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MINI REVIEW article

Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1497881

A short investigation of the effect of the selection of Human's Brain Atlases on the Performance of ASD's Classification Models

Provisionally accepted
Naseer Ahmed Khan Naseer Ahmed Khan *XUEQUN SHANG XUEQUN SHANG
  • Northwestern Polytechnical University, Xi'an, China

The final, formatted version of the article will be published soon.

    This study investigates the impact of brain atlas selection on the classification accuracy of Autism Spectrum Disorder (ASD) models using functional Magnetic Resonance Imaging (fMRI) data. Brain atlases, such as AAL, CC200, Harvard-Oxford, and Yeo 7/17, are used to define regions of interest (ROIs) for fMRI analysis and have crucial role enabling researchers to study connectivity patterns and neural dynamics in ASD. Through a systematic review, we examine the performance of different atlases in various machine learning and deep learning frameworks for ASD classification. The results reveal that atlas selection significantly affects classification accuracy, with denser atlases like CC400 providing higher granularity, while coarser atlases like AAL offer computational efficiency. Furthermore, we discuss the dynamics of combining multiple atlases to enhance feature extraction and explore the implications of atlas selection across diverse datasets. Our findings emphasize the need for standardized approaches to atlas selection and highlight future research directions, including the integration of novel atlases, advanced data augmentation techniques, and end-to-end deep learning models. This study provides valuable insights into optimizing fMRI-based ASD diagnosis and underscores the importance of interpreting atlas-specific features for improved understanding of brain connectivity in ASD.

    Keywords: ASD, Atlas, fMRI, RS-fMRI, deep learning, pre-processing, Classification

    Received: 19 Sep 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Khan and SHANG. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Naseer Ahmed Khan, Northwestern Polytechnical University, Xi'an, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.