Coherent, time-shifted patterns of microstructural plasticity during motor-skill learning

Azzarito, Michela; Emmenegger, Tim; Ziegler, Gabriel; Huber, Eveline; Grabher, Patrick; Callaghan, Martina F.; Thompson, Alan; Friston, Karl J.; Weiskopf, Nikolaus; Killeen, Tim; Freund, Patrick

doi:10.1016/j.neuroimage.2023.120128

Cited by 3 publications

(2 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multivariate frameworks have the potential to counteract issues arising from the physiologically unspecific nature of commonly used neuroimaging metrics and to capture the complexity and heterogeneity of biological properties (Dean et al, 2017; Guberman et al, 2022; Seidlitz et al, 2018; Tardif et al, 2016; Taylor et al, 2020). Multiple mechanisms give rise to brain structure (e.g., myelination, cell proliferation), support neuroplastic change (e.g., Azzarito et al, 2023; Taubert et al, 2012) and behavioral performance (e.g., Seidlitz et al, 2018; Thiebaut de Schotten & Forkel, 2022), and are involved in neurological disorders (e.g., Iturria-Medina et al, 2017). Interpreting the results of univariate statistical analyses is thus challenging within this context.…”

Section: Introductionmentioning

confidence: 99%

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

Tremblay,

Alasmar,

Pirhadi

et al. 2024

Preprint

View full text Add to dashboard Cite

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group-or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

show abstract

Section: Introductionmentioning

confidence: 99%

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

Tremblay,

Alasmar,

Pirhadi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…[3][4][5] Multivariate frameworks have the potential to counteract issues arising from the physiologically unspecific nature of commonly used neuroimaging measures and to capture the complexity and heterogeneity of biological properties. 3,[6][7][8][9] Multiple mechanisms give rise to brain structure (e.g., myelination, cell proliferation), support neuroplastic change 10,11 and behavioral performance, 8,12 and are involved in neurological disorders. 13 Interpreting the results of univariate statistical analyses is thus challenging within this context.…”

Section: Introductionmentioning

confidence: 99%

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across measures

Tremblay,

Alasmar,

Pirhadi

et al. 2024

Aperture Neuro

View full text Add to dashboard Cite

Introduction Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging measures and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between measures. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox (https://github.com/neuralabc/mvcomp (https://github.com/neuralabc/mvcomp)). The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI measures or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. Methods & Results The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI measure (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Discussion Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

show abstract

Adaptation in the spinal cord after stroke: Implications for restoring cortical control over the final common pathway

Urbin

2024

The Journal of Physiology

View full text Add to dashboard Cite

Control of voluntary movement is predicated on integration between circuits in the brain and spinal cord. Although damage is often restricted to supraspinal or spinal circuits in cases of neurological injury, both spinal motor neurons and axons linking these cells to the cortical origins of descending motor commands begin showing changes soon after the brain is injured by stroke. The concept of ‘transneuronal degeneration’ is not new and has been documented in histological, imaging and electrophysiological studies dating back over a century. Taken together, evidence from these studies agrees more with a system attempting to survive rather than one passively surrendering to degeneration. There tends to be at least some preservation of fibres at the brainstem origin and along the spinal course of the descending white matter tracts, even in severe cases. Myelin‐associated proteins are observed in the spinal cord years after stroke onset. Spinal motor neurons remain morphometrically unaltered. Skeletal muscle fibres once innervated by neurons that lose their source of trophic input receive collaterals from adjacent neurons, causing spinal motor units to consolidate and increase in size. Although some level of excitability within the distributed brain network mediating voluntary movement is needed to facilitate recovery, minimal structural connectivity between cortical and spinal motor neurons can support meaningful distal limb function. Restoring access to the final common pathway via the descending input that remains in the spinal cord therefore represents a viable target for directed plasticity, particularly in light of recent advances in rehabilitation medicine. image

show abstract

Coherent, time-shifted patterns of microstructural plasticity during motor-skill learning

Cited by 3 publications

References 99 publications

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across measures

Adaptation in the spinal cord after stroke: Implications for restoring cortical control over the final common pathway

Contact Info

Product

Resources

About