Atlases of cognition with large-scale human brain mapping

PLoS Comput Biol. 2018 Nov 29;14(11):e1006565. doi: 10.1371/journal.pcbi.1006565. eCollection 2018 Nov.

Abstract

To map the neural substrate of mental function, cognitive neuroimaging relies on controlled psychological manipulations that engage brain systems associated with specific cognitive processes. In order to build comprehensive atlases of cognitive function in the brain, it must assemble maps for many different cognitive processes, which often evoke overlapping patterns of activation. Such data aggregation faces contrasting goals: on the one hand finding correspondences across vastly different cognitive experiments, while on the other hand precisely describing the function of any given brain region. Here we introduce a new analysis framework that tackles these difficulties and thereby enables the generation of brain atlases for cognitive function. The approach leverages ontologies of cognitive concepts and multi-label brain decoding to map the neural substrate of these concepts. We demonstrate the approach by building an atlas of functional brain organization based on 30 diverse functional neuroimaging studies, totaling 196 different experimental conditions. Unlike conventional brain mapping, this functional atlas supports robust reverse inference: predicting the mental processes from brain activity in the regions delineated by the atlas. To establish that this reverse inference is indeed governed by the corresponding concepts, and not idiosyncrasies of experimental designs, we show that it can accurately decode the cognitive concepts recruited in new tasks. These results demonstrate that aggregating independent task-fMRI studies can provide a more precise global atlas of selective associations between brain and cognition.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Area Under Curve
  • Bayes Theorem
  • Brain / physiology*
  • Brain Mapping / methods*
  • Cognition / physiology*
  • Databases, Factual
  • Functional Neuroimaging / methods*
  • Hearing
  • Humans
  • Magnetic Resonance Imaging
  • Motor Skills
  • Neuroimaging / methods*
  • ROC Curve
  • Reproducibility of Results

Grants and funding

The authors acknowledge support from the following contracts: NSF OCI-113144 1, ANR ANR-10-JCJC 1408-01, ANR 2017 "DirtyData" ANR 2017 "FastBig", EU H2020 Framework Programme for Research and Innovation, Grant Agreement No 720270 (Human Brain Project SGA1), and 785907 (Human brain project SGA2), the Deutsche Forschungsgemeinschaft (DFG, BZ2/2-1, BZ2/3-1, and BZ2/4-1; International Research Training Group IRTG2150), Amazon AWS Research Grant (2016 and 2017), the German National Merit Foundation, as well as the START-Program of the Faculty of Medicine (126/16) and Exploratory Research Space (OPSF449), RWTH Aachen, ERC-2010-StG_20091209 MindTime, ANR-10-JCJC-1904 BrainTime and the Inria MetaMRI associate team. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.