Biology subjects

Pouratian, N.

Publications and source records attributed to Pouratian, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Conserved fiber topography of the anterior limb of the internal capsule in treatment-resistant psychiatric patients

IntroductionThe anterior limb of the internal capsule (ALIC) is a major white matter highway connecting prefrontal cortical (PFC) regions to the thalamus, brainstem, and subthalamic nucleus. Structural and functional abnormalities within the ALIC circuit have been associated with many neuropsychiatric disorders, including obsessive-compulsive disorder (OCD) and depression, and deep brain stimulation (DBS) may provide effective treatment to some of these patients. However, it remains unclear whether the well-characterized topographic organization of the ALIC observed in healthy individuals and preclinical models is preserved in treatment-resistant psychiatric populations. MethodsWe first used diffusion tractography to evaluate the topography of PFC and subcortical fibers through the ALIC in patients with treatment-resistant OCD (n=18) and depression (n=5). In depression patients, we also evaluated ALIC topography using cerebro-cerebral evoked potentials (CCEPs) elicited by single-pulse electrical stimulation (SPES) of DBS leads in the ALIC and recordings in the ventral PFC (vPFC). ResultsThe topographic organization of PFC and subcortical projections is preserved in the ALIC among treatment-resistant psychiatric patients, consistent with patterns observed in healthy individuals and preclinical models. CCEP recordings in the ventral PFC showed a ventral ALIC to medial vPFC/dorsal ALIC to lateral vPFC response pattern in the left hemisphere, but not in the right. ConclusionOur findings confirm that topographic patterns within the ALIC previously identified using preclinical models and healthy controls are preserved in treatment-resistant psychiatric patients. Furthermore, by linking white matter topography to stimulation effects, this work supports more precise and individualized neuromodulatory strategies for neuropsychiatric disorders.

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bioRxiv2026

Pathological cortico-STN beta coupling in Parkinson's disease is confined to beta bursts

Abnormal beta-band activity (13-30 Hz) within the cortico-basal ganglia network is a hallmark of Parkinsons disease (PD) and is closely linked to motor impairment. Pathological beta activity in the subthalamic nucleus (STN) occurs predominantly as brief, high-amplitude bursts rather than continuous oscillations. Although beta-band coherence between the STN and cortex increases during bursts, it remains unclear whether cortico-STN beta coupling persists outside these bursts. Using intraoperative STN local field potentials and simultaneous cortical electrocorticography from seven patients undergoing deep brain stimulation implantation surgery, cortico-STN beta coupling during burst and non-burst epochs was compared. Coupling was assessed using magnitude-squared coherence and the debiased weighted phase lag index (dwPLI) and compared against surrogate distributions generated by circular time-shifting. Both coupling metrics were significantly elevated during burst epochs relative to non-burst periods. During non-burst epochs, coupling collapsed to surrogate levels, indicating no evidence of sustained synchronization. Time-resolved analyses further demonstrated that elevated coupling was confined to burst epochs. Although a subset of motor cortical contacts exhibited elevated baseline coherence, coupling was less evident using dwPLI. These findings suggest that pathological cortico-STN beta coupling in PD is preferentially expressed during beta bursts rather than sustained across non-burst epochs, with implications for adaptive neuromodulation strategies.

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bioRxiv2025

An integrative computational approach for obstacle avoidance during action selection

Action selection in cluttered environments, where individuals must simultaneously pursue goals and avoid obstacles, presents a significant challenge for the brain. To understand the underlying mechanisms of action selection in such contexts, we propose a computational model that extends stochastic optimal control theory through a novel framework for obstacle avoidance. The model decomposes action selection as a weighted combination of individual control policies, each generated for either target approach or obstacle avoidance. By integrating value information from goals, obstacles, and actions into a unified measure of "relative desirability", the model dynamically determines the contribution of each policy to the overall action selection process. We evaluated the framework using simulated target-reaching tasks in cluttered environments based on previous human studies. The results showed that the model captures key features of human motor behavior, including the influence of obstacle properties on movement trajectories and the transient tendency to initiate movements toward obstacles before avoidance. This work offers new insights into the dynamic interaction between approach and avoidance behaviors, providing a comprehensive framework for understanding action selection in complex and naturalistic settings. Author SummaryEvery day, we make countless movements that require us to reach for something while avoiding obstacles--for example, grabbing a cup without knocking over nearby objects. How the brain selects such actions in cluttered environments remains poorly understood. In this study, we developed a computational model that explains how people plan and control movements when both goals and obstacles are present. Our model assumes that the brain prepares multiple potential actions at once--some for reaching the goal and others for avoiding obstacles--and then combines them based on how desirable each option is at any moment. Using computer simulations of reaching movements, we found that the model reproduced key patterns observed in human behavior, including the brief tendency to move toward an obstacle before steering away from it. By showing how the brain might continuously balance approach and avoidance drives, our work offers a new way to understand how people make rapid and flexible movement decisions in complex, everyday settings.

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bioRxiv2025

Effect of Large Language Models on P300 Speller Performance with Cross-Subject Training

Amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease, severely impairs communication, requiring assistive technologies that restore interaction. The P300 speller brain-computer interface (BCI) enables communication by translating EEG responses into text; however, its practical adoption is limited by slow typing speed and the need for subject-specific calibration. Recent work has demonstrated that large language models (LLMs), such as GPT-2, can significantly improve the performance of the P300 speller by predicting words. However, it remains unclear whether these gains are model-specific or represent a broader trend across language models. Furthermore, the fundamental performance limits of LLM-assisted P300 spellers have not been systematically characterized within a unified decoding framework. In this study, we address these gaps through a systematic multi-model theoretical analysis framework. We evaluate a wide range of language models and introduce an idealized LLM to establish upper bounds on achievable performance. In addition, we incorporate cross-subject classifier training to reduce calibration requirements and assess generalization across subjects. Using extensive simulations on EEG data from 78 subjects, we demonstrate that the evaluated models consistently achieve substantial improvements in typing speed, with gains of up to [~]40% (across-subject training) and [~]75% (within-subject training) over conventional approaches. More importantly, we show that multiple models, despite architectural differences, operate within 5% of the theoretical performance bound, indicating diminishing returns from further model scaling. These improvements generalize across both within-subject and across-subject classifiers. Our results suggest that LLM-assisted P300 spellers are approaching their fundamental performance limits within the considered decoding framework, shifting the primary bottleneck from language modeling to neural signal decoding. This work provides both a practical framework for improving BCI communication and a theoretical perspective on its achievable limits.

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bioRxiv2025

The neural basis of emotional generalization in empathy

The essence of empathy is generalization of emotion across persons. Here, we leverage recent theoretical advances in the neuroscience of generalization to help us understand empathy. We measured brain activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one identifying emotional responses in others. We quantified the representational geometry of local field potential (LFP) high-gamma activity in four regions: the medial temporal lobe, anterior cingulate cortex, orbitofrontal cortex, and insula. We found encoding of both self- and other-emotions in all four regions, but codes for emotion and person are disentangled (that is, factorized) in the insula, but not the other regions. This factorized representation allows for cross-person generalization of emotion in a way that tangled (non-factorized) representations do not. Together, these results support the hypothesis that the insula uniquely contributes to social mirroring processes by which we understand emotions across individuals.

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bioRxiv2025

Multivariate Associations Between Neurophysiological Activity and Sensory Motor Cortical Morphometry in Parkinson's Disease

Parkinsons disease (PD) involves progressive neurodegeneration and distinctive structural and functional alterations in cortico-basal ganglia circuits. This study proposes bridging the gap between structural and functional biomarkers to uncover fundamental mechanisms underlying PD pathophysiology and support more comprehensive diagnostic and therapeutic approaches. We examined 50 PD patients using high-resolution MRI to quantify cortical thickness, surface area, and volume in sensorimotor regions, alongside intraoperative neurophysiological recordings of spectral power, burst parameters, and coherence. Pairwise correlation and Sparse Partial Least Squares analyses revealed significant low-dimensional latent relationships, particularly linking cortical atrophy in Brodmann areas BA1-BA6 with alpha and low-beta burst abnormalities. These associations remained robust after controlling for age, disease duration, and symptom severity, and were absent in a control cohort with essential tremor. These findings highlight the value of multimodal approaches for uncovering structure-function interactions in PD and highlight the potential of integrated biomarkers for improving diagnosis and treatment strategies.

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bioRxiv2024

An AI-Driven Model of Consciousness, Its Disorders, and Their Treatment

Understanding disorders of consciousness (DOC) remains one of the most challenging problems in neuroscience, hindered by the lack of experimental models for probing mechanisms or testing interventions [1]. To address this, we introduce a generative adversarial AI framework [2] that pits deep neural networks--trained to detect consciousness across over 680,000 neuroelectro-physiology samples and validated on 565 patients, healthy volunteers, and animals--against interpretable, machine learning-driven neural field models [3]. This adversarial architecture produces biologically realistic simulations of both conscious and comatose brains that recapitulate empirical neurophysiological features across humans, monkeys, rats, and bats. Without explicit programming, the AI model retrodicts known DOC responses to brain stimulation and generates testable predictions about unconsciousness mechanisms. Two such predictions are validated here: selective disruption of the basal ganglia indirect pathway, supported by diffusion MRI in 51 DOC patients; and increased cortical inhibitory-to-inhibitory synaptic coupling, supported by RNA sequencing from resected brain tissue in six human coma patients and a rat stroke model. The model also identifies high-frequency subthalamic nucleus stimulation as a promising DOC intervention, supported here using electrophysiology data from human patients. This work introduces an AI framework for causal inference and therapeutic discovery in consciousness research and complex systems more broadly.

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bioRxiv2024

Insula uses overlapping codes for emotion in self and others

In daily life, we must recognize others emotions so we can respond appropriately. This ability may rely, at least in part, on neural responses similar to those associated with our own emotions. We hypothesized that the insula, a cortical region near the junction of the temporal, parietal, and frontal lobes, may play a key role in this process. We recorded local field potential (LFP) activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one on identifying facial emotional responses in others. We found matching patterns of gamma- and high-gamma band activity for the two tasks in the insula. Three other regions (MTL, ACC, and OFC) clearly encoded both self- and other-emotions, but used orthogonal activity patterns to do so. These results support the hypothesis that the insula plays a particularly important role in mediating between experienced vs. observed emotions.

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bioRxiv2023

Computational mechanism underlying switching of motor plans

Surviving in a constantly changing environment requires not only the ability to select actions, but also the flexibility to stop and switch actions when necessary. Extensive research has been devoted to understanding how the brain switches actions, yet the computations underlying switching and how it relates to selecting and stopping processes remain elusive. A central question is whether switching is an extension of the stopping process or involves different mechanisms. To address this question, we modeled action regulation tasks with a neurocomputational theory and evaluated its predictions on individuals performing reaches in a dynamic environment. Our findings suggest that, unlike stopping, switching does not necessitate a proactive pause mechanism to delay movement onset. However, switching engages a pause mechanism after movement onset, if the new target location is unknown prior to switch signal. These findings offer a new understanding of the action-switching computations, opening new avenues for future neurophysiological investigations.

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bioRxiv2023

Brain mechanisms underlying the emotion processing bias in treatment-resistant depression

Depression is associated with a cognitive bias towards negative information and away from positive information. This biased emotion processing may underlie core depression symptoms, including persistent feelings of sadness or low mood and a reduced capacity to experience pleasure. The neural mechanisms responsible for this biased emotion processing remain unknown. Here, we had a unique opportunity to record stereotactic electroencephalography (sEEG) signals in the amygdala and prefrontal cortex (PFC) from 5 treatment-resistant depression (TRD) patients and 12 epilepsy patients (as control) while they participated in an affective bias task in which happy and sad faces were rated. First, compared with the control group, patients with TRD showed increased amygdala responses to sad faces in the early stage (around 300 ms) and decreased amygdala responses to happy faces in the late stage (around 600 ms) following the onset of faces. Further, during the late stage of happy face processing, alpha-band activity in PFC as well as alpha-phase locking between the amygdala and PFC were significantly greater in TRD patients compared to the controls. Second, after deep brain stimulation (DBS) delivered to bilateral subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS), atypical amygdala and PFC processing of happy faces in TRD patients remitted toward the normative pattern. The increased amygdala activation during the early stage of sad face processing suggests an overactive bottom-up processing system in TRD. Meanwhile, the reduced amygdala response during the late stage of happy face processing could be attributed to inhibition by PFC through alpha-band oscillation, which can be released by DBS in SCC and VC/VS.

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bioRxiv2023

Irregularity of instantaneous gamma frequency in the motor control network characterize visuomotor and proprioceptive information processing

BackgroundGoal-directed movements involve integrating proprioceptive and visuo-motor information. Although the neural correlates of such information processing are known, the details of how sensory-motor integration occurs are still largely unknown. ObjectiveThe study aims to characterize movements with different sensory goals, by contrasting the neural activity involved in processing proprioceptive and visuo-motor information. To accomplish this, we have developed a new methodology that utilizes the irregularity of the instantaneous gamma frequency parameter for characterization. ApproachIn this study, 8 essential tremor patients undergoing an awake deep brain stimulation (DBS) implantation surgery repetitively touched the clinicians finger (forward visually-guided/FV movement) and then ones own chin (backward proprioceptively-guided/BP movement). Neural electrocorticographic (ECoG) recordings from the motor (M1), somatosensory (S1), and posterior parietal cortex (PPC) were obtained and band-pass filtered in the gamma range (30-80Hz). The irregularity of the inter-event intervals (IEI; inverse of instantaneous gamma frequency) were examined as: 1) correlation between the amplitude and its proceeding IEI, and 2) auto-information of the IEI time series. We further explored the network connectivity after segmenting the FV and BP movements by periods of accelerating and decelerating forces, and applying the IEI parameter to transfer entropy methods. ResultsConceptualizing that the irregularity in IEI reflects active new information processing, we found the highest irregularity in M1 during BP movement, highest in PPC during FV movement, and the lowest during rest at all sites. Also, connectivity was the strongest from S1 to M1 and from S1 to PPC during FV movement with accelerating force and weakest during rest. SignificanceWe introduce a novel methodology that utilize the instantaneous gamma frequency (i.e., IEI) parameter in characterizing goal-oriented movements with different sensory goals, and demonstrate its use to inform the directional connectivity within the motor cortical network. This method successfully characterizes different movement types, while providing interpretations to the sensory-motor integration processes.

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bioRxiv2023

Criticality supports cross-frequency cortical-thalamic information transfer during conscious states

Consciousness is thought to be regulated by bidirectional information transfer between the cortex and thalamus, but the nature of this bidirectional communication - and its possible disruption in unconsciousness - remains poorly understood. Here, we present two main findings elucidating mechanisms of corticothalamic information transfer during conscious states. First, we identify a highly preserved spectral channel of cortical-thalamic communication which is present during conscious states but which is diminished during the loss of consciousness and enhanced during psychedelic states. Specifically, we show that in humans, mice, and rats, information sent from either the cortex or thalamus via{delta} /{theta}/ waves (~1.5-13 Hz) is consistently encoded by the other brain region by high{gamma} waves (~50-100 Hz); moroever, unconsciousness induced by propofol anesthesia or generalized spike-and-wave seizures diminishes this cross-frequency communication, whereas the psychedelic 5-methoxy-N,N/-dimethyltryptamine (5-MeO-DMT) enhances this interregional communication. Second, we leverage numerical simulations and neural electrophysiology recordings from the thalamus and cortex of human patients, rats, and mice to show that these changes in cross-frequency cortical-thalamic information transfer are mediated by excursions of low-frequency thalamocortical electrodynamics toward/away from edge-of-chaos criticality, or the phase transition from stability to chaos. Overall, our findings link thalamic-cortical communication to consciousness, and further offer a novel, mathematically well-defined framework to explain the disruption to thalamic-cortical information transfer during unconscious states.

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bioRxiv2022

Frontotemporal network engagement by human intracranial stimulation in limbic hubs

BackgroundPrefrontal circuits in the human brain play an important role in cognitive and affective processing. Neuromodulation therapies delivered to certain key hubs within these circuits are being used with increasing frequency to treat a host of neuropsychiatric disorders. However, the detailed neurophysiological effects of stimulation to these hubs are largely unknown. MethodsHere, we performed intracranial recordings across prefrontal networks while delivering electrical stimulation to two well-established white matter hubs involved in cognitive regulation and depression: the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS). ResultsWe demonstrate a shared frontotemporal circuit consisting of the ventromedial PFC, amygdala, and lateral orbitofrontal cortex where gamma oscillations are differentially modulated by stimulation target. Additionally, we found subject-specific responses to stimulation in the dorsal anterior cingulate cortex and demonstrate the capacity for further tuning of neural activity using current-steered stimulation. ConclusionsOur findings indicate a potential neurophysiological mechanism for the dissociable therapeutic effects seen across the SCC and VC/VS DBS targets for psychiatric neuromodulation and our results lay the groundwork for personalized, network-guided neurostimulation therapy.

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bioRxiv2022

Cognition through internal models: Mirror neurons as one manifestation of a broader mechanism

Cognition relies on transforming sensory inputs into a more generalizable understanding. Mirror neurons are proposed to underlie this process, yet they fail to explain many key features of human thinking and learning. Here we hypothesize that mirror-like responses are one limited view into a more general framework by which internal models of the world are built and used. We recorded populations of single neurons in the human posterior parietal cortex as a participant felt or observed diverse tactile stimuli. We found that mirror-like responses were fragile and embedded within a richer population response that encoded generalizable and compositional features of the stimuli. We speculate that populations of neurons support versatile understanding, not through mirroring, but instead by encoding representational building blocks of cognition. One-Sentence SummarySimilar neural responses during observed and experienced sensations are mediated by shared compositional building blocks, not mirror neurons.

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bioRxiv2022

A neurocomputational theory of action regulation predicts motor behavior in neurotypical individuals and patients with Parkinsons disease

Surviving in an uncertain environment requires not only the ability to select the best action, but also the flexibility to withhold inappropriate actions when the environmental conditions change. Although selecting and withholding actions have been extensively studied in both human and animals, there is still lack of consensus on the mechanism underlying these action regulation functions, and more importantly, how they inter-relate. A critical gap impeding progress is the lack of a computational theory that will integrate the mechanisms of action regulation into a unified framework. The current study aims to advance our understanding by developing a neurodynamical computational theory that models the mechanism of action regulation that involves suppressing responses, and predicts how disruption of this mechanism can lead to motor deficits in Parkinsons disease (PD) patients. We tested the model predictions in neurotypical individuals and PD patients in three behavioral tasks that involve free action selection between two opposed directions, action selection in the presence of conflicting information and abandoning an ongoing action when a stop signal is presented. Our results and theory suggest an integrated mechanism of action regulation that affects both action initiation and inhibition. When this mechanism is disrupted, motor behavior is affected, leading to longer reaction times and higher error rates in action inhibition. Author SummaryHumans can rapidly regulate actions according to updated demands of the environment. A key component of action regulation is action inhibition, the failure of which contributes to various neuropsychiatric disorders. When faced with multiple choices, dealing with conflicting information, or current actions become inappropriate or unwanted, we should be able to pause or completely abandon actions. Despite extensive efforts to understand how the brain selects, pauses, and abandons actions based on environmental demands, the mechanisms underlying these action regulation functions and, perhaps more importantly, how they inter-relate remain elusive. Part of this challenge lies in the fact that these mechanisms were rarely explored together, making it difficult to develop a unified theory that explains the computational aspects of action regulation functions. The current study introduces a large-scale model that better characterizes the computations of action regulation functions, how they are implemented within brain networks that involve frontal, motor and basal ganglia (BG) circuits, and how disruption of these circuits can lead to deficits in motor behavior seen in Parkinsons disease (PD).The model was developed by studying the motor behavior of healthy individuals and PD patients in three motor tasks that involve action inhibition. Overall, the model explains many key aspects on how the brain regulates actions that involve inhibitory processes, opening new avenues for improving and developing therapeutic interventions for diseases that may involve these circuits.

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bioRxiv2021

Preserved motor representations after paralysis

AO_SCPLOWBSTRACTC_SCPLOWNeural plasticity allows us to learn skills and incorporate new experiences. What happens when our lived experiences fundamentally change, such as after a severe injury? To address this question, we analyzed intracortical population activity in a tetraplegic adult as she controlled a virtual hand through a brain-computer interface (BCI). By attempting to move her fingers, she could accurately drive the corresponding virtual fingers. Neural activity during finger movements exhibited robust representational structure and dynamics that matched the representational structure, previously identified in able-bodied individuals. The finger representational structure was consistent during extended use, even though the structure contributed to BCI decoding errors. Our results suggest that motor representations are remarkably stable, even after complete paralysis. BCIs re-engage these preserved representations to restore lost motor functions.

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bioRxiv2020

Distinct roles of dorsal and ventral subthalamic neurons in action selection and cancellation

The subthalamic nucleus (STN) supports action selection by inhibiting all motor programs except the desired one. Recent evidence suggests that STN can also cancel an already selected action when goals change, a key aspect of cognitive control. However, there is little neurophysiological evidence for a dissociation between selecting and cancelling actions in the human STN. We recorded single neurons in the STN of humans performing a stop-signal task. Movement-related neurons suppressed their activity during successful stopping whereas stop-signal neurons activated at low-latencies regardless of behavioral outcome. In contrast, STN and motor-cortical beta-bursting occurred only later in the stopping process. Task-related neuronal properties varied by recording location from dorsolateral movement to ventromedial stop-signal tuning. Therefore, action selection and cancellation coexist in STN but are anatomically segregated. These results show that human ventromedial STN neurons carry fast stop-related signals suitable for implementing cognitive control.

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bioRxiv2020

NEURAL ENCODING OF FELT AND IMAGINED TOUCH WITHIN HUMAN POSTERIOR PARIETAL CORTEX

In the human posterior parietal cortex (PPC), single units encode high-dimensional information with partially mixed representations that enable small populations of neurons to encode many variables relevant to movement planning, execution, cognition, and perception. Here we test whether a PPC neuronal population previously demonstrated to encode visual and motor information is similarly selective in the somatosensory domain. We recorded from 1423 neurons within the PPC of a human clinical trial participant during objective touch presentation and during tactile imagery. Neurons encoded experienced touch with bilateral receptive fields, organized by body part, and covered all tested regions. Tactile imagery evoked body part specific responses that shared a neural substrate with experienced touch. Our results are the first neuron level evidence of touch encoding in human PPC and its cognitive engagement during tactile imagery which may reflect semantic processing, sensory anticipation, and imagined touch.

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