Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes

TY - CHAP

T1 - Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes

AU - Lisowska, Anna

AU - Rekik, Islem

PY - 2018

Y1 - 2018

N2 - In this study, we aim to predict emotional intelligence scores from functional connectivity data acquired at different timepoints. To enhance the generalizability of the proposed predictive model to new data and accurate identification of most relevant neural correlates with different facets of the human intelligence, we propose a joint support vector machine and support vector regression (SVM + SVR) model. Specifically, we first identify most discriminative connections between subjects with high vs low emotional intelligence scores in the SVM step and then perform a multi-variate linear regression using these connections to predict the target emotional intelligence score in the SVR step. Our method outperformed existing methods including the Connectome-based Predictive Model (CPM) using functional connectivity data simultaneously acquired with the intelligence scores. The most predictive connections of intelligence included brain regions involved in processing of emotions and social behaviour.

AB - In this study, we aim to predict emotional intelligence scores from functional connectivity data acquired at different timepoints. To enhance the generalizability of the proposed predictive model to new data and accurate identification of most relevant neural correlates with different facets of the human intelligence, we propose a joint support vector machine and support vector regression (SVM + SVR) model. Specifically, we first identify most discriminative connections between subjects with high vs low emotional intelligence scores in the SVM step and then perform a multi-variate linear regression using these connections to predict the target emotional intelligence score in the SVR step. Our method outperformed existing methods including the Connectome-based Predictive Model (CPM) using functional connectivity data simultaneously acquired with the intelligence scores. The most predictive connections of intelligence included brain regions involved in processing of emotions and social behaviour.

M3 - Chapter (peer-reviewed)

SN - 9783030003197

T3 - Lecture Notes in Computer Science

BT - PRedictive Intelligence in MEdicine

A2 - Rekik, Islem

A2 - Unal, Gozde

A2 - Adeli, Ehsan

A2 - Park, Sang Hyun

PB - Springer International Publishing

T2 - First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018

Y2 - 16 September 2018 through 20 September 2018

ER -