Jul 2026· Brain : a journal of neurology· 0 citations
Medicine
TL;DR
The findings disentangle neurophysiological substrates of PD psychiatry, identifying symptom-specific biomarkers and informing targeted neuromodulation strategies.
Abstract
Psychiatric symptoms in Parkinson's disease (PD) are highly prevalent and challenging to treat. This study maps oscillatory neural activity to diverse psychiatric symptoms in PD, using resting-state subthalamic nucleus (STN) local field potentials (LFPs) and frontal EEG in 55 PD patients undergoing deep brain stimulation (DBS). We tested whether 1) distinct psychiatric symptoms are associated with frequency-specific neural signatures using power spectral analyses and machine learning, across both eyes-open and eyes-closed sensory-attentional states. 2) symptom encoding is spatially segregated within the STN, with electrophysiological (defined by peak spectral power) and anatomical (defined by STN boundaries) mappings providing complementary information. 3) these regions exhibit distinct structural connectivity profiles, assessed using STN-seeded tractography from the UK Biobank normative connectome. Our analysis revealed spectral, spatial, and connectivity segregation. Depression was associated with increased alpha power, primarily detected by anatomical mapping, whereas apathy (increased high beta) and trait impulsivity (reduced low gamma) were detected with both anatomical and electrophysiological STN mapping. UK Biobank analyses further showed that STN-based alpha clusters (depression-related) preferentially connected with prefrontal, orbitofrontal, and cingulate cortices, while peak low-beta clusters (motor-related) connected with SMA and premotor areas. High-beta and low-gamma bands showed convergent connectivity across peak and STN-based clusters despite ventral-dorsal differences. These findings disentangle neurophysiological substrates of PD psychiatry, identifying symptom-specific biomarkers and informing targeted neuromodulation strategies.
ABSTRACT Background Dynamic analysis of resting‐state fMRI (rs‐fMRI) offers a novel approach to differentiate Parkinson's disease (PD) from progressive supranuclear palsy (PSP) by capturing temporal features of brain network activity, which may shed light on the mechanisms underlying non‐motor symptoms. Objectives To characterize differences in brain dynamics between PD and PSP using dynamic brain metrics, evaluate their exploratory discriminative performance, and investigate associations with non‐motor symptoms. Methods Sixty‐nine healthy controls, 82 PD patients, and 29 PSP patients underwent standardized clinical assessment and rs‐fMRI. Hidden Markov models extracted temporal features including fraction occurrence (FO), dwell time, and transition probability. These metrics were used for group comparisons, correlation analyses, and machine learning classification. Results PD and PSP showed opposite trends in fraction occurrence of unimodal network‐dominant states. Compared to PD and controls, PSP exhibited prolonged duration in the dorsal attention and limbic network (DAN&LIM) state and increased occurrence in the dorsal attention and frontoparietal control network (DAN&FPCN) state. Reduced unimodal state occupancy correlated with cognitive decline in both groups, while increased DAN&LIM and unimodal persistence linked to worse mood and sleep disturbances in PSP. Machine learning with these metrics achieved moderate accuracy in differentiating PD from PSP. Conclusions Temporal features of brain network dynamics may provide candidate imaging markers for distinguishing PD and PSP while offering mechanistic insights into non‐motor symptomatology. However, their diagnostic applicability requires validation in independent external multicenter cohorts.
Chen-Fei Ye, Jia-Hui Shi, Ji-Liang Fang et al.· CNS Neuroscience & Therapeut...· 0 citations
The neurobiological mechanism underpinning cognitive impairment in Parkinson’s disease remains poorly understood, though it increasingly points to aberrant spatiotemporal switching among large-scale network configurations. Leveraging the high temporal resolution of source-reconstructed EEG, we characterized these microstate alterations and their direct coupling with cognitive performance.
This study employed an enhanced Leading eigenvector dynamics analysis framework on source-reconstructed EEG data to probe the temporal properties and transition dynamics of brain functional states in Parkinson’s disease patients with and without mild cognitive impairment.
EEG signals were projected into source space via individual MRI-constrained source imaging, producing time series from 400 cortical regions. Recurring brain states were identified based on instantaneous phase coherence. We computed each state occupancy rate, state duration, and transition probabilities in both Parkinson’s disease patients with and without mild cognitive impairment groups. Correlations with Montreal cognitive assessment scores were assessed.
By identifying four recurrent brain states (state A-D), Parkinson’s disease patients with mild cognitive impairment exhibited increased state occupancy rate and state duration in state B and decreased engagement in state D. In addition, transitions into state D from other states were significantly diminished in the mild cognitive impairment group. Crucially, both the temporal expression and kinetic accessibility of state D were positively correlated with Montreal cognitive assessment scores, suggesting that this integrated configuration underpins high-order cognitive function and may serve as a neural marker of cognitive resilience in Parkinson’s disease.
Cognitive deterioration in Parkinson’s disease is fundamentally associated to disrupted brain state dynamics, marked by a pathological hyper-convergence toward state B and a systemic attrition of state D. These alterations likely reflect impaired attentional control and network rigidity, and may serve as dynamic biomarkers and therapeutic targets for Parkinson’s disease-related cognitive decline.
Yu-Xin Wang, Zhen Zhang, Jiang Wang et al.· Brain Communications· 0 citations
AIM
Somatic symptom disorder (SSD) is a functional disorder characterized by dysregulated neural processing of bodily signals. However, the neural mechanisms underlying recovery versus chronicity in SSD remain unclear. We investigated hierarchical neurophysiological features associated with symptom persistence and improvement using longitudinal source-localized electroencephalography (EEG).
METHODS
Resting-state EEG was analyzed in 83 patients with SSD and 80 matched healthy controls, with 76 participants completing a 6-month follow-up. We examined longitudinal changes in source-localized spectral power and network efficiency in relation to somatic and affective symptom trajectories.
RESULTS
Patients showed a significant reduction in somatic symptoms (P < 0.001) over time. SSD featured a persistent deficit in Gamma-band global efficiency (P = 0.035) that remained lower than healthy levels (P = 0.972 for interaction), representing residual neurobiological vulnerability. Conversely, thalamic spectral power exhibited significant group × time interactions (P < 0.05), reflecting longitudinal subcortical changes. Symptomatic relief was specifically associated with nodal efficiency reorganization within the left central sensorimotor region (Beta band, r = -0.54, P < 0.001). These associations remained robust after controlling for depression and anxiety, with mediation analyses indicating that these associations were largely independent of affective fluctuations.
CONCLUSIONS
SSD involves a multilayered neurobiological architecture, where persistent network-level limitations coexist with longitudinal subcortical and cortical changes. While broader functional deficits endure, clinical recovery is accompanied by longitudinal thalamic changes and is more directly associated with localized sensorimotor reorganization. These findings suggest that addressing both persistent network differences and longitudinal recovery-related changes is essential for effective SSD management.
Y. Jang, E. Yoon, Arum Hong et al.· Psychiatry and Clinical Neur...· 0 citations
Parkinson’s disease (PD) is associated with severe non-motor symptoms, including mood disorders and sleep disturbances, whose neural mechanisms are linked to dysfunction in large-scale brain networks. Functional connectivity measured by electroencephalography (EEG) can noninvasively map abnormalities at the network level.
This study employed the debiased weighted phase lag index (dwPLI) to mitigate the effects of volumetric conductivity and investigated EEG functional connectivity in PD patients (
n
= 14) and healthy controls (
n
= 14) during resting and emotional states. Diagnostic performance was evaluated using L1-regularized logistic regression combined with a strict subject-level leave-one-subject-out cross-validation method.
The accuracy of the beta band model combining the four states reached 82.14% (
p
< 0.05, permutation test), while the accuracy of the gamma band model under the eyes-open state reached 89.29% (
p
< 0.01). Furthermore, theta band connectivity during the sadness state significantly predicted PSQI scores in PD patients.
These findings suggest that emotion-induced network dynamics may reflect sleep disturbances in PD, and establish EEG functional connectivity as a candidate neurophysiological biomarker for PD, with potential applications in both diagnostic classification and the assessment of non-motor symptoms, although validation in larger cohorts is still required.
Fan-Zun Meng, Shuo Liu, Xiao-Yu Xiao et al.· Frontiers in Human Neuroscie...· 0 citations
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterised by motor impairments extending to balance and postural regulation. Although EEG abnormalities in oscillatory activity and functional connectivity are well documented in PD, large-scale brain dynamics during tasks directly engaging postural control remain poorly understood. To address this gap, we examined EEG microstate organisation during the BioVRSea virtual-reality postural-control task in early-stage PD patients (n = 30) and matched healthy controls (HC; n = 26). EEG microstates, brief quasi-stable scalp topographies representing global neural states, provide a robust framework for characterising the rapid temporal structure of whole-brain activity. Although task-based microstate approaches exist, applications to PD remain limited and largely confined to resting-state research. Topographical analyses revealed pronounced between-group differences in microstates D and E, whose group-averaged maps in PD diverged markedly from canonical configurations. Because these maps were not topographically equivalent between groups, comparisons of temporal parameters were restricted to the comparable microstates A-C. Across all task phases, PD patients showed increased duration and coverage of microstates A and B. Transition-probability analysis, likewise restricted to A-C, indicated different trajectories across phases in PD and HC. A single significant Group × Phase interaction emerged for A→C: the largest between-group difference occurred during the POST-movement phase, when PD showed a higher probability than HC. For the remaining transitions, no phase-dependent differences emerged within PD. Because patients were assessed ON medication and clinical or behavioural correlates were unavailable, these findings represent candidate task-state EEG microstate alterations requiring validation, rather than established disease-specific markers.
Carmine Gelormini, Lorena Guerrini, Federica Pescaglia et al.· Parkinsonism & Related Disor...· 0 citations
Early identification of mild cognitive impairment in Parkinson’s disease (PD-MCI) is crucial for delaying dementia progression, yet the mechanisms underlying cortical excitability and time-varying network dysconnectivity remain elusive. This study utilized transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) to characterize time-varying directed brain network alterations targeting the right posterior parietal cortex (PPC) in PD-MCI. 20 PD patients were categorized into PD-MCI and cognitively normal (PD-NC) groups using the Montreal Cognitive Assessment (MoCA). Adaptive directed transfer function (ADTF) was applied to assess whole-brain directed time-varying functional connectivity following right PPC stimulation. Machine learning models were then employed to classify PD-MCI using spatiotemporal network features. Compared to PD-NC, the PD-MCI group exhibited significantly reduced cross-regional directed connectivity across the full 1–45 Hz frequency band. Notably, this decoupling was most pronounced in the γ band, with widespread disruptions across multiple time windows. Abnormal connectivity strengths were significantly positively correlated with MoCA cognitive scores. A KNN classification model was constructed based on 5 key spatiotemporal features, and using 5 repetitions of 5-fold cross-validation, ultimately achieving an optimal classification accuracy of 93.33%. These findings reveal that PPC-targeted, full-frequency time-varying network decoupling is a core neuropathological mechanism in PD-MCI. The identified spatiotemporal features hold promise as objective biomarkers for the early identification of PD-MCI, providing a foundation for early diagnosis and targeted neuromodulation.
Qi Zhu, Guangying Pei, Meng-Xuan Hu et al.· Journal of Physics, Conferen...· 0 citations
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