Dataset Information

Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke.

ABSTRACT: Background:Recovery prediction can assist in the planning for impairment-focused rehabilitation after a stroke. This study investigated a new prediction model based on a lesion network analysis. To predict the potential for recovery, we focused on the next link-step connectivity of the direct neighbors of a lesion. Methods:We hypothesized that this connectivity would contribute to recovery after stroke onset. Each lesion in a patient who had suffered a stroke was transferred to a healthy subject. First link-step connectivity was identified by observing voxels functionally connected to each lesion. Next (second) link-step connectivity of the first link-step connectivity was extracted by calculating statistical dependencies between time courses of regions not directly connected to a lesion and regions identified as first link-step connectivity. Lesion impact on second link-step connectivity was quantified by comparing the lesion network and reference network. Results:The lower the impact of a lesion was on second link-step connectivity in the brain network, the better the improvement in motor function during recovery. A prediction model containing a proposed predictor, initial motor function, age, and lesion volume was established. A multivariate analysis revealed that this model accurately predicted recovery at 3 months poststroke (R? 2?=?0.788; cross-validation, R? 2?=?0.746, RMSE?=?13.15). Conclusion:This model can potentially be used in clinical practice to develop individually tailored rehabilitation programs for patients suffering from motor impairments after stroke.

SUBMITTER: Lee J

PROVIDER: S-EPMC7243376 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke.

Lee Jungsoo J Park Eunhee E Lee Ahee A Chang Won Hyuk WH Kim Dae-Shik DS Kim Yun-Hee YH

Therapeutic advances in neurological disorders 20200521

<h4>Background</h4>Recovery prediction can assist in the planning for impairment-focused rehabilitation after a stroke. This study investigated a new prediction model based on a lesion network analysis. To predict the potential for recovery, we focused on the next link-step connectivity of the direct neighbors of a lesion.<h4>Methods</h4>We hypothesized that this connectivity would contribute to recovery after stroke onset. Each lesion in a patient who had suffered a stroke was transferred to a ...[more]

PMID: 32499835

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Project description:Behavioural deficits in stroke reflect both structural damage at the site of injury, and widespread network dysfunction caused by structural, functional, and metabolic disconnection. Two recent methods allow for the estimation of structural and functional disconnection from clinical structural imaging. This is achieved by embedding a patient's lesion into an atlas of functional and structural connections in healthy subjects, and deriving the ensemble of structural and functional connections that pass through the lesion, thus indirectly estimating its impact on the whole brain connectome. This indirect assessment of network dysfunction is more readily available than direct measures of functional and structural connectivity obtained with functional and diffusion MRI, respectively, and it is in theory applicable to a wide variety of disorders. To validate the clinical relevance of these methods, we quantified the prediction of behavioural deficits in a prospective cohort of 132 first-time stroke patients studied at 2 weeks post-injury (mean age 52.8 years, range 22-77; 63 females; 64 right hemispheres). Specifically, we used multivariate ridge regression to relate deficits in multiple functional domains (left and right visual, left and right motor, language, spatial attention, spatial and verbal memory) with the pattern of lesion and indirect structural or functional disconnection. In a subgroup of patients, we also measured direct alterations of functional connectivity with resting-state functional MRI. Both lesion and indirect structural disconnection maps were predictive of behavioural impairment in all domains (0.16 < R2 < 0.58) except for verbal memory (0.05 < R2 < 0.06). Prediction from indirect functional disconnection was scarce or negligible (0.01 < R2 < 0.18) except for the right visual field deficits (R2 = 0.38), even though multivariate maps were anatomically plausible in all domains. Prediction from direct measures of functional MRI functional connectivity in a subset of patients was clearly superior to indirect functional disconnection. In conclusion, the indirect estimation of structural connectivity damage successfully predicted behavioural deficits post-stroke to a level comparable to lesion information. However, indirect estimation of functional disconnection did not predict behavioural deficits, nor was a substitute for direct functional connectivity measurements, especially for cognitive disorders.

Dataset Information

Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke.

Publications

Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke.

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