Biology subjects

Van Erp, T.

Publications and source records attributed to Van Erp, T..

3 recordsLinked to original sources

bioRxiv2024

Functionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion Approach

The precise relationship between brain structure and function has been investigated through a multitude of lenses, but one detail that is held constant across most neuroimaging studies in this space is the identification of a singular structural basis set of the brain, upon which functional activation signals can be reconstructed to examine the linkage between structure and function. Such basis sets can be considered "functionally independent", as they are derived through structural data alone and have no explicit association to functional data. Recent work in multimodal fusion has facilitated a more integrated view of structure-function linkages by enabling the equal contribution of both modalities to the joint decomposition, resulting in components that are independent within modality but co-vary closely across modalities. These existing symmetric fusion approaches thus identify structural bases given an associated functional context. In this work we consider an additional layer of precision to the investigation of structure-function coupling by studying these context-dependent linkages in a time-resolved manner. In other words, we ask which features of brain structure become (or remain) salient given the dynamically changing functional contexts (i.e., dynamic functional connectivity states, task structure, etc.) the brain may pass through during a given fMRI scan. We introduce "dynamic fusion", an ICA-based symmetric fusion approach that enables flexible, time-resolved linkages between brain structure and dynamic brain function. We show evidence that temporally resolved, and functionally contextualized, structural basis sets can accurately reflect dynamic functional processes and capture diagnostically relevant structure-functional coupling while detecting nuanced functionally-driven structural components that cannot be captured with traditionally computed structural bases. Lastly, differential analysis of component stability across repeated scans from a control cohort reveals organization of static and dynamic structure/function coupling falls along unimodal/transmodal hierarchical lines.

neuroscience↗

bioRxiv2021

Nonlinear Functional Network Connectivity In Resting fMRI Data

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (MI), that calculates the nonlinear correlation between different brain regions. We demonstrate our proposed approach using simulated data, then apply it to a dataset previously studied in (Damaraju et al., 2014). This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in nonlinear dependencies between schizophrenia patients and healthy controls particularly in visual cortex, with controls showing more nonlinearity in most cases. Certain domains, including cognitive control, and default mode, appeared much less nonlinear, whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Further, we propose a method that captures both linear and nonlinear effects in a boosted approach. This method increases the sensitivity to group differences in comparison to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

neuroscience↗

bioRxiv2021

Multi-Spatial Scale Dynamic Interactions between Functional Sources Reveal Sex-Specific Changes in Schizophrenia

We introduce an extension of independent component analysis (ICA), called multiscale ICA (msICA), and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. msICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and -common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (1) within the subcortical domain, (2) between the somatomotor and cerebellum domains, and (3) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.

neuroscience↗