Multimodal Imaging Biomarker-Based Model Using Stratification Strategies for Predicting Upper Extremity Motor Recovery in Severe Stroke Patients

 And you really think predicting failure to recover is of ANY FUCKING USE AT ALL TO SURVIVORS? Do you have any usable brain cells at all?

 

Multimodal Imaging Biomarker-Based Model Using Stratification Strategies for Predicting Upper Extremity Motor Recovery in Severe Stroke Patients

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Jungsoo Lee, PhD, Heegoo Kim, BS, Jinuk Kim, MS, ...

First Published December 31, 2021 Research Article 

https://doi.org/10.1177/15459683211070278

Article information

Abstract

Background. 

 Various prognostic biomarkers for upper extremity (UE) motor recovery after stroke have been reported. However, most have relatively low predictive accuracy in severe stroke patients.

Objective. 

This study suggests an imaging biomarker-based model for effectively predicting UE recovery in severe stroke patients.

Methods. 

 Of 104 ischemic stroke patients screened, 42 with severe motor impairment were included. All patients underwent structural, diffusion, and functional magnetic resonance imaging at 2 weeks and underwent motor function assessments at 2 weeks and 3 months after stroke onset. According to motor function recovery at 3 months, patients were divided into good and poor subgroups. The value of multimodal imaging biomarkers of lesion load, lesion volume, white matter integrity, and cortical functional connectivity for motor recovery prediction was investigated in each subgroup.

Results. 

Imaging biomarkers varied depending on recovery pattern. The integrity of the cerebellar tract (P = .005, R² = .432) was the primary biomarker in the good recovery group. In contrast, the sensory-related corpus callosum tract (P = .026, R² = .332) and sensory-related functional connectivity (P = .001, R² = .531) were primary biomarkers in the poor recovery group. A prediction model was proposed by applying each biomarker in the subgroup to patients with different motor evoked potential responses (P < .001, R² = .853, root mean square error = 5.28).

Conclusions. 

 Our results suggest an optimized imaging biomarker model for predicting UE motor recovery after stroke. This model can contribute to individualized management of severe stroke in a clinical setting.

Keywords

stroke, recovery, multimodal imaging, prediction

Introduction

Understanding the recovery mechanisms and predicting recovery patterns are important for establishing individually tailored rehabilitation strategies and allowing patients to set realistic goals in clinics.¹ Previous studies have reported diverse prognostic factors related to upper extremity (UE) recovery, such as injury of the corticospinal tract (CST).^1,2 A previous neuroimaging study used CST lesion load, which measures damage to the CST, to predict UE motor recovery after events including severe stroke.³ However, the predictive accuracy of CST lesion load tended to decline when applied in severe stroke patients.³ Another factor with prognostic value for motor recovery is motor evoked potential (MEP). The positive predictive value for MEP status is high, but the negative predictive value is low.¹ Because patients with severe motor impairments have a higher rate of negative MEP response, predicting UE motor recovery in these patients is challenging.

Therefore, there is a need among clinicians and investigators to identify novel neuroimaging biomarkers with better recovery prediction of UE motor function among patients with severe stroke. Multimodal neuroimaging data such as magnetic resonance imaging (MRI) images have been useful for predicting motor recovery of severe stroke patients.¹ Considering that severe stroke patients exhibit a large degree of inter-individual variability in functional recovery,⁴ it is conceivable that different neuroimaging biomarkers contribute to good or poor recovery. Various imaging biomarkers have been reported. In anatomical imaging, the CST lesion load is a representative biomarker, as mentioned above, and lesion volume is a well-known biomarker even though it is not the most predictive.⁵ In diffusion tensor imaging (DTI), the integrity of the CST or partial regions of the CST is a predictive neuroimaging biomarker with the greatest consensus among experts.⁶ Also, the integrity of the cortico-cerebellar tract and that of the corpus callosum are worth noting as predictive biomarkers.^7-9 The cerebellum is connected densely to motor areas and is involved in motor learning and control.^10,11 The corpus callosum is the largest white matter structure that plays an important role in the transfer of motor and sensory information.¹² In resting-state functional magnetic resonance imaging (rs-fMRI), interhemispheric connectivity is related to prediction of motor outcomes.^13-16 Disruption of interhemispheric connectivity is the most noticeable characteristic and a good indicator of bihemispheric imbalance after stroke.^14,17 We hypothesized that there are distinct neuroimaging biomarkers predicting good and poor recovery. We examined neuroimaging biomarkers in patients who showed good or poor recovery, and these biomarkers were applied to patients with a positive or negative response to MEP.^18,19 Then, we examined whether certain neuroimaging biomarkers could improve the predictive accuracy of UE motor recovery compared to previously established neuroimaging biomarkers and MEP response alone.

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