ArticleHuman brain mapping2009
A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework.
Article in Human brain mapping, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 53 citations in OpenAlex.
- Alterations in Gray Matter Structure Linked to Frequency-Specific Cortico-Subcortical Connectivity in Schizophrenia via Multimodal Data Fusion.Neuroinformatics · 2025Article
- Brain connectivity disruptions in PTSD related to early adversity: a multimodal neuroimaging study.European journal of psychotraumatology · 2024Article
- Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets.Human brain mapping · 2023Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
- Trait related aberrant connectivity in clinically stable patients with schizophrenia: A seed based resting state fMRI study.Brain imaging and behavior · 2022Article
- Tools of the trade: estimating time-varying connectivity patterns from fMRI data.Social cognitive and affective neuroscience · 2021Review
- Multimodal Imaging Analysis Reveals Frontal-Associated Networks in Relation to Individual Resilience Strength.International journal of environmental research and public health · 2021Article
- A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.Journal of neuroscience methods · 2019Article
- Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.Acta psychiatrica Scandinavica · 2018Article
- Article
- Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2016Article
- Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI.Frontiers in neuroscience · 2016Article
- Fusion analysis of first episode depression: where brain shape deformations meet local composition of tissue.NeuroImage. Clinical · 2015Article
- A statistically motivated framework for simulation of stochastic data fusion models applied to multimodal neuroimaging.NeuroImage · 2014Review
- The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia.Neuroinformatics · 2013Article
- Extracting intrinsic functional networks with feature-based group independent component analysis.Psychometrika · 2013Article
- Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA.Frontiers in human neuroscience · 2013Article
- Fusion of fNIRS and fMRI data: identifying when and where hemodynamic signals are changing in human brains.Frontiers in human neuroscience · 2013Article
- A review of multivariate methods for multimodal fusion of brain imaging data.Journal of neuroscience methods · 2012Review
- MRI patterns of atrophy and hypoperfusion associations across brain regions in frontotemporal dementia.NeuroImage · 2012Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Independent component analysis (ICA) is a promising method that is increasingly used to analyze brain imaging data such as functional magnetic resonance imaging (fMRI), structural MRI, and electroencephalography and has also proved useful for group comparison, e.g., differentiating healthy controls from patients. An advantage of ICA is its ability to identify components that are mixed in an unknown manner. However, ICA is not necessarily robust and optimal in identifying between-group effects, especially in highly noisy situations. Here, we propose a modified ICA framework for multigroup data analysis that incorporates prior information regarding group membership as a constraint into the mixing coefficients. Our approach, called coefficient-constrained ICA (CC-ICA), prioritizes identification of components that show a significant group difference. The performance of CC-ICA via synthetic and hybrid data simulations is evaluated under different hypothesis testing assumptions and signal to noise ratios (SNRs). Group analysis is also conducted on real multitask fMRI data. Results show that CC-ICA improves the estimation accuracy of the independent components greatly, especially those that have different patterns for different groups (e.g., patients vs. controls); In addition, it enhances the data extraction sensitivity to group differences by ranking components with P value or J-divergence more consistently with the ground truth. The proposed algorithm performs quite well for both group-difference detection and multitask fMRI data fusion, which may prove especially important for the identification of relevant disease biomarkers.
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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.