Biology subjects

Hartford, J. W.

Publications and source records attributed to Hartford, J. W..

5 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

Noninvasive profiling of input-output excitability curves in human prefrontal cortex

Abstract.O_ST_ABSBackgroundC_ST_ABSThe prefrontal cortex plays a critical role in cognitive control and behavior, and its dysfunction has been linked to numerous psychiatric and neurological disorders. However, noninvasive measurement of prefrontal activity remains challenging, limiting our understanding of how to optimize prefrontal treatments. Input-output relationships reveal how neural circuits respond to different inputs and are essential for determining optimal treatment parameters and understanding individual variability in treatment response, yet systematic investigation of prefrontal input-output relationships has been lacking. ObjectiveTo characterize human prefrontal excitability with input-output (I/O) curves. MethodsWe employed transcranial magnetic stimulation (TMS) with electroencephalography in a randomized mixed-block design with 28 healthy participants receiving single-pulse TMS to left dorsolateral prefrontal cortex (dlPFC) across 12 stimulation intensities (60-140% of resting motor threshold). We quantified prefrontal excitability using early local TMS-evoked potentials (EL-TEPs), cortical responses measured locally 20-60 ms post-stimulus. ResultsWe observed a strong effect of TMS intensity on prefrontal EL-TEP amplitudes. Sigmoidal EL-TEP I/O curves were observed in 57% of participants, with the sigmoidality partially explained by EL-TEP signal quality. Correlations were observed between EL-TEP and motor-evoked potential curve parameters, but intensity parameterization approaches did not significantly differ in explaining inter-individual EL-TEP response variability. Reliable EL-TEPs could be obtained using fewer TMS pulses at high intensities, and test- retest assessments revealed robust I/O curve profiles. ConclusionsThese findings provide a systematic noninvasive characterization of prefrontal input-output physiology, establishing a framework for estimating prefrontal excitability. The comparison of various intensity parameterizations motivates the need for enhanced models and individualized measurement of stimulation responses. HighlightsO_LIWe present noninvasive input-output curves for prefrontal TMS. C_LIO_LIEL-TEPs exhibit robust dose-dependent responses to TMS intensity. C_LIO_LISigmoidal I/O curves observed in 57% of participants, with a strong dependence on signal quality. C_LIO_LICorrelations observed between MEP and prefrontal EL-TEP curve parameters. C_LIO_LIHigh test-retest reliability and rapid protocols at [≥]110% rMT C_LI

neuroscience↗

bioRxiv2025

Sensory Entrained TMS (seTMS) enhances motor cortex plasticity

Neural excitability fluctuates with sensory events, creating windows of opportunity to enhance brain stimulation. Repetitive transcranial magnetic stimulation (TMS), including intermittent theta burst stimulation (iTBS), is a promising treatment for neurological and psychiatric disorders, but does not account for fluctuations in neural excitability, likely contributing to variable outcomes. Sensory Entrained TMS (seTMS) leverages sensorimotor oscillations to enhance corticospinal responses, but the sustained effects as a repetitive protocol are unknown. We extended seTMS to iTBS measuring motor-evoked potentials (MEPs) as a physiological readout in a randomized crossover study comparing standard iTBS with sensory entrained iTBS (se-iTBS, n=20). se-iTBS more than doubled the MEP effect (55% vs. 26% MEP enhancement) and persisted for at least 30 minutes. Notably, more than 80% of participants showed larger responses with se-iTBS at all time points. se-iTBS may provide a robust and practical framework for optimizing TMS that bridges electrophysiological mechanisms and clinical applications.

neuroscience↗

bioRxiv2025

Scalp EEG predicts intracranial brain activity in humans

Inferring deep brain activity from noninvasive scalp recordings remains a fundamental challenge in neuroscience. Here, we analyzed concurrent scalp and intracranial recordings from 1918 electrode contacts across 20 patients affected by drug-resistant epilepsy undergoing intracranial depth electrode monitoring for pre-surgical evaluation to establish predictive relationships between surface and deep brain signals. Using regularized and cross-validated linear regression within subjects, we demonstrate that scalp recordings can predict spontaneous intracranial activity, with accuracy varying by region, depth, and frequency. Low-frequency signals (<12 Hz) were most predictable, with our models explaining approximately 10% of intracranial signal variance across contacts. Prediction accuracy decreased with contact depth, particularly for high-frequency signals. Using Bayesian modeling with leave-one-patient-out cross-validation, we observed generalizable prediction of activity in mesial temporal, prefrontal, and orbitofrontal cortices, explaining 10-12% of low-frequency signal variance. This scalp-to-intracranial mapping derived from spontaneous activity was further validated by its correlation with scalp responses evoked by direct electrical stimulation. These findings support the development of improved inverse models of brain activity and potentially more accurate scalp-based markers of disease and treatment response.

neuroscience↗

bioRxiv2024

Sensory Entrained TMS (seTMS) enhances motor cortex excitability

Transcranial magnetic stimulation (TMS) applied to the motor cortex has revolutionized the study of motor physiology in humans. Despite this, TMS-evoked electrophysiological responses show significant variability, due in part to inconsistencies between TMS pulse timing and ongoing brain oscillations. Variable responses to TMS limit mechanistic insights and clinical efficacy, necessitating the development of methods to precisely coordinate the timing of TMS pulses to the phase of relevant oscillatory activity. We introduce Sensory Entrained TMS (seTMS), a novel approach that uses musical rhythms to synchronize brain oscillations and time TMS pulses to enhance cortical excitability. Focusing on the sensorimotor alpha rhythm, a neural oscillation associated with motor cortical inhibition, we examine whether rhythm-evoked sensorimotor alpha phase alignment affects primary motor cortical (M1) excitability in healthy young adults (n=33). We first confirmed using electroencephalography (EEG) that passive listening to musical rhythms desynchronizes inhibitory sensorimotor brain rhythms (mu oscillations) around 200 ms before auditory rhythmic events (27 participants). We then targeted this optimal time window by delivering single TMS pulses over M1 200 ms before rhythmic auditory events while recording motor-evoked potentials (MEPs; 19 participants), which resulted in significantly larger MEPs compared to standard single pulse TMS and an auditory control condition. Neither EEG measures during passive listening nor seTMS-induced MEP enhancement showed dependence on musical experience or training. These findings demonstrate that seTMS effectively enhances corticomotor excitability and establishes a practical, cost-effective method for optimizing non-invasive brain stimulation outcomes.

neuroscience↗