Motor imagery (MI) is similar to overt movement, engaging common neural substrates and facilitating the corticomotor pathway; however, it does not result in excitatory descending motor output. Transcranial magnetic stimulation (TMS) can be used to assess inhibitory networks in the primary motor cortex via measures of 1-ms short-interval intracortical inhibition (SICI), long-interval intracortical inhibition (LICI), and late cortical disinhibition (LCD). These measures are thought to reflect extrasynaptic GABA tonic inhibition, postsynaptic GABA inhibition, and presynaptic GABA disinhibition, respectively. The behavior of 1-ms SICI, LICI, and LCD during MI has not yet been explored. This study aimed to investigate how 1-ms SICI, LICI, and LCD are modulated during MI and voluntary relaxation (VR) of a target muscle. Twenty-five healthy young adults participated. TMS was used to assess nonconditioned motor evoked potential (MEP) amplitude, 1-ms SICI, 100- (LICI) and 150-ms LICI, and LCD in the right abductor pollicis brevis (APB) and right abductor digiti minimi during rest, MI, and VR of the hand. Compared with rest, MEP amplitudes were facilitated in APB during MI. SICI was not affected by task or muscle. LICI decreased in both muscles during VR but not MI, whereas LCD was recruited in both muscles during both tasks. This indicates that VR modulates postsynaptic GABA inhibition, whereas both tasks modulate presynaptic GABA inhibition in a non-muscle-specific way. This study highlights further neurophysiological parallels between actual and imagined movement, which may extend to voluntary relaxation. This is the first study to investigate how 1-ms short-interval intracortical inhibition, long-interval intracortical inhibition, and late cortical disinhibition are modulated during motor imagery and voluntary muscle relaxation. We present novel findings of decreased 100-ms long-interval intracortical inhibition during voluntary muscle relaxation and increased late cortical disinhibition during both motor imagery and voluntary muscle relaxation.
Background Atlas-based voxel features have the potential to aid motor outcome prognostication after stroke, but are seldom used in clinically feasible prediction models. This could be because neuroimaging feature development is a non-standardized, complex, multistep process. This is a barrier to entry for researchers and poses issues for reproducibility and validation in a field of research where sample sizes are typically small. Objectives The primary aim of this review is to describe the methodologies currently used in motor outcome prediction studies using atlas-based voxel neuroimaging features. Another aim is to identify neuroanatomical regions commonly used for motor outcome prediction. Methods A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol was constructed and OVID Medline and Scopus databases were searched for relevant studies. The studies were then screened and details about imaging modality, image acquisition, image normalization, lesion segmentation, region of interest determination, and imaging measures were extracted. Results Seventeen studies were included and examined. Common limitations were a lack of detailed reporting on image acquisition and the specific brain templates used for normalization and a lack of clear reasoning behind the atlas or imaging measure selection. A wide variety of sensorimotor regions relate to motor outcomes and there is no consensus use of one single sensorimotor atlas for motor outcome prediction. Conclusion There is an ongoing need to validate imaging predictors and further improve methodological techniques and reporting standards in neuroimaging feature development for motor outcome prediction post-stroke.
Highlights MRI metrics can classify MEP status with 81% accuracy using support vector machine. Metrics from both T1 and diffusion MRI are important for this classification. Machine learning is a powerful tool for analysing multivariate MRI data.
Baseline scores after stroke have long been known as a good predictor of post-stroke outcomes. Similarly, the extent of baseline impairment has been shown to strongly correlate with spontaneous recovery in the first 3 to 6 months after stroke, a principle known as proportional recovery. However, recent critiques have proposed that proportional recovery is confounded, most notably by mathematical coupling and ceiling effects, and that it may not be a valid model for post-stroke recovery. This article reviews the current understanding of proportional recovery after stroke, discusses its supposed confounds of mathematical coupling and ceiling effects, and comments on the validity and usefulness of proportional recovery as a model for post-stroke recovery. We demonstrate that mathematical coupling of the true measurement value is not a real statistical confound, but rather a notational construct that has no effect on the correlation itself. On the other hand, mathematical coupling does apply to the measurement error and can spuriously amplify correlation effect sizes, but should be negligible in most cases. We also explain that compression toward ceiling and the corresponding proportional recovery relationship are consistent with our understanding of post-stroke recovery dynamics, rather than being unwanted confounds. However, while proportional recovery is valid, it is not particularly groundbreaking or meaningful as previously thought, just like how correlations between baseline scores and outcomes are relatively common in stroke research. Whether through proportional recovery or baseline-outcome regression, baseline scores are a starting point for investigating factors that determine recovery and outcomes after stroke.
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