ArticleBrain imaging and behavior2016
Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification.
Article in Brain imaging and behavior, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Functional MRI-Specific Alterations in Executive Control Network in Mild Cognitive Impairment: An ALE Meta-Analysis.Frontiers in aging neuroscience · 2020Pooled it
- Group Sparse Representation Enhances Brain Network Classification of Major Depressive Disorder in Two Chinese Cohorts.Alpha psychiatry · 2026Article
- CLASSIFFICATION OF MILD COGNITIVE IMPAIRMENT BASED ON DYNAMIC FUNCTIONAL CONNECTIVITY USING SPATIO-TEMPORAL TRANSFORMER.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- A Narrative Review on Cognitive Impairment in Type 2 Diabetes: Global Trends and Diagnostic Approaches.Biomedicines · 2025Review
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data.Frontiers in computational neuroscience · 2024Article
- Minimum spanning tree analysis of unimpaired individuals at risk of Alzheimer's disease.Brain communications · 2024Article
- Article
- Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.Cognitive neurodynamics · 2023Article
- Temporal Dynamic Alterations of Regional Homogeneity in Parkinson's Disease: A Resting-State fMRI Study.Biomolecules · 2023Article
- Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.Frontiers in neuroscience · 2023Article
- State-switching and high-order spatiotemporal organization of dynamic functional connectivity are disrupted by Alzheimer's disease.Network neuroscience (Cambridge, Mass.) · 2023Article
- Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders.Frontiers in neuroscience · 2023Article
- Different Dynamic Nodal Properties Contribute to Cognitive Impairment in Patients with White Matter Hyperintensities.Brain sciences · 2022Article
- Dynamic Brain Connectivity in Resting-State FMRI Using Spectral ICA and Graph Approach: Application to Healthy Controls and Multiple Sclerosis.Diagnostics (Basel, Switzerland) · 2022Article
- Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging.NeuroImage · 2022Article
- Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis.Frontiers in neuroscience · 2022Article
- A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection.Frontiers in neuroscience · 2022Article
- Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls.Frontiers in neuroscience · 2022Article
26 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan duration. Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks. These connectivity networks may potentially capture subtle yet short neural connectivity disruptions induced by disease pathologies. Accordingly, we are motivated to utilize disrupted temporal network properties for improving control-patient classification performance. Specifically, a sliding window approach is firstly employed to generate a sequence of overlapping R-fMRI sub-series. Based on these sub-series, sliding window correlations, which characterize the neural interactions between brain regions, are then computed to construct a series of temporal networks. Individual estimation of these temporal networks using conventional network construction approaches fails to take into consideration intrinsic temporal smoothness among successive overlapping R-fMRI sub-series. To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm. This sparse learning algorithm encourages temporally correlated networks to have similar network topology and correlation strengths. We design a disease identification framework based on the estimated temporal networks, and group level network property differences and classification results demonstrate the importance of including temporally dynamic R-fMRI scan information to improve diagnosis accuracy of mild cognitive impairment patients.
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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.