%0 Conference Proceedings
%T A Machine Learning Tool for Interpreting Differences in Cognition Using Brain Features
%+ Computer Laboratory [Cambridge]
%+ Department of Clinical Neurosciences [Cambridge]
%+ Athinoula A. Martinos Center for Biomedical Imaging
%+ Università degli Studi di Roma Tor Vergata [Roma] = University of Rome Tor Vergata
%A Azevedo, Tiago
%A Passamonti, Luca
%A Lió, Pietro
%A Toschi, Nicola
%Z Part 10: Machine Learning - Natural Language
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Greece
%Y John MacIntyre
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-559
%P 475-486
%8 2019-05-24
%D 2019
%R 10.1007/978-3-030-19823-7_40
%K Brain
%K Machine learning
%K Data science
%K Interpretability
%K Cognition
%K Morphometry
%K Myelin
%K Tool
%K XGBoost
%K SHAP
%Z Computer Science [cs]Conference papers
%X Predicting variability in cognition traits is an attractive and challenging area of research, where different approaches and datasets have been implemented with mixed results. Some powerful Machine Learning algorithms employed before are difficult to interpret, while other algorithms are easy to interpret but might not be as powerful. To improve understanding of individual cognitive differences in humans, we make use of the most recent developments in Machine Learning in which powerful prediction models can be interpreted with confidence. We used neuroimaging data and a variety of behavioural, cognitive, affective and health measures from 905 people obtained from the Human Connectome Project (HCP). As a main contribution of this paper, we show how one could interpret the neuroanatomical basis of cognition, with recent methods which we believe are not yet fully explored in the field. By reducing neuroimages to a well characterised set of features generated from surface-based morphometry and cortical myelin estimates, we make the interpretation of such models easier as each feature is self-explanatory. The code used in this tool is available in a public repository: https://github.com/tjiagoM/interpreting-cognition-paper-2019.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-02331338/document
%2 https://inria.hal.science/hal-02331338/file/483292_1_En_40_Chapter.pdf
%L hal-02331338
%U https://inria.hal.science/hal-02331338
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-559