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Human brain networks from functional MRI

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If you have a question about this talk, please contact Deborah McSkimming.

Functional MRI has many significant disadvantages as a source of information about nervous systems. It does not directly represent neuronal activity; it has coarse spatial and temporal resolution compared to the range of scales of space and time that brains subtend; it is not measured in SI units; experimental recordings are at least 80% noise; etc. Nonetheless, the patterns of between-regional correlation in slowly oscillating fMRI time series have turned out to be robustly replicable and not trivially explained. Graph theoretical models of human fMRI networks, derived from association matrices of pair-wise functional connectivity estimated for all possible pairs of 300 regional nodes, demonstrate complex topology: small-worldness, hubs, modules, core/periphery, etc. These features are replicable and heritable. The topological and spatial or geometrical organization of fMRI networks is consistent with the theory that their formation is largely determined by the trade-off between a few competitive factors or conservation laws. Hypothetically, an economic trade-off between the biological cost and the topological value of network components could drive the formation of fMRI networks. To test the generality of this and other hypotheses generated by connectomic analysis of “resting state” fMRI data, graph theoretical methods can be used to make comparable measurements in many other neuroscientific datasets. Meta-analysis of large scale libraries (N 1000 primary papers) of fMRI activation studies demonstrated that more expensive topological features (hubs, rich club) were associated with domain-general, “higher-order” cognitive functions; and that high cost / high value network hubs were hotspots for structural brain deficits associated with many different brain disorders (including Alzheimer’s disease and schizophrenia). Many of the complex topological characteristics of large-scale human fMRI networks are qualitatively reproduced at the microscopic scale of functional networks derived from multi-electrode array recordings of growing neuronal cultures in vitro. The economical model of a trade-off between biological cost and topological value has been specifically re-affirmed by analysis of viral tract tracing data (~400 anterograde tracer injection experiments) on the anatomical connectivity of the mouse brain. We conclude that despite the well-known limitations of fMRI, it has emerged as almost uniquely capable of measuring the complex network organization of human brain function in a way that is physically, neurobiologically, cognitively, and clinically meaningful.

This talk is part of the Chaucer Club series.

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