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Predicting human inhibitory control from brain structural MRI

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Abstract

The anatomical structure of the human brain varies widely, as does individual cognitive behavior. It is important and interesting to study the relationship between brain structure and cognitive behavior. There has however been little previous work on the relationship between inhibitory control and brain structure. The goal of this study was to elucidate possible cortical markers related to inhibitory control using structural magnetic resonance imaging (sMRI) data. In this study, we analyzed sMRI data and inhibitory control behavior measurement values from 361 healthy adults from the Human Connectome Project (HCP). The data of all participants were divided into two datasets. In the first dataset, we first constructed individual brain morphometric similarity networks by calculating the inter-regional statistical similarity relationship of nine cortical characteristic measures (such as volume) for each brain area obtained from sMRI data. Areas that covary in their morphology are termed ‘connected’. After that, we used a brain connectome-based predictive model (CPM) to search for ‘connected’ brain areas that were significantly related to inhibitory control. This is a purely data-driven method with built-in cross-validation. Two different ‘connected’ patterns were observed for high and low inhibitory control networks. The high inhibitory control network comprised 25 ‘connections’ (edges between nodes), mostly involving nodes in the prefrontal and especially orbitofrontal cortex and inferior frontal gyrus. In the low inhibitory control network, nodes were scattered between parietal, occipital and limbic areas. Furthermore, these ‘connections’ were verified as reliable and generalizable in a cross-validation dataset. Two regions of interest, the right ventromedial prefrontal cortex including a part of medial area 10 (R.OFCmed) and left middle temporal gyrus (L.MTG) were crucial nodes in the two networks, respectively, which suggests that these two regions may be fundamentally involved in inhibitory control. Our findings potentially help to understand the relationship between areas with a correlated cortical structure and inhibitory control, and further help to reveal the brain systems related to inhibition and its disorders.

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Abbreviations

CPM:

connectome-based predictive model

L.MTG:

left middle temporal gyrus

MAPE:

mean absolute percentage error

R.OFCmed:

right medial orbitofrontal

sMRI:

structural magnetic resonance imaging

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Acknowledgements

Use of the Human Connectome Project (https://www.humanconnectome.org/) dataset is acknowledged. Shuixia Guo is supported by the National Natural Science Foundation of China (NSFC) grant (No.11671129). All work of the study was done by the authors. The authors were responsible for the authenticity of the data and related results.

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  1. MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, 410006, People’s Republic of China

    Ningning He, Wei Zhao & Shuixia Guo

  2. Oxford Centre for Computational Neuroscience, Oxford, England

    Edmund T. Rolls

  3. Department of Computer Science, University of Warwick, Coventry, England

    Edmund T. Rolls

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  1. Ningning He
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He, N., Rolls, E.T., Zhao, W. et al. Predicting human inhibitory control from brain structural MRI. Brain Imaging and Behavior 14, 2148–2158 (2020). https://doi.org/10.1007/s11682-019-00166-9

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