ArticleFrontiers in neuroscience2022
Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 33 citations in OpenAlex.
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- CLASSIFFICATION OF MILD COGNITIVE IMPAIRMENT BASED ON DYNAMIC FUNCTIONAL CONNECTIVITY USING SPATIO-TEMPORAL TRANSFORMER.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications.Health data science · 2025Review
- Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.Sensors (Basel, Switzerland) · 2024Article
- Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.Frontiers in neuroscience · 2024Article
- Prediction of Arteriovenous Access Dysfunction by Mel Spectrogram-based Deep Learning Model.International journal of medical sciences · 2024Article
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
- Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.Frontiers in neuroscience · 2023Article
- GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition.Frontiers in neuroscience · 2023Article
- Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer's Diseases.Brain sciences · 2022Article
- Learning a spatial-temporal texture transformer network for video inpainting.Frontiers in neurorobotics · 2022Article
- Deep learning and high-resolution magnetic resonance vascular wall imaging: current challenges and future perspectives.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks.
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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.