ArticleNeuroImage2014
High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia.
Article in NeuroImage, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- High-order brain interactions in ketamine during rest and task: a double-blinded cross-over design using portable EEG on male participants.Translational psychiatry · 2024Trial
- A complex network perspective on brain disease.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Higher-order statistics for constructing centered edge functional connectivity.Network neuroscience (Cambridge, Mass.) · 2026Article
- Research on brain functional network property analysis and recognition methods targeting brain fatigue.Scientific reports · 2024Article
- A synergetic turn in cognitive neuroscience of brain diseases.Trends in cognitive sciences · 2024Review
- High-order brain interactions in ketamine during rest and task: A double-blinded cross-over design using portable EEG.Research square · 2024Article
- Neurobehavioral indices of gaze perception are associated with social cognition across schizophrenia patients and healthy controls.Journal of psychopathology and clinical science · 2023Article
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders.Frontiers in neuroscience · 2023Article
- Estimating high-order brain functional networks by correlation-preserving embedding.Medical & biological engineering & computing · 2022Article
- High-order brain functional network for electroencephalography-based diagnosis of major depressive disorder.Frontiers in neuroscience · 2022Article
- Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis.IEEE transactions on bio-medical engineering · 2021Article
- MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI.IEEE access : practical innovations, open solutions · 2020Article
- Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction.IEEE transactions on bio-medical engineering · 2019Article
- A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.Journal of neuroscience methods · 2019Article
- Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment.Frontiers in neuroinformatics · 2018Article
- Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification.Frontiers in neuroscience · 2018Article
- Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning.Medical image analysis · 2017Article
- Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment.Journal of Alzheimer's disease : JAD · 2016Article
- In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia.Biological psychiatry · 2015Article
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Authors and funding
11 authors.
Funding
Abstract
Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors ("network clusters"). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pairwise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important.
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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.