Imaging structural co-variance between human brain regions

By Aaron F. Alexander-Bloch, Jay N. Giedd, E. Bullmore. Nature Reviews Neuroscience, 2013

1,025 citations recorded by the source corpus.

Brain structure varies between people in a markedly organized fashion. Communities of brain regions co-vary in their morphological properties. For example, cortical thickness in one region influences the thickness of structurally and functionally connected regions. Such networks of structural co-variance partially recapitulate the functional networks of healthy individuals and the foci of grey matter loss in neurodegenerative disease. This architecture is genetically heritable, is associated with behavioural and cognitive abilities and is changed systematically across the lifespan. The biological meaning of this structural co-variance remains controversial, but it appears to reflect developmental coordination or synchronized maturation between areas of the brain. This Review discusses the state of current research into brain structural co-variance, its underlying mechanisms and its potential value in the understanding of various neurological and psychiatric conditions.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b94a128ebfbee21d7825296496a9f4e95c042935

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Citations in the corpus

Small-World Brain Networks Revisited (2016, background): “…of connectivity from traditional tract-tracing experiments [67], and much less ambiguously related to the cellular substrates of brain networks than the statistical measures of functional connectivity [48, 68] and structural covariance [69, 70] used to build graphs from human neuroimaging data.”
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture (2016, neutral): “Although its biological significance remains controversial, it is hence assumed that functional connectivity should at least contribute to the patterns of structural covariance (Mechelli et al. 2005; Alexander-Bloch et al. 2013).”
Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation (2017, positive): “Then the covariance (usually, in fact, the correlation) between regional estimates of cortical thickness is estimated for each possible pair of regions, resulting in a single SCN for the whole group (Alexander-Bloch et al., 2013a).”