ArticleHuman brain mapping2022
Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Longitudinal co-activation pattern analysis of menstrual cycle-related brain dynamics in primary dysmenorrhea.iScience · 2026Article
- Spatiotemporal alterations of thalamo-cortical effective connectivity in major depressive disorder patients.BMC psychiatry · 2025Article
- Frequency-dependent and temporal variability of low-frequency fluctuations in patients with primary Sjögren's syndrome.Lupus science & medicine · 2025Article
- NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders.Proceedings of machine learning research · 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
- Topological functional network analysis of cortical blood flow in hyperacute ischemic rats.Brain structure & function · 2024Article
- Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: Machine learning applied to resting-state fMRI.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Integrative role of attention networks in frequency-dependent modular organization of human brain.Brain structure & function · 2024Article
- Neural functions vary by return-to-sport status in participants with anterior cruciate ligament reconstruction: a retrospective cohort study using sub-bands of resting-state functional magnetic resonance.Frontiers in human neuroscience · 2024Article
- Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification.Brain structure & function · 2023Article
- Frequency dependent whole-brain coactivation patterns analysis in Alzheimer's disease.Frontiers in neuroscience · 2023Article
- Exploring the role of hub and network dysfunction in brain connectomes of schizophrenia using functional magnetic resonance imaging.Frontiers in psychiatry · 2023Article
- A schizophrenia study based on multi-frequency dynamic functional connectivity analysis of fMRI.Frontiers in human neuroscience · 2023Article
- Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study.Human brain mapping · 2022Article
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The resting-state human brain is a dynamic system that shows frequency-dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub-bands from slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), to slow-2 (0.198-0.25 Hz), in addition to the typical low-frequency range (0.01-0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow-5, 4, and 3, but not in slow-2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between-state transitions. Specifically, from slow-5 to slow-4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow-5. Using leave-one-pair-out, hold-out and resampling validations, the highest classification accuracy (84%) was achieved by slow-4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.
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