessing the functional interplay between cortical networks and cardiac dynamics from noninvasive electrophysiological recordings. We focused on fluctuating network metrics obtained from connectivity matrices of EEG data. Specifically, we quantified the coupling between cardiac sympathetic-vagal activity and brain network metrics of clustering, efficiency, assortativity, and modularity. We validate our proposal using open-source datasets: one that involves emotion elicitation in healthy individuals, and another with resting-state data from patients with Parkinson's disease. Our results suggest that the connection between cortical network segregation and cardiac dynamics may offer valuable insights into the affective state of healthy participants, and alterations in the network physiology of Parkinson's disease. By considering multiple network properties, this framework may offer a more comprehensive understanding of brain-heart interactions. Our findings hold promise in the development of biomarkers for diagnostic and cognitive/motor function evaluation.

Assessment of noninvasive brain stimulation interventions in Parkinson's disease: a systematic review and network meta-analysis

A network meta-analysis of randomized controlled trials was conducted to compare and rank the effectiveness of various noninvasive brain stimulation (NIBS) for Parkinson's disease (PD). We searched PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and Chinese Biomedical Literature Service System (SinoMed) databases from the date of database inception to April 30th, 2024. Two researchers independently screened studies of NIBS treatment in patients with PD based on inclusion and exclusion criteria. Two researchers independently performed data extraction of the included studies using an Excel spreadsheet and assessed the quality of the literature according to the Cochrane Risk of Bias Assessment Tool (RoB2). Network meta-analysis was performed in StataMP 17.0. A total of 28 studies involving 1628 PD patients were included. The results showed that HF-rTMS over the SMA (SMD = - 2.01; 95% CI [- 2.87, - 1.15]), HF-rTMS over the M1 and DLPFC (SMD = - 1.80; 95% CI [- 2.90, - 0.70]), HF-rTMS over the M1 (SMD = - 1.10; 95% CI [- 1.55, - 0.65]), a-tDCS over the DLPFC (SMD = - 1.08; 95% CI [- 1.90, - 0.27]), HF-rTMS over the M1 and PFC (SMD = - 0.92; 95% CI [- 1.71, - 0.14]), LF-rTMS over the M1 (SMD = - 0.72; 95% CI [- 1.17, - 0.28]), and HF-rTMS over the DLPFC (SMD = - 0.70; 95% CI [- 1.21, - 0.19]) were significantly improved motor function compared with sham stimulation. The SUCRA three highest ranked were HF-rTMS over the SMA (95.1%), HF-rTMS over the M1 and DLPFC (89.6%), and HF-rTMS over the M1 (73.0%). In terms of enhanced cognitive function, HF-rTMS over the DLPFC (SMD = 0.80; 95% CI [0.03,1.56]) was significantly better than sham stimulation. The SUCRA three most highly ranked were a-tDCS over the M1 (69.8%), c-tDCS over the DLPFC (66.9%), and iTBS over the DLPFC (65.3%). HF-rTMS over the M1 (SMD = - 1.43; 95% CI [- 2.26, - 0.61]) and HF-rTMS over the DLPFC (SMD = - 0.79; 95% CI [- 1.45, - 0.12)]) significantly improved depression. The SUCRA three highest ranked were HF-rTMS over the M1 (94.1%), LF-rTMS over the M1 (71.8%), and HF-rTMS over the DLPFC (69.0%). HF-rTMS over the SMA may be the best option for improving motor symptoms in PD patients. a-tDCS and HF-rTMS over the M1 may be the NIBS with the most significant effects on cognition and depression, separately.Trial registration: International Prospective Register of Systematic Review, PROSPERO (CRD42023456088).

A framework for quantifying the coupling between brain connectivity and heartbeat dynamics: Insights into the disrupted network physiology in Parkinson's disease

Parkinson's disease (PD) often shows disrupted brain connectivity and autonomic dysfunctions, progressing alongside with motor and cognitive decline. Recently, PD has been linked to a reduced sensitivity to cardiac inputs, that is, cardiac interoception. Altogether, those signs suggest that PD causes an altered brain-heart connection whose mechanisms remain unclear. Our study aimed to explore the large-scale network disruptions and the neurophysiology of disrupted interoceptive mechanisms in PD. We focused on examining the alterations in brain-heart coupling in PD and their potential connection to motor symptoms. We developed a proof-of-concept method to quantify relationships between the co-fluctuations of brain connectivity and cardiac sympathetic and parasympathetic activities. We quantified the brain-heart couplings from electroencephalogram and electrocardiogram recordings from PD patients on and off dopaminergic medication, as well as in healthy individuals at rest. Our results show that the couplings of fluctuating alpha and gamma connectivity with cardiac sympathetic dynamics are reduced in PD patients, as compared to healthy individuals. Furthermore, we show that PD patients under dopamine medication recover part of the brain-heart coupling, in proportion with the reduced motor symptoms. Our proposal offers a promising approach to unveil the physiopathology of PD and promoting the development of new evaluation methods for the early stages of the disease.

MRI brain structural and functional networks changes in Parkinson disease with REM sleep behavior disorders

Background

Rapid eye movement sleep behavior disorder (RBD) is common in individuals with Parkinson's disease (PD). In spite of that, the precise mechanism underlying the pathophysiology of RBD among PD remains unclear.

Objective

The aim of the present study was to analyze gray matter volumes (GMVs) as well as the changes of functional connectivity (FC) among PD patients with RBD (PD-RBD) by employing a combination of voxel-based morphometry (VBM) and FC methods.

Methods

A total of 65 PD patients and 21 healthy control (HC) subjects were included in this study. VBM analyses were performed on all subjects. Subsequently, regions with significant different GMVs between PD patients with and without RBD (PD-nRBD) were selected for further analysis of FC. Correlations between altered GMVs and FC values with RBD scores were also investigated. Additionally, receiver operating characteristic (ROC) curves were employed for the evaluation of the predictive value of GMVs and FC in identifying RBD in PD.

Results

PD-RBD patients exhibited lower GMVs in the left middle temporal gyrus (MTG) and bilateral cuneus. Furthermore, we observed higher FC between the left MTG and the right postcentral gyrus (PoCG), as well as lower FC between the bilateral cuneus (CUN) and the right middle frontal gyrus (MFG) among PD-RBD patients in contrast with PD-nRBD patients. Moreover, the GMVs of MTG (extending to the right PoCG) was positively correlated with RBD severity [as measured by REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) score]. Conversely, the FC value between the bilateral CUN and the right MTG in PD-RBD patients was negatively correlated with RBDSQ score.

Conclusion

This study revealed the presence replace with GMV and FC changes among PD-RBD patients, which were closely linked to the severity of RBD symptoms. Furthermore, the combination of basic clinical characteristics, GMVs and FC values effectively predicted RBD for individuals with PD.

Intrinsic brain functional connectivity predicts treatment-related motor complications in early Parkinson's disease patients

BACKGROUND

Treatment-related motor complications may develop progressively over the course of Parkinson's disease (PD).

OBJECTIVE

We investigated intrinsic brain networks functional connectivity (FC) at baseline in a cohort of early PD patients which successively developed treatment-related motor complications over 4 years.

METHODS

Baseline MRI images of 88 drug-naïve PD patients and 20 healthy controls were analyzed. After the baseline assessments, all PD patients were prescribed with dopaminergic treatment and yearly clinically re-assessed. At the 4-year follow-up, 36 patients have developed treatment-related motor complications (PD-Compl) whereas 52 had not (PD-no-Compl). Single-subject and group-level independent component analyses were used to investigate FC changes within the major large-scale resting-state networks at baseline. A multivariate Cox regression model was used to explore baseline predictors of treatment-related motor complications at 4-year follow-up.

RESULTS

At baseline, an increased FC in the right middle frontal gyrus within the frontoparietal network as well as a decreased connectivity in the left cuneus within the default-mode network were detected in PD-Compl compared with PD-no-Compl. PD-Compl patients showed a preserved sensorimotor FC compared to controls. FC differences were found to be independent predictors of treatment-related motor complications over time.

CONCLUSION

Our findings demonstrated that specific FC differences may characterize drug-naïve PD patients more prone to develop treatment-related complications. These findings may reflect the presence of an intrinsic vulnerability across frontal and prefrontal circuits, which may be potentially targeted as a future biomarker in clinical trials.

Optimization of structural connectomes and scaled patterns of structural-functional decoupling in Parkinson's disease

Parkinson's disease (PD) is manifested with disrupted topology of the structural connection network (SCN) and the functional connection network (FCN). However, the SCN and its interactions with the FCN remain to be further investigated. This multimodality study attempted to precisely characterize the SCN using diffusion kurtosis imaging (DKI) and further identify the neuropathological pattern of SCN-FCN decoupling, underscoring the neurodegeneration of PD. Diffusion-weighted imaging and resting-state functional imaging were available for network constructions among sixty-nine patients with PD and seventy demographically matched healthy control (HC) participants. The classification performance and topological prosperities of both the SCN and the FCN were analyzed, followed by quantification of the SCN-FCN couplings across scales. The SCN constructed by kurtosis metrics achieved optimal classification performance (area under the curve 0.89, accuracy 80.55 %, sensitivity 78.40 %, and specificity 80.65 %). Along with diverse alterations of structural and functional network topology, the PD group exhibited decoupling across scales including: reduced global coupling; increased nodal coupling within the sensorimotor network (SMN) and subcortical network (SN); higher intramodular coupling within the SMN and SN and lower intramodular coupling of the default mode network (DMN); decreased coupling between the modules of DMN-fronto-parietal network and DMN-visual network, but increased coupling between the SMN-SN module. Several associations between the coupling coefficient and topological properties of the SCN, as well as between network values and clinical scores, were observed. These findings validated the clinical implementation of DKI for structural network construction with better differentiation ability and characterized the SCN-FCN decoupling as supplementary insight into the pathological process underlying PD.

Abnormal alterations of regional spontaneous neuronal activity and functional connectivity in insomnia patients with difficulty falling asleep: a resting-state fMRI study

BACKGROUND

Insomnia disorder (ID) seriously affects people's daily life. Difficulty falling asleep is the most commonly reported complaint in patients with ID. However, the mechanism of prolonged sleep latency (SL) is still obscure. The aim of our present study was to investigate the relationship between prolonged SL and alterations in spontaneous neural activity and brain functional connectivity (FC) in ID patients using functional magnetic resonance imaging (fMRI).

METHODS

A total of 52 insomniacs with difficulty falling asleep and 30 matched healthy controls (HCs) underwent resting-state fMRI. The amplitude of low-frequency fluctuation (ALFF) was measured and group differences were compared. The peak areas with significantly different ALFF values were identified as the seed regions to calculate FC to the whole brain. SL was assessed by a wrist actigraphy device in ID patients. The Pittsburgh Sleep Quality Index (PSQI), Hamilton Anxiety Rating Scale (HAMA), and Hyperarousal Scale (HAS) were evaluated in both ID patients and HCs. Finally, correlation analyses were performed between the clinical features and FC/ALFF values.

RESULTS

ID patients showed higher PSQI, HAMA, HAS scores than HCs. The functional MRI results indicated increased ALFF value in the left insula and right amygdala and decreased ALFF value in the right superior parietal lobe (SPL) in ID patients. The seed-based FC analysis demonstrated increased FC between the left insula and the bilateral precentral gyrus and FC between the right amygdala and the left posterior cingulate cortex (PCC) in patients with ID. Correlation analysis indicated that the increased FC value of the right amygdala-left PCC was positively correlated with SL measured by actigraphy.

CONCLUSION

This study revealed abnormal regional spontaneous fluctuations in the right amygdala, left insula, and right SPL, as well as increased FC in the left insula-precentral and right amygdala-left PCC. Moreover, the prolonged SL was positively correlated with the abnormal FC in the right amygdala-left PCC in ID patients. The current study showed the correlation between prolonged SL and the abnormal function of emotion-related brain regions in ID patients, which may contribute to a better understanding of the neural mechanisms underlying difficulty falling asleep in patients with ID.

CLINICAL TRIAL REGISTRATION

http://www.chictr.org.cn ., ChiCTR1800015282. Registered on 20th March 2018.

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.

The pollutome-connectome axis: a putative mechanism to explain pollution effects on neurodegeneration

The study of pollutant effects is extremely important to address the epochal challenges we are facing, where world populations are increasingly moving from rural to urban centers, revolutionizing our world into an urban world. These transformations will exacerbate pollution, thus highlighting the necessity to unravel its effect on human health. Epidemiological studies have reported that pollution increases the risk of neurological diseases, with growing evidence on the risk of neurodegenerative disorders. Air pollution and water pollutants are the main chemicals driving this risk. These chemicals can promote inflammation, acting in synergy with genotype vulnerability. However, the biological underpinnings of this association are unknown. In this review, we focus on the link between pollution and brain network connectivity at the macro-scale level. We provide an updated overview of epidemiological findings and studies investigating brain network changes associated with pollution exposure, and discuss the mechanistic insights of pollution-induced brain changes through neural networks. We explain, in detail, the pollutome-connectome axis that might provide the functional substrate for pollution-induced processes leading to cognitive impairment and neurodegeneration. We describe this model within the framework of two pollutants, air pollution, a widely recognized threat, and polyfluoroalkyl substances, a large class of synthetic chemicals which are currently emerging as new neurotoxic source.

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

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

Aberrant changes of dynamic global synchronization in patients with Parkinson's disease

BACKGROUND

Patients with Parkinson's disease (PD) have been documented with disrupted dynamic profiles of functional connectivity. However, the complementary information that is relevant to the dynamic pattern of global synchronization in patients with PD requires further investigation.

PURPOSE

To reveal the aberrant dynamic profiles of global synchronization involved in PD with a focus on temporal variability, strength, and property.

MATERIAL AND METHODS

A total of 46 patients with PD and 50 matched healthy controls (HCs) were enrolled. Degree centrality (DC) was used as the metric of global synchronization. The intergroup differences in the dynamic DC (dDC) pattern were compared, followed by further analysis of their clinical relevance in PD.

RESULTS

Relative to HCs, the PD group showed decreased dDC variability in right inferior occipital gyrus, right insula, right middle occipital gyrus (MOG), and bilateral postcentral gyrus. The dDC variability in the MOG was significantly correlated with MoCA score. Two states (state I and state II) were suggested. Relative to HCs, the PD group demonstrated a shorter mean dwell time (MDT) in state I, a longer MDT in state II, and fewer transitions. For the PD group, dDC properties were significantly correlated with UPDRS-III scores. In state II, significantly decreased dynamic dDC strength in bilateral supplementary motor area was observed in the PD group, with a significant correlation with UPDRS-III scores.

CONCLUSION

These findings on PD imply that dynamic alterations of global synchronization are engaged in the dysfunction of movement and cognition, deepening the understanding of deteriorations that underlie PD with complementary evidence.

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.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

Altered functional connectivity of cerebellar dentate nucleus in peak-dose dyskinesia in Parkinson’s disease

The cerebellum is associated with the emergence of levodopa-induced dyskinesia (LID) in Parkinson’s disease (PD), yet the neural mechanism remains obscure. Our aim was to ascertain the role of functional connectivity (FC) patterns of the cerebellar dentate nucleus (DN) in the pathogenesis of peak-dose dyskinesia in PD. Twenty-three peak-dose dyskinetic PD patients, 27 non-dyskinetic PD patients, and 36 healthy controls (HCs) were enrolled and underwent T1-weighted and resting-state functional magnetic resonance imaging (rs-fMRI) scans after dopaminergic medication intake. We selected left and right DN as the regions of interest and then employed voxel-wise FC analysis and voxel-based morphometry analysis (VBM). The correlations between the altered FC pattern and clinical scores were also examined. Finally, receiver operating characteristic (ROC) curve analysis was performed to assess the potential of DN FC measures as a feature of peak-dose dyskinesia in PD. Dyskinetic PD patients showed excessively increased FC between the left DN and right putamen compared with the non-dyskinetic. When compared with controls, dyskinetic PD patients mainly exhibited increased FC between left DN and bilateral putamen, left paracentral lobule, right postcentral gyrus, and supplementary motor area. Additionally, non-dyskinetic PD patients displayed increased FC between left DN and left precentral gyrus and right paracentral lobule compared with controls. Meanwhile, increased FC between DN (left/right) and ipsilateral cerebellum lobule VIII was observed in both PD subgroups. However, no corresponding alteration in gray matter volume (GMV) was found. Further, a positive correlation between the z-FC values of left DN-right putamen and the Unified Dyskinesia Rating Scale (UDysRS) was confirmed in dyskinetic PD patients. Notably, ROC curve analyses revealed that the z-FC values of left DN-right putamen could be a potential neuroimaging feature identifying dyskinetic PD patients. Our findings demonstrated that the excessively strengthened connectivity of DN-putamen might contribute to the pathophysiological mechanisms of peak-dose dyskinesia in PD.

Gray matter microstructural alterations in manganese-exposed welders: a preliminary neuroimaging study

OBJECTIVES

Chronic occupational manganese (Mn) exposure is characterized by motor and cognitive dysfunction. This study aimed to investigate structural abnormalities in Mn-exposed welders compared to healthy controls (HCs).

METHODS

Thirty-five HCs and forty Mn-exposed welders underwent magnetic resonance imaging (MRI) scans in this study. Based on T1-weighted MRI, the voxel-based morphometry (VBM), structural covariance, and receiver operating characteristic (ROC) curve were applied to examine whole-brain structural changes in Mn-exposed welders.

RESULTS

Compared to HCs, Mn-exposed welders had altered gray matter volume (GMV) mainly in the medial prefrontal cortex, lentiform nucleus, hippocampus, and parahippocampus. ROC analysis indicated the potential highest classification power of the hippocampus/parahippocampus. Moreover, distinct structural covariance patterns in the two groups were associated with regions, mainly including the thalamus, insula, amygdala, sensorimotor area, and middle temporal gyrus. No significant relationships were found between the findings and clinical characteristics.

CONCLUSIONS

Our findings showed Mn-exposed welders had changed GMV and structural covariance patterns in some regions, which implicated in motivative response, cognitive control, and emotional regulation. These results might provide preliminary evidence for understanding the pathophysiology of Mn overexposure.

KEY POINTS

• Chronic Mn exposure might be related to abnormal brain structural neural mechanisms. • Mn-exposed welders had morphological changes in brain regions implicated in emotional modulation, cognitive control, and motor-related response. • Altered gray matter volume in the hippocampus/parahippocampus and putamen might serve as potential biomarkers for Mn overexposure.

Neurofunctional characteristics of executive control in older people with HIV infection: a comparison with Parkinson's disease

Expression of executive dysfunctions is marked by substantial heterogeneity in people living with HIV infection (PLWH) and attributed to neuropathological degradation of frontostriatal circuitry with age and disease. We compared the neurophysiology of executive function in older PLWH and Parkinson's disease (PD), both affecting frontostriatal systems. Thirty-one older PLWH, 35 individuals with PD, and 28 older healthy controls underwent executive task-activated fMRI, neuropsychological testing, and a clinical motor exam. fMRI task conditions distinguished cognitive control operations, invoking a lateral frontoparietal network, and motor control operations, activating a cerebellar-precentral-medial prefrontal network. HIV-specific findings denoted a prominent sensorimotor hypoactivation during cognitive control and striatal hypoactivation during motor control related to CD4⁺ T cell count and HIV disease duration. Activation deficits overlapped for PLWH and PD, relative to controls, in dorsolateral frontal, medial frontal, and middle cingulate cortices for cognitive control, and in limbic, frontal, parietal, and cerebellar regions for motor control. Thus, despite well-controlled HIV infection, frontostriatal and sensorimotor activation deficits occurred during executive control in older PLWH. Overlapping activation deficits in posterior cingulate and hippocampal regions point toward similarities in mesocorticolimbic system aberrations among older PLWH and PD. The extent of pathophysiology in PLWH was associated with variations in immune system health, neural signature consistent with subclinical parkinsonism, and mild neurocognitive impairment. The failure to adequately engage these pathways could be an early sign for cognitive and motor functional decline in the aging population of PLWH.

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

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

Dopaminergic modulation of primary motor cortex: From cellular and synaptic mechanisms underlying motor learning to cognitive symptoms in Parkinson's disease

The primary motor cortex (M1) is crucial for movement execution, especially dexterous ones, but also for cognitive functions like motor learning. The acquisition of motor skills to execute dexterous movements requires dopamine-dependent and -independent plasticity mechanisms within M1. In addition to the basal ganglia, M1 is disturbed in Parkinson's disease (PD). However, little is known about how the lack of dopamine (DA), characteristic of PD, directly or indirectly impacts M1 circuitry. Here we review data from studies of PD patients and the substantial research in non-human primate and rodent models of DA depletion. These models enable us to understand the importance of DA in M1 physiology at the behavioral, network, cellular, and synaptic levels. We first summarize M1 functions and neuronal populations in mammals. We then look at the origin of M1 DA and the cellular location of its receptors and explore the impact of DA loss on M1 physiology, motor, and executive functions. Finally, we discuss how PD treatments impact M1 functions.

Altered Dynamic Functional Connectivity in de novo Parkinson’s Disease Patients With Depression

Background

Depression is one of the most prevalent and disturbing non-motor symptoms in Parkinson’s disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics in de novo patients with PD with depression (dPD).

Methods

We performed dFC analysis on the data of resting-state functional MRI from 21 de novo dPD, 34 de novo patients with PD without depression (ndPD), and 43 healthy controls (HCs). Group independent component analysis, a sliding window approach, followed by k-means clustering were conducted to assess functional connectivity states (which represented highly structured connectivity patterns reoccurring over time) and temporal properties for comparison between groups. We further performed dynamic graph-theoretical analysis to examine the variability of topological metrics.

Results

Four distinct functional connectivity states were clustered via dFC analysis. Compared to patients with ndPD and HCs, patients with dPD showed increased fractional time and mean dwell time in state 2, characterized by default mode network (DMN)-dominated and cognitive executive network (CEN)-disconnected patterns. Besides, compared to HCs, patients with dPD and patients with ndPD both showed weaker dynamic connectivity within the sensorimotor network (SMN) in state 4, a regionally densely connected state. We additionally observed that patients with dPD presented less variability in the local efficiency of the network.

Conclusions

Our study demonstrated that altered network connection over time, mainly involving the DMN and CEN, with abnormal dynamic graph properties, may contribute to the presence of depression in patients with PD.

Altered Degree Centrality of Brain Networks in Parkinson's Disease With Freezing of Gait: A Resting-State Functional MRI Study

Freezing of gait (FOG) in Parkinson's disease (PD) leads to devastating consequences; however, little is known about its functional brain network. We explored the differences in degree centrality (DC) of functional networks among PD with FOG (PD FOG+), PD without FOG (PD FOG–), and healthy control (HC) groups. In all, 24 PD FOG+, 37 PD FOG–, and 22 HCs were recruited and their resting-state functional magnetic imaging images were acquired. The whole brain network was analyzed using graph theory analysis. DC was compared among groups using the two-sample t-test. The DC values of disrupted brain regions were correlated with the FOG Questionnaire (FOGQ) scores. Receiver operating characteristic curve analysis was performed. We found significant differences in DC among groups. Compared with HCs, PD FOG+ patients showed decreased DC in the middle frontal gyrus (MFG), superior temporal gyrus (STG), parahippocampal gyrus (PhG), inferior temporal gyrus (ITG), and middle temporal gyrus (MTG). Compared with HC, PD FOG– presented with decreased DC in the MFG, STG, PhG, and ITG. Compared with PD FOG–, PD FOG+ showed decreased DC in the MFG and ITG. A negative correlation existed between the DC of ITG and FOGQ scores; the DC in ITG could distinguish PD FOG+ from PD FOG– and HC. The calculated AUCs were 81.3, 89.5, and 77.7% for PD FOG+ vs. HC, PD FOG– vs. HC, and PD FOG+ vs. PD FOG–, respectively. In conclusion, decreased DC of ITG in PD FOG+ patients compared to PD FOG– patients and HCs may be a unique feature for PD FOG+ and can likely distinguish PD FOG+ from PD FOG– and HC groups.