Functional Connectivity's Degenerate View of Brain Computation

PLoS Comput Biol. 2016 Oct 13;12(10):e1005031. doi: 10.1371/journal.pcbi.1005031. eCollection 2016 Oct.

Abstract

Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled. Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions, within and between participants. We used a unified approach combining multivariate analysis, bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively. Empirical data and simulations from generative models with different dynamical behaviors demonstrated, largely irrespective of FC metrics, that a linear subspace with dimension one or two could explain much of the variability across patterns of FC. On the contrary, the variability across BOLD time-courses could not be reduced to such a small subspace. FC appeared to strongly reflect SC and to be partly governed by a Gaussian process. The main differences between simulated and empirical data related to limitations of DWI-based SC estimation (and SC itself could then be estimated from FC). Above and beyond the limited dynamical range of the BOLD signal itself, measures of FC may offer a degenerate representation of brain interactions, with limited access to the underlying complexity. They feature an invariant common core, reflecting the channel capacity of the network as conditioned by SC, with a limited, though perhaps meaningful residual variability.

MeSH terms

  • Brain / anatomy & histology*
  • Brain / physiology*
  • Cognition / physiology*
  • Computer Simulation
  • Connectome / methods*
  • Female
  • Humans
  • Male
  • Models, Neurological*
  • Models, Statistical*
  • Nerve Net / anatomy & histology
  • Nerve Net / physiology
  • Young Adult

Grant support

AM is supported by Deutsche Forschungsgemeinschaft (DFG) grant SFB 936/Z3. DR is supported by the European Research Council under the European Union’s Seventh Framework Program (FP/2007-2013) / ERC Grant Agreement #616268 “F-TRACT”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.