ArticleMedical image analysis2022
An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses 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.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Multi-Scale Structural MRI Features Reveal Task-Based Functional Connectivity and Its Alterations in Psychiatric Disorders: A Collaborative Graph Attention Network Approach.Brain topography · 2026Article
- Robustness of NeuroMark-Derived Functional Networks to fMRI Spatial Normalization Across the Human Lifespan.bioRxiv : the preprint server for biology · 2026Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence.Brain topography · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification.Interdisciplinary sciences, computational life sciences · 2024Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- sMagma (New York, N.Y.) · 2024Article
- Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.NeuroImage · 2024Article
- Applications of interpretable deep learning in neuroimaging: A comprehensive review.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Aberrant brain dynamics and spectral power in children with ADHD and its subtypes.European child & adolescent psychiatry · 2023Article
- Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning.Neuroscience bulletin · 2023Review
- Article
- Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.Frontiers in neuroscience · 2023Article
- Interpretation of deep non-linear factorization for autism.Frontiers in psychiatry · 2023Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
- Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report.NeuroImage. Clinical · 2022Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNN
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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.