Neuromark Fusion: A Replicable Multimodal Template for Structure-Function Fusion of Brain MRI
Duda, M.; Baker, B.; Turner, J. A.; van Erp, T.; Calhoun, V.
Show abstract
Multimodal data fusion is a powerful technique for extracting shared and complementary information about the brain that is captured across neuroimaging modalities. Independent component analysis (ICA)-based approaches are among the most widely utilized methods for multimodal fusion, as they are data-driven, robust to noise, and capable of identifying complex, hidden linkages of varying strengths across high-dimensional datasets. However, the data-driven nature of ICA fusion approaches can make comparisons across analyses difficult without a normative framework in place. In this work, we utilize resting state functional MRI (rsfMRI) and structural MRI (sMRI) scans from >15,000 subjects to generate a normative model of multimodal structure-function linkages that can be used as a template to guide ICA fusions of new datasets. When applying this template in two datasets, resultant components exhibit high correspondence to the template even in small sample sizes, and subject-level loadings from template-derived ICs show significant associations to age.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Direct linkage detection with multimodal IVA fusion reveals markers of age, sex, cognition, and schizophrenia in large neuroimaging studies 96%
- Comparison between EEG and MEG of static and dynamic resting-state networks 96%
- Assessing methods for geometric distortion compensation in 7T gradient echo fMRI data 95%
Similar papers in this journal
Similar papers in this journal
- Structural brain architectures match intrinsic functional networks and vary across domains: A study from 15000+ individuals 95%
- Fine temporal brain network structure modularizes and localizes differently in men and women: Insights from a novel explainability framework 95%
- Imaging the columnar functional organization of human area MT+ to axis-of-motion stimuli using VASO at 7 Tesla 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.