Biology subjects

Li, J.-S.

Publications and source records attributed to Li, J.-S..

4 recordsLinked to original sources

bioRxiv2026

Oral somatosensation shapes lingual muscle maps in macaque orofacial primary motor cortex

Oral behaviors such as chewing, swallowing, and speech depend on continuous sensory feedback from the tongue and oral tissues. Yet how oral somatosensation shapes motor cortical output to drive individual tongue muscles remains unknown. We combined electrical stimulation of 96 sites in the orofacial primary motor cortex (M1) with electromyographic (EMG) recordings from six tongue muscles in awake rhesus macaques (Macaca mulatta). Cortical motor maps of evoked muscle activity were evaluated under three oral sensory conditions: intact sensation, combined oral sensory nerve block, and selective preservation of tongue sensation. Orofacial M1 has a broadly distributed, strongly overlapping, and bilateral organization of individual tongue muscle representations. Under altered sensation, spatial reorganization, attenuation, and enhancement of cortical-to-muscle output emerged within a fast timescale. Selectively preserving tongue sensation neither restored the intact pattern, nor reproduced the combined nerve block pattern; instead, it generated a spatially distinct configuration of cortical-to-muscle outputs. Together, these findings demonstrate muscle-level, input-specific, and dynamic sensory shaping in orofacial M1, establishing oral somatosensory input as an active determinant of tongue-muscle output, rather than a peripheral modulator. The rapid reorganization suggests that altered sensation qualitatively reweights existing sensorimotor pathways, rather than merely scaling cortical output up or down, such that its disruption could immediately degrade tongue muscle coordination while enabling adaptation to sensory loss.

neuroscience↗

bioRxiv2025

An Integrated Approach to Tracking Mandibular Position Relative to Incisal and Condylar Envelopes of Motion During Intraoral Clinical Procedures: A New Look at TMJ Movements

Current methods for tracking temporomandibular joint (TMJ) movements are difficult to perform during dental procedures. Yet precise and accurate quantification of mandibular movements is critical for understanding temporomandibular biomechanics and how it may contribute to temporomandibular dysfunction. This is particularly relevant to clinical procedures that might move the mandible near or beyond its functional range of motion. We present a novel approach that integrates cone-beam computed tomography, optical intra-oral scans, and six degree-of-freedom electromagnetic sensor data to quantify mandibular movements. This method employs rigid body transformations to generate subject-specific three-dimensional (3D) envelopes of motion and assess whether incisal and condylar landmarks remain within their functional 3D envelopes of motion. We demonstrate the clinical utility of this approach through simulated mandibular poses presented relative to the limits of incisal and condylar envelopes created from that individuals voluntary border movements. Our findings reveal that condylar or incisal points in simulated mandibular poses are located beyond their normal motion envelopes, highlighting the importance of simultaneous monitoring of incisal and condylar landmarks. This methodology provides a clinically relevant tool for understanding temporomandibular biomechanics and it has the potential to signal clinicians when jaw movements during dental and oral surgical procedures approach or exceed the jaws functional range of motion and thereby could prevent adverse effects on the TMJ.

bioengineering↗

bioRxiv2024

The Functional Connectome Mediating Circadian Synchrony in the Suprachiasmatic Nucleus

Circadian rhythms in mammals arise from the spatiotemporal synchronization of [~]20,000 neuronal clocks in the Suprachiasmatic Nucleus (SCN). While anatomical, molecular, and genetic approaches have revealed diverse cell types and signaling mechanisms, the network wiring that enables SCN cells to communicate and synchronize remains unclear. To overcome the challenges of revealing functional connectivity from fixed tissue, we developed MITE (Mutual Information & Transfer Entropy), an information theory approach that infers directed cell-cell connections with high fidelity. By analyzing 3447 hours of continuously recorded clock gene expression from 9011 cells in 17 mice, we found that the functional connectome of SCN was highly conserved bilaterally and across mice, sparse, and organized into a dorsomedial and a ventrolateral module. While most connections were local, we discovered long-range connections from ventral cells to cells in both the ventral and dorsal SCN. Based on their functional connectivity, SCN cells can be characterized as circadian signal generators, broadcasters, sinks, or bridges. For example, a subset of VIP neurons acts as hubs that generate circadian signals critical to synchronize daily rhythms across the SCN neural network. Simulations of the experimentally inferred SCN networks recapitulated the stereotypical dorsal-to-ventral wave of daily PER2 expression and ability to spontaneously synchronize, revealing that SCN emergent dynamics are sculpted by cell-cell connectivity. We conclude that MITE provides a powerful method to infer functional connectomes, and that the conserved architecture of cell-cell connections mediates circadian synchrony across space and time in the mammalian SCN. HighlightsO_LIWe developed MITE, an information theory method, to accurately infer directed functional connectivity among circadian cells. C_LIO_LISCN cell types with conserved connectivity patterns spatially organize into two regions and function as generators, broadcasters, sinks, or bridges of circadian information. C_LIO_LIOne-third of VIP neurons serve as hubs that drive circadian synchrony across the SCN. C_LIO_LIKey connectivity features mediate the generation and maintenance of intercellular synchrony and daily waves of clock gene expression across the SCN. C_LI

neuroscience↗

bioRxiv2019

Diffusion Histology Imaging Detects and Classifies Glioblastoma Pathology Missed by Conventional Magnetic Resonance Imaging

PurposeGlioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted for examining GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in the clinical management of GBMs. Experimental DesignWe employ a novel Diffusion Histology Imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM. ResultsGd-enhanced T1W or hyper-intense FLAIR failed to reflect the morphological complexity underlying tumor in GBM patients. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in glioblastoma specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0% and 93.4% accuracy, respectively. ConclusionOur results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques for guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of glioblastoma. Translational RelevanceCurrent clinical diagnosis, surgical planning, and assessment of treatment response for GBM patients relies heavily on gadolinium-enhanced T1-weighted MRI, which is non-specific for tumor growth and merely reflects a disrupted blood-brain barrier. The complex tumor microenvironment and spatial heterogeneity make GBM difficult to characterize using current clinical imaging modalities. In this study, we developed a novel imaging technique to characterize and accurately predict key histological features of GBM - high tumor cellularity, tumor necrosis, and tumor infiltration. While further validation in a larger cohort of patients is needed, the current proof-of-concept approach could provide a solution to resolve important clinical questions such as the identification of true tumor progression vs. pseudoprogression or radiation necrosis.

neuroscience↗