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PERSPECTIVE article

Front. Neurol.
Sec. Applied Neuroimaging
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1489822
This article is part of the Research Topic Elucidating the Relationships between Pupil Size and Neural and Autonomic Functions View all 5 articles

Recent Trends in Multiple Metrics and Multimodal Analysis for Neural Activity and Pupillometry

Provisionally accepted
Sou Nobukawa Sou Nobukawa 1*Aya Shirama Aya Shirama 2Tetsuya Takahashi Tetsuya Takahashi 3Shigenobu Toda Shigenobu Toda 4
  • 1 Chiba Institute of Technology, Narashino, Japan
  • 2 National Center of Neurology and Psychiatry (Japan), Kodaira, Tokyo, Japan
  • 3 Uozu Shinkei Sanatorium, Uozu, Japan
  • 4 Showa University, Shinagawa, Japan

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

    Recent studies focusing on neural activity captured by neuroimaging modalities have provided various metrics for elucidating the functional networks and dynamics of the entire brain.Functional magnetic resonance imaging (fMRI) can depict spatiotemporal functional neural networks and dynamic characteristics due to its excellent spatial resolution. However, its temporal resolution is limited. Neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG), which have higher temporal resolutions, are utilized for multi-temporal scale and multi-frequency-band analyses. With this advantage, numerous EEG/MEG-bases studies have revealed the frequency-band specific functional networks involving dynamic functional connectivity and multiple temporal-scale time-series patterns of neural activity. In addition to analyzing neural data, the examination of behavioral data can unveil additional aspects of brain activity through unimodal and multimodal data analyses performed using appropriate integration techniques. Among the behavioral data assessments, pupillometry can provide comprehensive spatial-temporal-specific features of neural activity. In this perspective, we summarize the recent progress in the development of metrics for analyzing 1Multiple Metrics/Multimodal Analysis neural data obtained from neuroimaging modalities such as fMRI, EEG, and MEG, as well as behavioral data, with a special focus on pupillometry data. First, we review the typical metrics of neural activity, emphasizing functional connectivity, complexity, dynamic functional connectivity, and dynamic state transitions of whole-brain activity. Second, we examine the metrics related to the time-series data of pupillary diameters and discuss the possibility of multimodal metrics that combine neural and pupillometry data. Finally, we discuss future perspectives on these multiple and multimodal metrics.

    Keywords: Cognitive Function, Complexity, emergence, graph analysis, functional connectivity, Neuroimaging, Multimodal data, Pupillometry

    Received: 02 Sep 2024; Accepted: 13 Nov 2024.

    Copyright: © 2024 Nobukawa, Shirama, Takahashi and Toda. 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: Sou Nobukawa, Chiba Institute of Technology, Narashino, Japan

    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.