HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals
Year: 2026
Type: preprint
Abstract: 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 brain's 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.
Institution: University of California, San Francisco
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Computational dissection of cellular and network vulnerability in Alzheimer's and related dementias, Mapping the Human Connectome: Structure, Function, and Heritability, Network Modelling of Multimodal Dynamics in Alzheimer’s Disease and Dementia, Multimodal modeling framework for fusing structural and functional connectome data, Understanding the dynamic interactions between tau pathology and microgliamediated inflammation in Alzheimer's Disease
OpenAlex ID: W7118967724
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