Objective. Maintaining upright posture is a complex task governed by the integration of afferent sensorimotor and visual information with compensatory neuromuscular reactions. The objective of the present work was to characterize the visual dependency and functional dynamics of cortical activation during postural control.Approach. Proprioceptic vibratory stimulation of calf muscles at 85 Hz was performed to evoke postural perturbation in open-eye (OE) and closed-eye (CE) experimental trials, with pseudorandom binary stimulation phases divided into four segments of 16 stimuli. 64-channel EEG was recorded at 512 Hz, with perturbation epochs defined using bipolar electrodes placed proximal to each vibrator. Power spectra variation and linearity analysis was performed via fast Fourier transformation into six frequency bands (Δ, θ, α, β, γ_low, and γ_high,. Finally, functional connectivity assessment was explored via network segregation and integration analyses.Main Results. Spectra variation showed waveform and vision-dependent activation within cortical regions specific to both postural adaptation and habituation. Generalized spectral variation yielded significant shifts from low to high frequencies in CE adaptation trials, with overall activity suppressed in habituation; OE trials showed the opposite phenomenon, with both adaptation and habituation yielding increases in spectral power. Finally, our analysis of functional dynamics reveals novel cortical networks implicated in postural control using EEG source-space brain networks. In particular, our reported significant increase in local θ connectivity may signify the planning of corrective steps and/or the analysis of falling consequences, while α band network integration results reflect an inhibition of error detection within the cingulate cortex, likely due to habituation.Significance. Our findings principally suggest that specific cortical waveforms are dependent upon the availability of visual feedback, and we furthermore present the first evidence that local and global brain networks undergo characteristic modification during postural control.
Background Tracking longitudinal functional brain dysconnectivity in Parkinson's disease (PD) is a key element to decoding the underlying physiopathology and understanding PD progression. Objectives The objectives of this follow‐up study were to explore, for the first time, the longitudinal changes in the functional brain networks of PD patients over 5 years and to associate them with their cognitive performance and the lateralization of motor symptoms. Methods We used a 5‐year longitudinal cohort of PD patients (n = 35) who completed motor and non‐motor assessments and sequent resting state (RS) high‐density electroencephalography (HD‐EEG) recordings at three timepoints: baseline (BL), 3 years follow‐up (3YFU) and 5 years follow‐up (5YFU). We assessed disruptions in frequency‐dependent functional networks over the course of the disease and explored their relation to clinical symptomatology. Results In contrast with HC (n = 32), PD patients showed a gradual connectivity impairment in α2 (10‐13 Hz) and β (13–30 Hz) frequency bands. The deterioration in the global cognitive assessment was strongly correlated with the disconnected networks. These disconnected networks were also associated with the lateralization of motor symptoms, revealing a dominance of the right hemisphere in terms of impaired connections in the left‐affected PD patients in contrast to dominance of the left hemisphere in the right‐affected PD patients. Conclusions Taken together, our findings suggest that with disease progression, dysconnectivity in the brain networks in PD can reflect the deterioration of global cognitive deficits and the lateralization of motor symptoms. RS HD‐EEG may be an early biomarker of PD motor and non‐motor progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org.
BackgroundParkinson's disease (PD) patients present with a heterogeneous clinical phenotype, including motor, cognitive, sleep, and affective disruptions. However, this heterogeneity is often either ignored or assessed using only clinical assessments.ObjectivesWe aimed to identify different PD sub‐phenotypes in a longitudinal follow‐up analysis and their electrophysiological profile based on resting‐state electroencephalography (RS‐EEG) and to assess their clinical significance over the course of the disease.MethodsUsing electrophysiological features obtained from RS‐EEG recordings and data‐driven methods (similarity network fusion and source‐space spectral analysis), we have performed a clustering analysis to identify disease sub‐phenotypes and we examined whether their different patterns of disruption are predictive of disease outcome.ResultsWe showed that PD patients (n = 44) can be sub‐grouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterized by different levels of disruptions in the somatomotor network (Δ and β band), the frontotemporal network (α2 band) and the default mode network (α1 band), which consistently correlate with clinical profiles and disease courses. These clusters are classified into either moderate (only‐motor) or mild‐to‐severe (diffuse) disease. We showed that EEG features can predict cognitive evolution of PD patients from baseline, when the cognitive clinical scores were overlapped.ConclusionsThe identification of novel PD subtypes based on electrical brain activity signatures may provide a more accurate prognosis in individual patients in clinical practice and help to stratify subgroups in clinical trials. Innovative profiling in PD can also support new therapeutic strategies that are brain‐based and designed to modulate brain activity disruption. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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