Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.05.14.443663

Corpus callosal microstructure predicts bimanual motor performance in chronic stroke survivors

Abstract

AO_SCPLOWBSTRACTC_SCPLOWMicrostructural changes in the corpus callosum are associated with more severe motor impairment in the paretic hand, poor recovery, and general disability. Considering its role in bimanual coordination, we suspected that these microstructural changes across the callosum may also be reflected in the performance of ecologically valid routine bimanual tasks. Thus, the purpose of this study was to determine if callosal microstructure predicts bimanual motor performance in chronic stroke survivors by examining the regions of the corpus callosum connecting both the sensorimotor and non-sensorimotor cortices. We examined the relationship between the fractional anisotropy across the CC and movement times for two self-initiated and self-paced bimanual tasks in 41 chronic stroke survivors. Using publicly available control datasets (n = 52), matched closely for acquisition parameters, we also explored the effect of stroke and age on callosal microstructure. There were two main findings: First, callosal microstructure was significantly associated with bimanual performance in chronic stroke survivors. Notably, a significant relationship was observed not only with the primary sensorimotor regions, but also regions of the premotor/supplementary motor and prefrontal regions. Second, chronic stroke survivors presented with significantly lower mean FA, compared to neurologically intact adults. We conclude that in mild-to-moderate chronic stroke survivors with relatively localized lesions to the motor areas, callosal microstructure can be expected to change in not only the primary sensorimotor region, but also more anteriorly in the secondary motor regions and the genu and is associated with performance on cooperative bimanual tasks. SignificanceA goal of rehabilitation after stroke is to promote the return to pre-stroke levels of upper limb function and use, predominantly characterized by coordinated bimanual activities. In this study, we find that in the chronic phase of stroke, microstructural disorganization within the corpus callosum predicts motor performance on real-world bimanual tasks and lends important insight into the indirect, remote effects of stroke. HO_SCPLOWIGHLIGHTSC_SCPLOWO_LIWe provide initial evidence that corpus callosal microstructure predicts performance on two self-initiated and self-paced bimanual tasks. C_LIO_LIAssociations were strongest for fibers connecting the primary sensorimotor cortices followed by the pre- and supplementary motor, and prefrontal cortices. C_LIO_LISignificant reductions in fractional anisotropy were observed in stroke survivors for all regions of the corpus callosum. C_LI

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Varghese, R., Chang, B., Kim, B., Liew, S.-L., Schweighofer, N., Winstein, C. J.. 2021-05-17. Corpus callosal microstructure predicts bimanual motor performance in chronic stroke survivors. https://doi.org/10.1101/2021.05.14.443663

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗