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Hines, A. M.

Publications and source records attributed to Hines, A. M..

2 recordsLinked to original sources

Unconstrained naturalistic human brain imaging and decoding with a fully wearable high-density optical system

Understanding how the brain supports complex cognition in real-world environments requires neuroimaging systems that impose minimal constraints on natural behavior. Existing high-fidelity modalities, such as functional magnetic resonance imaging (fMRI), confine participants to the scanner, while wearable alternatives sacrifice spatial resolution or cortical coverage. Here, we developed a fully untethered whole-head optical neuroimaging system that achieves high-fidelity tomographic reconstruction through dense spatial sampling, configurable source multiplexing, and high dynamic range detection. This wearable high-density diffuse optical tomography (WHD-DOT) system achieves 151 dB effective dynamic range and nearly 3000 source-detector measurements, comparable to the highest-performing fiber-based DOT systems, while maintaining wireless, battery-powered mobility. We validate WHD-DOT across three paradigms of increasing ecological complexity, ranging from standard functional localizers to naturalistic movie viewing and live piano performance. Across all paradigms, WHD-DOT produces robust, well-localized encoding, repeatable single-trial responses, and above-chance decoding of stimulus-specific dynamics. Piano performance, which requires continuous bimanual movement and an unconstrained posture, is a rigorous real-world test for a wearable neuroimaging system. By decoding song-segment identity of free piano performance at 71.1% accuracy (chance 12.5%), WHD-DOT shows that brain activity from unconstrained, real-world behavior, previously beyond reach of high-fidelity imaging, is now both measurable and decodable.

neuroscience↗

Visual Semantic Encoding and Identification of Naturalistic Movies via High-Density Diffuse Optical Tomography

Understanding how the brain represents meaning in real-world contexts is essential for both fundamental neuroscience and clinical applications. Brain encoding and decoding models from naturalistic stimuli provide a powerful window into semantic representations. Yet, existing approaches rely on a constrained scanning environment, or on conventional fNIRS, which has been limited to sparse sampling and/or block-design paradigms. Here, we tested whether high-density diffuse optical tomography (HD-DOT), an advanced high-density tomographic optical imaging method, can support semantic encoding and decoding using naturalistic movies. We collected 3.5 hours of naturalistic movie viewing data from six participants using stimuli labeled with 1,708 categories. Encoding models robustly predicted voxel-level responses, yielding single semantic category maps consistent with prior fMRI studies. In complementary decoding analyses, we showed that DOT responses captured sufficient semantic content to identify which clips participants viewed. To assess organization across individuals, we identified a shared low-dimensional semantic space that captures common semantic dimensions. Finally, clustering analyses revealed interpretable higher-order semantic dimensions like social and animate agents, objects vs natural organisms, and textural scenes, consistently mapped across the cortex. These findings demonstrate that DOT can recover distributed, high-dimensional semantic representations from naturalistic movies, bridging fMRI-level semantic mapping with the accessibility of optical imaging.

neuroscience↗