Dataset Information

Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network.

ABSTRACT:

Significance

Motor function evaluation is essential for poststroke dyskinesia rehabilitation. Neuroimaging techniques combined with machine learning help decode a patient's functional status. However, more research is needed to investigate how individual brain function information predicts the dyskinesia degree of stroke patients.

Aim

We investigated stroke patients' motor network reorganization and proposed a machine learning-based method to predict the patients' motor dysfunction.

Approach

Near-infrared spectroscopy (NIRS) was used to measure hemodynamic signals of the motor cortex in the resting state (RS) from 11 healthy subjects and 31 stroke patients, 15 with mild dyskinesia (Mild), and 16 with moderate-to-severe dyskinesia (MtS). The graph theory was used to analyze the motor network characteristics.

Results

The small-world properties of the motor network were significantly different between groups: (1) clustering coefficient, local efficiency, and transitivity: MtS > Mild > Healthy and (2) global efficiency: MtS < Mild < Healthy. These four properties linearly correlated with patients' Fugl-Meyer Assessment scores. Using the small-world properties as features, we constructed support vector machine (SVM) models that classified the three groups of subjects with an accuracy of 85.7%.

Conclusions

Our results show that NIRS, RS functional connectivity, and SVM together constitute an effective method for assessing the poststroke dyskinesia degree at the individual level.

SUBMITTER: Lin S

PROVIDER: S-EPMC10072005 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network.

Lin Shuoshu S Wang Dan D Sang Haojun H Xiao Hongjun H Yan Kecheng K Wang Dongyang D Zhang Yizheng Y Yi Li L Shao Guangjian G Shao Zhiyong Z Yang Aoran A Zhang Lei L Sun Jinyan J

Neurophotonics 20230404 2

<h4>Significance</h4>Motor function evaluation is essential for poststroke dyskinesia rehabilitation. Neuroimaging techniques combined with machine learning help decode a patient's functional status. However, more research is needed to investigate how individual brain function information predicts the dyskinesia degree of stroke patients.<h4>Aim</h4>We investigated stroke patients' motor network reorganization and proposed a machine learning-based method to predict the patients' motor dysfunctio ...[more]

PMID: 37025568

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Project description:While a tumour in or abutting primary motor cortex leads to motor weakness, how tumours elsewhere in the frontal or parietal lobes affect functional connectivity in a weak patient is less clear. We hypothesized that diminished functional connectivity in a distributed network of motor centres would correlate with motor weakness in subjects with brain masses. Furthermore, we hypothesized that interhemispheric connections would be most vulnerable to subtle disruptions in functional connectivity. We used task-free functional magnetic resonance imaging connectivity to probe motor networks in control subjects and patients with brain tumours (n = 22). Using a control dataset, we developed a method for automated detection of key nodes in the motor network, including the primary motor cortex, supplementary motor area, premotor area and superior parietal lobule, based on the anatomic location of the hand-motor knob in the primary motor cortex. We then calculated functional connectivity between motor network nodes in control subjects, as well as patients with and without brain masses. We used this information to construct weighted, undirected graphs, which were then compared to variables of interest, including performance on a motor task, the grooved pegboard. Strong connectivity was observed within the identified motor networks between all nodes bilaterally, and especially between the primary motor cortex and supplementary motor area. Reduced connectivity was observed in subjects with motor weakness versus subjects with normal strength (P < 0.001). This difference was driven mostly by decreases in interhemispheric connectivity between the primary motor cortices (P < 0.05) and between the left primary motor cortex and the right premotor area (P < 0.05), as well as other premotor area connections. In the subjects without motor weakness, however, performance on the grooved pegboard did not relate to interhemispheric connectivity, but rather was inversely correlated with connectivity between the left premotor area and left supplementary motor area, for both the left and the right hands (P < 0.01). Finally, two subjects who experienced severe weakness following surgery for their brain tumours were followed longitudinally, and the subject who recovered showed reconstitution of her motor network at follow-up. The subject who was persistently weak did not reconstitute his motor network. Motor weakness in subjects with brain tumours that do not involve primary motor structures is associated with decreased connectivity within motor functional networks, particularly interhemispheric connections. Motor networks become weaker as the subjects become weaker, and may become strong again during motor recovery.

Project description:BackgroundThe thalamus plays a pivotal role in functional brain networks, yet its contribution to motor function recovery following stroke remains elusive. We aim to explore changes in thalamocortical functional connectivity poststroke and its correlation with motor function.MethodsThirty-nine subacute ischemic stroke patients and 32 healthy individuals underwent resting-state functional magnetic resonance imaging (MRI). The Fugl-Meyer Assessment (FMA) was employed to evaluate upper and lower extremity motor function before and 1 year after stroke rehabilitation. The ipsilesional thalamus and contralesional thalamus were parceled into functional regions of interest (ROIs) based on connectivity with six cortical ROIs: prefrontal, motor, temporal, posterior parietal, somatosensory, and occipital cortex. Functional connectivity between each cortical ROI and its corresponding thalamic ROI was calculated and compared between groups. Differences identified in the ROI-to-ROI analysis were further investigated through seed-to-voxel whole-brain connectivity analyses to pinpoint thalamic dysconnectivity. Correlations with upper and lower extremity motor function were also analyzed.ResultsSignificant changes in thalamocortical functional connectivity were observed after stroke in ROI-to-ROI analysis, with bilateral somatosensory-thalamic connectivity decreased and ipsilesional temporal-thalamic and bilateral occipital-thalamic connectivity increased. Seed-to-voxel analysis localized ipsilesional thalamic hypoconnectivity to the ipsilesional rolandic operculum and ipsilesional precentral gyrus. Ipsilesional somatosensory-thalamic connectivity was positively correlated with baseline upper extremity FMA scores and negatively correlated with upper extremity motor function change rate at 1-year postdischarge.ConclusionsThis study provides new insights into the role of the thalamus in motor function recovery after stroke, offering preliminary evidence for its potential as a therapeutic target in poststroke rehabilitation.

Dataset Information

Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network.

Significance

Aim

Approach

Results

Conclusions

Publications

Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network.

Similar Datasets

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