A Multi-Model Framework to Explore ADHD Diagnosis from Neuroimaging Data

Yavuz Ozdemir, Yagmur; Nukala, Naga Chandra Padmini; Molinari, Roberto; Deshpande, Gopikrishna

doi:10.6339/24-JDS1128

Journal of Data Science

A Multi-Model Framework to Explore ADHD Diagnosis from Neuroimaging Data

Volume 22, Issue 2 (2024): Special Issue: 2023 Symposium on Data Science and Statistics (SDSS): “Inquire, Investigate, Implement, Innovate”, pp. 191–207

Yagmur Yavuz Ozdemir Naga Chandra Padmini Nukala Roberto Molinari All authors (4)

https://doi.org/10.6339/24-JDS1128

Pub. online: 2 May 2024 Type: Data Science In Action

Open Access

Received
31 July 2023

Accepted
3 April 2024

Published
2 May 2024

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a frequent neurodevelopmental disorder in children that is commonly diagnosed subjectively. The objective detection of ADHD based on neuroimaging data has been a complex problem with low ranges of accuracy, possibly due to (among others) complex diagnostic processes, the high number of features considered and imperfect measurements in data collection. Hence, reliable neuroimaging biomarkers for detecting ADHD have been elusive. To address this problem we consider a recently proposed multi-model selection method called Sparse Wrapper AlGorithm (SWAG), which is a greedy algorithm that combines screening and wrapper approaches to create a set of low-dimensional models with good predictive power. While preserving the previous levels of accuracy, SWAG provides a measure of importance of brain regions for identifying ADHD. Our approach also provides a set of equally-performing and simple models which highlight the main feature combinations to be analyzed and the interactions between them. Taking advantage of the network of models resulting from this approach, we confirm the relevance of the frontal and temporal lobes as well as highlight how the different regions interact to detect the presence of ADHD. In particular, these results are fairly consistent across different learning mechanisms employed within the SWAG (i.e. logistic regression, linear and radial-kernel support vector machines) thereby providing population-level insights, as well as delivering feature combinations that are smaller and often perform better than those that would be used if employing their original versions directly.

Supplementary material

Supplementary Material

All of our code is open source in the following GitHub repository https://github.com/yagmuryavuzozdemir/SDSS_SWAG_ADHD. One can find the necessary codes and the datasets used in the analysis of our work in this folder.

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2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

automated detection functional magnetic resonance imaging interpretability prediction accuracy SWAG

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