# A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis

arXiv: https://arxiv.org/abs/2005.04573 · HTML: https://mlsanity.com/paper/2005.04573/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2020.

## Built on by (most cited first, 27 of 27)

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- [A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification](https://mlsanity.com/paper/2302.07243/) (2023)
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- [Improving the Level of Autism Discrimination through GraphRNN Link Prediction](https://mlsanity.com/paper/2202.09538/) (2022)
- [Learning Generative Factors of EEG Data with Variational auto-encoders](https://mlsanity.com/paper/2206.01939/) (2022)
- [Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks](https://mlsanity.com/paper/2302.11871/) (2023)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [DyMix: Dynamic Frequency Mixup Scheduler based Unsupervised Domain Adaptation for Enhancing Alzheimer's Disease Identification](https://mlsanity.com/paper/2410.12827/) (2024)
- [Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classification](https://mlsanity.com/paper/2603.10253/) (2026)
- [Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learning](https://mlsanity.com/paper/2603.15168/) (2026)

## Abstract (arXiv)

Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures, by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.