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Structural covariance networks in the mouse brain.

Marco Pagani1, Angelo Bifone2, Alessandro Gozzi2

  • 1Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Trento 38068, Italy; Center for Mind and Brain Sciences, University of Trento, Rovereto, Trento 38068, Italy.

Neuroimage
|January 24, 2016
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Summary

Researchers found structural covariance MRI (scMRI) networks in the mouse brain, similar to humans. This discovery supports using mice to study brain development and neurological disorders.

Keywords:
ConnectivityConnectomeMouse brainStructural covarianceVBMscMRI

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Area of Science:

  • Neuroscience
  • Comparative Anatomy
  • Neuroimaging

Background:

  • Structural covariance MRI (scMRI) reveals correlations in regional gray matter volume across subjects.
  • scMRI offers insights into trophic and plastic processes in brain health and disease.
  • Investigating scMRI in mice can advance understanding of brain development and pathology.

Purpose of the Study:

  • To determine if structural covariance MRI networks exist in the laboratory mouse brain.
  • To map these networks using high-resolution morphoanatomical MRI.
  • To establish the mouse as a model for studying scMRI network development and disorders.

Main Methods:

  • Employed high-resolution morphoanatomical MRI in a large cohort of wild-type mice (C57Bl6/J).
  • Utilized a seed-based approach and independent component analyses to map scMRI networks.
  • Applied hierarchical cluster analysis to identify neuroanatomical systems within the networks.

Main Results:

  • Identified robust homotopic scMRI networks in mouse primary and associative cortices.
  • Observed highly symmetric inter-hemispheric correlations in subcortical structures.
  • Detected distributed antero-posterior networks in the thalamus and hypothalamus, forming six distinct clusters.

Conclusions:

  • The mouse brain exhibits homotopic cortical and subcortical scMRI networks.
  • This validates the use of mice for investigating scMRI network underpinnings and neuropathological states.
  • Findings align with the role of environmental factors in shaping brain correlational networks.