A Siamese Network With Node Convolution for Individualized Predictions Based on Connectivity Maps Extracted From Resting-State fMRI Data

Xu, Linlin; Ma, Huiming; Guan, Yun; Huang, Huifang; Yang, Zhipeng; Tian, Lihong

doi:10.1109/jbhi.2023.3304974

IEEE J. Biomed. Health Inform.

2023

DOI: 10.1109/jbhi.2023.3304974

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A Siamese Network With Node Convolution for Individualized Predictions Based on Connectivity Maps Extracted From Resting-State fMRI Data

Linlin Xu

Huiming Ma

Yun Guan

et al.

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Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review

Wu,

Gao,

Zhang

et al. 2024

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The concept of ‘brain age’, derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.

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Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review

Wu,

Gao,

Zhang

et al. 2024

Tomography

View full text Add to dashboard Cite

show abstract

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A Siamese Network With Node Convolution for Individualized Predictions Based on Connectivity Maps Extracted From Resting-State fMRI Data

Cited by 1 publication

References 73 publications

Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review

Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review

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