Abstract
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.
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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.
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Appendix
Appendix
The extracted features are sorted in descending order based on the absolute coefficient value. Regression coefficients and the first 6 features are displayed for each dimensionality reduction method (Fig. 4, 5, 6, 7 and 8).
Regression coefficients for each model. Black stars represent coefficients = 0.
The 6 features associated to the highest regression coefficients in the PCA-based model.
The 6 features associated to the highest regression coefficients in the ICA-based model.
The 6 features associated to the highest regression coefficients in the DL-based model.
The 6 features associated to the highest regression coefficients in the NNMF-based model.
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Calesella, F., Testolin, A., De Filippo De Grazia, M., Zorzi, M. (2020). A Systematic Assessment of Feature Extraction Methods for Robust Prediction of Neuropsychological Scores from Functional Connectivity Data. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_3
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