Abstract #0928

Face Decoding and Reconstruction from 7T Laminar fMRI Data using A Diffusion Generative Model

Nguyen Phuoc Huynh¹ and Gopikrishna Deshpande^1,2,3,4,5,6

¹Auburn University Neuroimaging Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, ²Department of Psychological Sciences, Auburn University, Auburn, AL, United States, ³Alabama Advanced Imaging Consortium, Birmingham, AL, United States, ⁴Center for Neuroscience, Auburn University, Auburn, AL, United States, ⁵Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India, ⁶Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad, India

Synopsis

Keywords: AI Diffusion Models, Brain Connectivity, Laminar fMRI

Motivation: Face recognition and perception are vital for social interactions, yet its neural mechanisms, including the interplay between feedforward and feedback processes, remain poorly understood.

Goal(s): Our goal is to use ultrahigh field fMRI data to investigate the contributions of different cortical layers to face perception in the visual cortex of a subject engaged in a naturalistic viewing paradigm.

Approach: We employed a diffusion generative model to reconstruct video scenes, particularly those featuring human faces, from laminar brain activity.

Results: The number of accurately reconstructed face images suggests that the superficial and deep layers across the visual areas significantly contribute to face recognition.

Impact: Brain disorders, like stroke and prosopagnosia, can impair brain regions for facial processing, making face perception difficult. By understanding the neural circuitry involved in face perception, researchers may identify pathways that could be targeted to alleviate symptoms in these conditions

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