A Systematic Assessment of Feature Extraction Methods for Robust Prediction of Neuropsychological Scores from Functional Connectivity Data

Federico Calesella, Alberto Testolin, Michele De Filippo De Grazia, Marco Zorzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationBrain Informatics - 13th International Conference, BI 2020, Proceedings
EditorsMufti Mahmud, Stefano Vassanelli, M. Shamim Kaiser, Ning Zhong
PublisherSpringer Science and Business Media Deutschland GmbH
Pages29-40
Number of pages12
ISBN (Print)9783030592769
DOIs
Publication statusPublished - 2020
Event13th International Conference on Brain Informatics, BI 2020 - Padua, Italy
Duration: Sep 19 2020Sep 19 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12241 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Brain Informatics, BI 2020
CountryItaly
CityPadua
Period9/19/209/19/20

Keywords

  • Dimensionality reduction
  • Feature extraction
  • Functional connectivity
  • Machine learning
  • Predictive modeling
  • Resting state networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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