Improving Diagnosis, Treatment, and Prognosis of Neuropsychiatric Disorders by Leveraging Neuroimaging-based Machine Learning

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Editorial

13 April 2022

Editorial: Improving Diagnosis, Treatment, and Prognosis of Neuropsychiatric Disorders by Leveraging Neuroimaging-based Machine Learning

Baojuan Li

,

Hongbing Lu

,

Yu-Feng Zang

,

Hui Shen

, 1 more and

Jian Liu

1,580 views

1 citations

Editors

5

Baojuan Li

Massachusetts General Hospital, Harvard Medical School

HONGBING LU

Air Force Medical University

Yu-Feng Zang

Hangzhou Normal University

Hui Shen

National University of Defense Technology

Qiuyun Fan

Tianjin University

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About

In recent years, the world has witnessed a significant increase in the number of machine learning studies using medical images. The results have consistently demonstrated the potential of machine learning in a variety of clinical settings, such as detection of diabetic retinopathy using retinal fundus photographs, skin cancer classification with dermoscopic images, breast cancer detection and diagnosis with mammography, or polyp detection with virtual colonoscopy. These clinical applications have already made a significant contribution to improving the reliability and accuracy of computer-assisted systems for disease detection and diagnosis.

Recently, machine learning has also demonstrated its great potential in neuroscience. Data from noninvasive neuroimaging techniques, such as structural and functional MRI images, electroencephalography and magnetoencephalography, are traditionally used to investigate brain structure and functions. We are accessing through machine learning models a level of analysis which allows a clearer understanding of how the brain works in healthy and diseased people. Indeed, those models are capable of detecting subtle and complex variations in neuroimaging data, which can provide support for more accurate diagnoses, treatment strategies, and prognosis of neuropsychiatric disorders. Moreover, novel targets for brain stimulation treatments could be identified.

This Research Topic aims to advance our understanding of healthy and diseased brains while contributing to improving diagnosis, treatment, and prognosis of neuropsychiatric disorders. We also hope to facilitate the identification of potential imaging biomarkers.

Specifically, we welcome original research articles and reviews that focus on new methods, and on the applications of novel machine learning approaches and neuroimaging techniques in the following conditions:

- schizophrenia spectrum and other psychotic disorders,
- depressive disorders, bipolar and related disorders,
- autism spectrum disorder, attention-deficit/hyperactivity disorder, etc…

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Editors

Baojuan Li

Massachusetts General Hospital, Harvard Medical School

HONGBING LU

Air Force Medical University

Yu-Feng Zang

Hangzhou Normal University

Hui Shen

National University of Defense Technology

Qiuyun Fan

Tianjin University

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141,733 Total views

105,467 Article views

34,025 Article downloads

2,241 Topic views

Published In

Frontiers in Neuroscience

Brain Imaging Methods

Frontiers in Psychiatry

Neuroimaging

Frontiers in Neurology

Applied Neuroimaging, Brain Imaging Methods

Frontiers in Neuroimaging

Brain Imaging Methods

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