TY - GEN
T1 - A Systematic Assessment of Feature Extraction Methods for Robust Prediction of Neuropsychological Scores from Functional Connectivity Data
AU - Calesella, Federico
AU - Testolin, Alberto
AU - De Filippo De Grazia, Michele
AU - Zorzi, Marco
N1 - Funding Information:
Acknowledgments. This work was supported by grants from the Italian Ministry of Health (RF-2013-02359306 to MZ, Ricerca Corrente to IRCCS Ospedale San Camillo) and by MIUR (Dipartimenti di Eccellenza DM 11/05/2017 n. 262 to the Department of General Psychology). We are grateful to Prof. Maurizio Corbetta for providing the stroke dataset, which was collected in a study funded by grants R01 HD061117-05 and R01 NS095741.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - Multivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated language deficits based on cross-validated regularized regression. Features extracted by Principal Component Analysis (PCA) were found to be the best predictors, followed by Independent Component Analysis (ICA), Dictionary Learning (DL) and Non-Negative Matrix Factorization. However, ICA and DL led to more parsimonious models. Overall, our findings suggest that the choice of the dimensionality reduction technique should not only be based on prediction/regression accuracy, but also on considerations about model complexity and interpretability.
AB - Multivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated language deficits based on cross-validated regularized regression. Features extracted by Principal Component Analysis (PCA) were found to be the best predictors, followed by Independent Component Analysis (ICA), Dictionary Learning (DL) and Non-Negative Matrix Factorization. However, ICA and DL led to more parsimonious models. Overall, our findings suggest that the choice of the dimensionality reduction technique should not only be based on prediction/regression accuracy, but also on considerations about model complexity and interpretability.
KW - Dimensionality reduction
KW - Feature extraction
KW - Functional connectivity
KW - Machine learning
KW - Predictive modeling
KW - Resting state networks
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U2 - 10.1007/978-3-030-59277-6_3
DO - 10.1007/978-3-030-59277-6_3
M3 - Conference contribution
AN - SCOPUS:85092148550
SN - 9783030592769
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 40
BT - Brain Informatics - 13th International Conference, BI 2020, Proceedings
A2 - Mahmud, Mufti
A2 - Vassanelli, Stefano
A2 - Kaiser, M. Shamim
A2 - Zhong, Ning
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Brain Informatics, BI 2020
Y2 - 19 September 2020 through 19 September 2020
ER -