ReviewFrontiers in neuroscience2020
A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.
Review in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it, 214 citations in OpenAlex.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Cross-modal latent alignment enables efficient compression of wearable sEMG and accelerometer signals.Scientific reports · 2026Article
- Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.Scientific reports · 2026Article
- Cross-domain dynamic routing decoders for multi-domain generalization in ultrasound imaging.Quantitative imaging in medicine and surgery · 2026Article
- SliceMap: a binary classification-driven 2D pipeline for detecting discriminative candidate regions in brain MRI.Frontiers in neuroimaging · 2026Article
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.Scientific reports · 2025Article
- Unveiling the invisible: How cutting-edge neuroimaging transforms adolescent depression diagnosis.World journal of psychiatry · 2025Article
- Artificial intelligence for risk analysis-A risk characterization perspective on advances, opportunities, and limitations.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- Multimodal Brain Growth Patterns: Insights from Canonical Correlation Analysis and Deep Canonical Correlation Analysis with Auto-Encoder.Information (Basel) · 2025Article
- Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- Using resting-state functional magnetic resonance imaging and contrastive learning to explore changes in the Parkinson's disease brain network and correlations with gait impairment.Quantitative imaging in medicine and surgery · 2025Article
- Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling.Frontiers in public health · 2025Observational
- Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions.Frontiers in neuroinformatics · 2025Review
- Advances in Diagnostic Approaches for Alzheimer's Disease: From Biomarkers to Deep Learning Technology.CNS & neurological disorders drug targets · 2025Review
- An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network.Patterns (New York, N.Y.) · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- A multi-slice attention fusion and multi-view personalized fusion lightweight network for Alzheimer's disease diagnosis.BMC medical imaging · 2024Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.