Joint connectivity matrix independent component analysis: Auto‐linking of structural and functional connectivities

Wu, Lei; Calhoun, Vince D.

doi:10.1002/hbm.26155

Cited by 8 publications

(21 citation statements)

References 65 publications

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“…In the presented case study, we used functional and structural connectivity matrices where network nodes are regions of interest (ROIs) defined according to the HOA. Our method can however be applied independently of the anatomical atlas used, or even exploit functional and structural connectivity matrices where nodes are spatial brain components estimated by independent components analysis (ICA) 74 – 76 , instead of ROIs. Moreover, functional connectivity matrices and the associated clustering could also be derived from other kinds of functional data (e.g.…”

Section: Discussionmentioning

confidence: 99%

Bridging functional and anatomical neural connectivity through cluster synchronization

Baruzzi,

Lodi,

Sorrentino

et al. 2023

Sci Rep

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The dynamics of the brain results from the complex interplay of several neural populations and is affected by both the individual dynamics of these areas and their connection structure. Hence, a fundamental challenge is to derive models of the brain that reproduce both structural and functional features measured experimentally. Our work combines neuroimaging data, such as dMRI, which provides information on the structure of the anatomical connectomes, and fMRI, which detects patterns of approximate synchronous activity between brain areas. We employ cluster synchronization as a tool to integrate the imaging data of a subject into a coherent model, which reconciles structural and dynamic information. By using data-driven and model-based approaches, we refine the structural connectivity matrix in agreement with experimentally observed clusters of brain areas that display coherent activity. The proposed approach leverages the assumption of homogeneous brain areas; we show the robustness of this approach when heterogeneity between the brain areas is introduced in the form of noise, parameter mismatches, and connection delays. As a proof of concept, we apply this approach to MRI data of a healthy adult at resting state.

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Section: Discussionmentioning

confidence: 99%

Bridging functional and anatomical neural connectivity through cluster synchronization

Baruzzi,

Lodi,

Sorrentino

et al. 2023

Sci Rep

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“…cmICA (Wu & Calhoun, 2023), uses multi-modal fMRI and dMRI, integrating features derived from each modality to identify ICNs shared between structural connectivity and FNC, acknowledging that there might be a mismatch between features from each modality. Although cmICA attempts to address this, the contributions of features from each modality are asymmetrical (Wu & Calhoun, 2023). In contrast, our proposed model is based on both direct and indirect features and the contribution of each modality enhances the other.…”

Section: Discussionmentioning

confidence: 99%

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

Fouladivanda

Iraji

et al. 2023

Preprint

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There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity models. Connectivity features of different modalities provide insight into brain functional organization by leveraging complementary information, especially in brain disorders as schizophrenia. To this end, we proposed a multimodal ICA model to utilize information from both structural and functional brain connectivity as well as guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity information is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model optimizes ICNs at the individual level using a multi-objective framework. We evaluated our method using synthetic data and a real dataset (including dMRI and rs-fMRI images from 300 schizophrenia patients and controls). The results demonstrated that our method enhances the functional coupling between ICs with higher structural connectivity in both synthetic and real data. Additionally, the resulting component maps showed improved modularity and enhanced network distinction, particularly in the patients group. Statistical analysis on the functional connectivity networks revealed more significant group differences when comparing the estimated structural-functional connectivity and spatially constrained ICNs with single modality ICNs. In summary, compared to an fMRI only method, the proposed joint approach for estimating ICNs showed multiple benefits from being jointly informed by structural and functional connectivity information. These findings suggest advantages in simultaneously learning from structural and functional connectivity information in brain network studies, effectively enhancing connectivity estimates based on structural connectivity.

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“…Blind methods that have been developed for raw fMRI data (4D data), including partial least squares (Chen et al ., 2009 ), multiset CCA (Correa et al ., 2010 ), distributional ICA (Wu et al ., 2021 ), and joint connectivity matrix ICA (joint cmICA) (Wu et al ., 2023 ).…”

Section: Data-driven Fusion Approaches Using Multimodal Mrimentioning

confidence: 99%

“…Incorporating the principles of jICA, Wu et al . proposed joint cmICA (Wu et al ., 2023 ), a data-driven parcellation and automated linking of voxel-wise structural connectivity and functional connectivity information from whole-brain fMRI and dMRI without the need for a prior atlas. The joint cmICA can automatically extract connectivity-based cortical sources that are shared between functional connectivity and structural connectivity, providing more flexibility in estimating sources and connectivity maps.…”

Section: Review Of Multivariate Multimodal Fusion Modelsmentioning

confidence: 99%

Data-driven multimodal fusion: approaches and applications in psychiatric research

Sui,

Zhi,

Calhoun

2023

Psychoradiology

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In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

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Joint connectivity matrix independent component analysis: Auto‐linking of structural and functional connectivities

Cited by 8 publications

References 65 publications

Bridging functional and anatomical neural connectivity through cluster synchronization

Bridging functional and anatomical neural connectivity through cluster synchronization

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

Data-driven multimodal fusion: approaches and applications in psychiatric research

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