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SYSTEMATIC REVIEW article
Front. Neuroinform.
Volume 18 - 2024 |
doi: 10.3389/fninf.2024.1399931
Artificial Intelligence role in Advancement of Human Brain Connectome Studies
Provisionally accepted
Dorsa Shekouh
1
Helia Sadat Kaboli
2
Mohammadreza Ghaffarzadeh-Esfahani
3
Mohammadmahdi Khayamdar
4
Zeinab Hamedani
5
Saeed Oraee-Yazdani
6
Alireza Zali
6*
Elnaz Amanzadeh
6*- 1 Student Research Committee, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- 2 Alborz University of Medical Sciences, Karaj, Iran
- 3 Isfahan University of Medical Sciences, Isfahan, Iran
- 4 Iran University of Medical Sciences, Tehran, Iran
- 5 Islamic Azad University of Karaj, Karaj, Iran
- 6 Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Neurons are interactive cells that connect via ions to develop electromagnetic fields in the brain. This structure functions directly in the brain. Connectome is the data obtained from neuronal connections. Since neural circuits change in the brain in various diseases, studying connectome sheds light on the clinical changes in special diseases. The ability to explore this data and its relation to the disorders leads us to find new therapeutic methods. Artificial intelligence (AI) is a collection of powerful algorithms used for finding the relationship between input data and the outcome. AI is used for extraction of valuable features from connectome data and in turn uses them for development of prognostic and diagnostic models in neurological diseases. Studying the changes of brain circuits in neurodegenerative diseases and behavioral disorders makes it possible to provide early diagnosis and development of efficient treatment strategies. Considering the difficulties in studying brain diseases, the use of connectome data is one of the beneficial methods for improvement of knowledge of this organ. In the present study, we provide a systematic review on the studies published using connectome data and AI for studying various diseases and we focus on the strength and weaknesses of studies aiming to provide a viewpoint for the future studies. Throughout, AI is very useful for development of diagnostic and prognostic tools using neuroimaging data, while bias in data collection and decay in addition to using small datasets restricts applications of AI-based tools using connectome data which should be covered in the future studies.
Keywords: connectome, artificial intelligence, machine learning, Neurological Diseases, deep learning, Neuroimaging
Received: 12 Mar 2024; Accepted: 05 Sep 2024.
Copyright: © 2024 Shekouh, Sadat Kaboli, Ghaffarzadeh-Esfahani, Khayamdar, Hamedani, Oraee-Yazdani, Zali and Amanzadeh. 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:
Alireza Zali, Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Elnaz Amanzadeh, Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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