ArticleNeuroImage2016
State-space model with deep learning for functional dynamics estimation in resting-state fMRI.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
99 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2023Pooled it
- Mapping Functional Brain Organization Using Artificial Intelligence.Chemical & biomedical imaging · 2026Review
- Atypical Development of Functional Brain Networks in Neonates with Congenital Heart Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Unraveling spatiotemporal dynamics in transdiagnosis subtypes of major depressive disorder and bipolar disorder: insights from co-activation patterns and treatment response.Molecular psychiatry · 2026Article
- A knowledge-based Bayesian network reveals shared prediction errors with physicians in Gamma Knife radiosurgery.Frontiers in neuroscience · 2026Article
- Classifying irritable bowel syndrome using spatio-temporal graph convolution networks on brain functional MRI data.Brain communications · 2026Article
- Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.BMC medical imaging · 2025Article
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- The top 100 cited articles on artificial intelligence in Alzheimer's disease and mild cognitive impairment: a bibliometric analysis.Frontiers in aging neuroscience · 2025Article
- Radiologic evaluation of the uncinate fasciculus using diffusion tensor imaging and tractography: review of technical considerations and clinical implications.Polish journal of radiology · 2025Review
- Deep linear matrix approximate reconstruction with integrated BOLD signal denoising reveals reproducible hierarchical brain connectivity networks from multiband multi-echo fMRI.Frontiers in neuroscience · 2025Article
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning.Behavioral and brain functions : BBF · 2024Article
- Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis.Scientific reports · 2024Article
- Article
- Generative dynamical models for classification of rsfMRI data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning.Human brain mapping · 2024Article
- Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans.Frontiers in neurology · 2024Article
39 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.
Funding
Abstract
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach.
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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.