Aberrant cortical reorganization and astrogliosis underlie memory deficit post-traumatic brain injury: a longitudinal multiscale multimodal study

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Abstract
THE BIGGER PICTURE: We now have imaging and analytical tools to study the brain at multiple scales, from scanning the whole brain to see how different neuronal networks interact to scrutinizing individual cells. However, multimodal and multiscale investigations are challenging to conduct in the same subject and generate copious amounts of complex data. Such experimental designs are invaluable in studying heterogeneous neurological disease with pathology at both cellular and network level. Using traumatic brain injury as a test case, we conducted macroscale MRI imaging as well as 3D whole-brain light-sheet fluorescence microscopy with cellular resolution to study network dysfunction and markers of cognitive deficits post-injury. We identified sensitive neurophysiological markers that distinguish healthy and injured mice, and predicted their memory capabilities. The framework we present is broadly applicable to understanding complex cognitive functions and identifying the physiological correlates of distributed network dysfunction in neurological disorders. HIGHLIGHTS: ● Multiscale framework integration of in vivo MRI and ex vivo whole-brain histology ● Connectomic gradients reveal network dedifferentiation linked to memory deficit ● 3D whole-brain segmentation show neuronal hyperactivity and widespread astrogliosis ● AI-based axon tracing reveals persistent damage despite normal neuroimaging scans SUMMARY: Integrating high-dimensional neuroimaging data across spatial scales remains a fundamental bottleneck in quantitative neuroscience. Here, we apply emerging analytical techniques including functional connectome gradient mapping and 3D segmentation of whole-brain cellular resolution microscopy data in a mouse model of traumatic brain injury. At the macroscale, longitudinal functional MRI revealed a transition from frontal hyperconnectivity to hippocampal hypoconnectivity and unimodal-transmodal gradient contraction indicating network dedifferentiation. At the cellular scale, deep-learning based segmentation of whole-brain light-sheet fluorescence microscopy volumes quantified regional astrogliosis, neuronal hyperactivity, and single-axon loss undetected by standard diffusion MRI. Multiscale multimodal data integration not only identified quantitative biomarkers predictive of memory performance post-injury but also enabled validation of in vivo modalities against more sensitive ex vivo histology. This study demonstrates a framework for probing complex multi-network cognitive functions and neurological disorders of network dysfunction.
Funding Information
  • Canadian Institute of Health Research (PJT178059)
  • Canadian Institute of Health Research (PJT376309)
  • Canadian Institute of Health Research (PJT156179)
  • National Institutes of Health (R01AG084681-01)
  • Natural Sciences and Engineering Research Council (Discovery grant RGPIN-2021-03728)
  • Weston Brain Institute

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