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

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

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 comports 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.

From animal models to human individuality: Integrative approaches to the study of brain plasticity

Plasticity allows organisms to form lasting adaptive changes in neural structures in response to interactions with the environment. It serves both species-general functions and individualized skill acquisition. To better understand human plasticity, we need to strengthen the dialogue between human research and animal models. Therefore, we propose to (1) enhance the interpretability of macroscopic methods used in human research by complementing molecular and fine-structural measures used in animals with such macroscopic methods, preferably applied to the same animals, to create macroscopic metrics common to both examined species; (2) launch dedicated cross-species research programs, using either well-controlled experimental paradigms, such as motor skill acquisition, or more naturalistic environments, where individuals of either species are observed in their habitats; and (3) develop conceptual and computational models linking molecular and fine-structural events to phenomena accessible by macroscopic methods. In concert, these three component strategies can foster new insights into the nature of plastic change.

Temporal dynamics of white and gray matter plasticity during motor skill acquisition: a comparative diffusion tensor imaging and multiparametric mapping analysis

Learning new motor skills relies on neural plasticity within motor and limbic systems. This study uniquely combined diffusion tensor imaging and multiparametric mapping MRI to detail these neuroplasticity processes. We recruited 18 healthy male participants who underwent 960 min of training on a computer-based motion game, while 14 were scanned without training. Diffusion tensor imaging, which quantifies tissue microstructure by measuring the capacity for, and directionality of, water diffusion, revealed mostly linear changes in white matter across the corticospinal-cerebellar-thalamo-hippocampal circuit. These changes related to performance and reflected different responses to upper- and lower-limb training in brain areas with known somatotopic representations. Conversely, quantitative MRI metrics, sensitive to myelination and iron content, demonstrated mostly quadratic changes in gray matter related to performance and reflecting somatotopic representations within the same brain areas. Furthermore, while myelin and iron-sensitive multiparametric mapping MRI was able to describe time lags between different cortical brain systems, diffusion tensor imaging detected time lags within the white matter of the motor systems. These findings suggest that motor skill learning involves distinct phases of white and gray matter plasticity across the sensorimotor network, with the unique combination of diffusion tensor imaging and multiparametric mapping MRI providing complementary insights into the underlying neuroplastic responses.

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

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.