Biology subjects

Sipes, B. S.

Publications and source records attributed to Sipes, B. S..

3 recordsLinked to original sources

bioRxiv2026

HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals

Brain regions perform distinct computations, and their signals propagate through the whole-brain white matter network. Yet, mathematical models that describe this signal propagation via purely passive diffusion can predict a considerable amount of the observed functional connectivity between regions. This raises a critical question: if so much functional connectivity can be explained by a passive process, how can we isolate the active process? Here, we calculate in closed-form an estimate for such an active signal in functional MRI by spatially deconvolving the effect of passive signal spread over the brains structural connectivity using a higher-order network diffusion (HONeD) model. Across 770 Human Connectome Project subjects, we show that the resulting HONeD-innovation (HONeD-in) signal 1) sparsifies functional connectivity while retaining a well-connected network, 2) remodels resting-state networks (RSNs), 3) mixes the unimodal- multimodal hierarchical organization of RSNs into a circle with no clear hierarchy, and 4) deblurs task-activation maps. Together, our results highlight HONeD deconvolution as a generalizable new way to study resting-state and task fMRI brain signals.

neuroscience↗

bioRxiv2024

Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI

Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brains functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signals spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.

neuroscience↗

bioRxiv2024

Integrative, Segregative, and Degenerate Harmonics of the Structural Connectome

Unifying integration and segregation in the brain has been a fundamental puzzle in neuroscience ever since the conception of the "binding problem." Here, we introduce a framework that places integration and segregation within a continuum based on a fundamental property of the brain-its structural connectivity graph Laplacian harmonics and a new feature we term the gap-spectrum. This framework organizes harmonics into three regimes-integrative, segregative, and degenerate- that together account for various group-level properties. Integrative and segregative harmonics occupy the ends of the continuum, and they share properties such as reproducibility across individuals, stability to perturbation, and involve "bottom-up" sensory networks. Degenerate harmonics are in the middle of the continuum, and they are subject-specific, flexible, and involve "top-down" networks. The proposed framework accommodates inter-subject variation, sensitivity to changes, and structure-function coupling in ways that offer promising avenues for studying cognition and consciousness in the brain.

neuroscience↗