Biology subjects

Persad, A.

Publications and source records attributed to Persad, A..

3 recordsLinked to original sources

bioRxiv2025

Distinct neural temporal architectures encode rapid social expressions and sustained internal mood states

Affective processing operates across multiple temporal scales, from rapid social signaling through facial expressions to sustained internal mood states, yet the neural computational principles governing these different timescales remain unclear. Understanding how the brain implements distinct temporal architectures for momentary versus persistent affective phenomena is important to comprehending emotional processing and developing objective biomarkers for psychiatric conditions. Here, we introduced a multimodal approach combining automated facial expression monitoring and continuous intracranial electroencephalography in 2,037 electrode contacts across 16 epilepsy patients, over multiple days. Of these, 15 and 12 patients met criteria for facial expression and for mood analysis, respectively. Among patients meeting criteria, we captured 1,396 naturalistic smiles, and 3,746 neutral expressions - separated by at least 10 seconds, alongside 336 periodic mood assessments. This paradigm revealed distinct behavioral and neural computational architectures. Aperiodic neural activity in the lateral temporal cortex (79.5% accuracy) encoded facial expressions with high cross-participant generalizability. Mood states, however, showed different encoding patterns. Facial expressions provided no consistent mood indicators across participants. Critically, low-gamma power dynamics in limbic regions encoded mood states in only a subset of individuals (5 of 12 participants) with expression-mood behavioral correlations, suggesting a distinct encoding phenotype. Cross-domain analysis confirmed computational independence: neural features optimized for facial expression decoding failed to predict sustained mood states, and vice versa. These findings suggest that multiple neural mechanisms may influence underlying affective processing, with variations in their contributions between individuals. The results provide a framework for understanding individual differences in neural mood representation and establish methodological approaches for objective measurement of naturalistic affective behaviors.

neuroscience↗

bioRxiv2025

Conformal Bioprinting of Bi-phasic Jammed Bioinks, Independent of Gravity, Orientation, and Curvature

Rapid in situ bioprinting on complex, human-scale anatomical surfaces remain a key challenge for clinical translation. Here, we present a gravity-independent, conformal bioprinting strategy using bi-phasic granular bioink and multinozzle printheads capable of adapting to arbitrary surface curvatures. The bioink comprised of jammed gelatin microgels suspended in a fibrinogen matrix exhibits yield-stress behavior to maintain shape fidelity after extrusion while supporting cell viability and proliferation. Two monolithic multinozzle printhead architectures with identical bioink delivery networks were evaluated: (1) a rigid configuration for handheld bioprinting and (2) a soft robotic variant capable of real-time curvature adaptation via pneumatic actuation. Microgravity experiments aboard a parabolic flight confirmed successful bioink deposition under [~]0 g conditions. A ladder-rung microfluidic architecture ensured uniform bioink delivery across printhead nozzles, improving deposition consistency. In situ bioprinting on anatomical facial phantoms confirmed conformal, high-throughput (deposition at 20 mm{middle dot}s-1) deposition of bioink over physiologically relevant curvatures, both with and without cells. Cell-laden constructs retained >85% cell viability post-printing and supported proliferation. This work introduces a scalable bioprinting platform suitable for clinical, remote, and deep-space environments, enabling autonomous tissue fabrication. The curvature-adaptive printhead advances current in situ bioprinting capabilities, facilitating the generation of personalized grafts with complex anatomical geometries.

bioengineering↗

bioRxiv2024

Naturalistic acute pain states decoded from neural and facial dynamics

Pain is a complex experience that remains largely unexplored in naturalistic contexts, hindering our understanding of its neurobehavioral representation in ecologically valid settings. To address this, we employed a multimodal, data-driven approach integrating intracranial electroencephalography, pain self-reports, and facial expression quantification to characterize the neural and behavioral correlates of naturalistic acute pain in twelve epilepsy patients undergoing continuous monitoring with neural and audiovisual recordings. High self-reported pain states were associated with elevated blood pressure, increased pain medication use, and distinct facial muscle activations. Using machine learning, we successfully decoded individual participants high versus low self-reported pain states from distributed neural activity patterns (mean AUC = 0.70), involving mesolimbic regions, striatum, and temporoparietal cortex. High self-reported pain states exhibited increased low-frequency activity in temporoparietal areas and decreased high-frequency activity in mesolimbic regions (hippocampus, cingulate, and orbitofrontal cortex) compared to low pain states. This neural pain representation remained stable for hours and was modulated by pain onset and relief. Objective facial expression changes also classified self-reported pain states, with results concordant with electrophysiological predictions. Importantly, we identified transient periods of momentary pain as a distinct naturalistic acute pain measure, which could be reliably differentiated from affect-neutral periods using intracranial and facial features, albeit with neural and facial patterns distinct from self-reported pain. These findings reveal reliable neurobehavioral markers of naturalistic acute pain across contexts and timescales, underscoring the potential for developing personalized pain interventions in real-world settings.

neuroscience↗