Abstract
Accumulating evidence shows that brain functional deficits may be impacted by damage to remote brain regions. Recent advances in neuroimaging suggest that stroke impairment can be better predicted based on disruption to brain networks rather than from lesion locations or volumes only. Our aim was to explore the feasibility of predicting post-stroke somatosensory function from brain functional connectivity through the application of machine learning techniques. Somatosensory impairment was measured using the Tactile Discrimination Test. Functional connectivity was employed to model the global brain function. Behavioral measures and MRI were collected at the same timepoint. Two machine learning models (linear regression and support vector regression) were chosen to predict somatosensory impairment from disrupted networks. Along with two feature pools (i.e., low-order and high-order functional connectivity, or low-order functional connectivity only) engineered, four predictive models were built and evaluated in the present study. Forty-three chronic stroke survivors participated this study. Results showed that the regression model employing both low-order and high-order functional connectivity can predict outcomes based on correlation coefficient of r = 0.54 (p = 0.0002). A machine learning predictive approach, involving high- and low-order modelling, is feasible for the prediction of residual somatosensory function in stroke patients using functional brain networks.
Highlights
Stroke is the second largest cause of death and disability, with a lifetime risk of 1 in 4 [1]
We aim to investigate whether the residual somatosensory function of stroke survivors, estimated by TDT scores, can be predicted from resting-state functional connectivity using multivariate predictive modelling techniques
We investigated the feasibility of applying a machine learning approach to predict somatosensory impairment after stroke using resting-state funcIn this preliminary study, we investigated the feasibility of applying a machine tional connectivity data
Summary
Stroke is the second largest cause of death and disability, with a lifetime risk of 1 in 4 [1]. A good recovery of brain function, and motor and somatosensory function in particular, is crucial for regaining independence and quality of life for most people who have experienced a stroke [2,3,4]. Neurological impairment following stroke is caused by damage to brain regions. Following this perspective, previous studies have mainly focused on the mapping of symptoms to a focal lesion. Accumulating evidence has demonstrated that brain functional deficits can extend to remote connected brain areas [6,7]. This is consistent with recent evidence from us that structural connectivity remote from lesions correlates with somatosensory outcome post-stroke [8]
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