Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, wh...

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Published inFrontiers in neuroscience Vol. 17; p. 1227491
Main Authors Teng, Jing, Mi, Chunlin, Shi, Jian, Li, Na
Format Journal Article
LanguageEnglish
Published Lausanne Frontiers Research Foundation 17.08.2023
Frontiers Media S.A
Subjects
Alzheimer's disease
Artificial intelligence
Autism
Brain diseases
Brain mapping
Brain research
Cognitive ability
Diagnosis
feature selection
Functional magnetic resonance imaging
Health care
Learning algorithms
Machine learning
Magnetic resonance imaging
Medical diagnosis
Mental disorders
Movement disorders
Neurodegenerative diseases
Neuroimaging
Neuroscience
Parkinson's disease
Pathogenesis
Public health
Reviews
Schizophrenia
Time series
Online AccessGet full text
ISSN1662-453X
1662-4548
1662-453X
DOI10.3389/fnins.2023.1227491

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Abstract Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.
AbstractList Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.
Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.
Author Li, Na
Teng, Jing
Mi, Chunlin
Shi, Jian
AuthorAffiliation 3 Department of Radiology, The Third Xiangya Hospital of Central South University , Changsha , China
1 School of Control and Computer Engineering, North China Electric Power University , Beijing , China
2 Department of Hematology and Critical Care Medicine, The Third Xiangya Hospital of Central South University , Changsha , China
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CitedBy_id crossref_primary_10_1038_s41598_024_84508_8
crossref_primary_10_1016_j_eswa_2024_125922
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Copyright © 2023 Teng, Mi, Shi and Li.
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Snippet Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge...
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SubjectTerms Alzheimer's disease
Artificial intelligence
Autism
Brain diseases
Brain mapping
Brain research
Cognitive ability
Diagnosis
feature selection
Functional magnetic resonance imaging
Health care
Learning algorithms
Machine learning
Magnetic resonance imaging
Medical diagnosis
Mental disorders
Movement disorders
Neurodegenerative diseases
Neuroimaging
Neuroscience
Parkinson's disease
Pathogenesis
Public health
Reviews
Schizophrenia
Time series
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Title Brain disease research based on functional magnetic resonance imaging data and machine learning: a review
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Volume 17
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