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Large‐scale analysis of functional connectivity within intrinsic brain networks using functional magnetic resonance imaging (fMRI) data has been widely used for identifying biomarkers in various psychiatric disorders. While the emerging access to large neuroimaging datasets provides unprecedented opportunities for exploring brain functions, they also pose significant computational complexity challenges due to the large amount of inherent variability across individuals and the complexity of brain activity patterns. To address these challenges, this paper introduces two novel constrained ICA methods, arc‐EBM and minc‐EBM, designed to overcome the computational complexity issue by incorporating prior information into the analysis framework. The proposed methods preserve the subject variability by adaptively selecting the constrained parameters for different functional networks and individuals, while also allowing estimation flexibility for activities not covered by the prior information through the concept of free components. Our methods are shown to enhance the precision of functional network estimation and improve the capture of subject variability across different cohorts. We evaluate the proposed methods using both synthetic and real fMRI data. By applying the proposed methods to a resting‐state fMRI dataset including 179 subjects, both algorithms successfully reveal significant group differences in functional network connectivity between healthy controls and schizophrenia patients. The observed group differences, particularly the abnormal connectivity alterations in networks involving the thalamus, subthalamus/hypothalamus, and superior temporal gyrus, align with findings from previous clinical studies. Furthermore, our results demonstrate that the constraint parameters adaptively selected by arc‐EBM reveal more diverse resting‐state network structures in individuals with schizophrenia compared with healthy controls. This finding is consistent with prior studies and suggests that the selected constraint parameters could serve as potential biomarkers for mental disorder diagnosis.
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Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks
The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 − 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.
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- Award ID(s):
- 2019976
- PAR ID:
- 10574293
- Editor(s):
- Dimitriadis, Stavros I
- Publisher / Repository:
- PLoS
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 16
- Issue:
- 11
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0250755
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pone.0250755
