Biology subjects

Bari, A.

Publications and source records attributed to Bari, A..

10 recordsLinked to original sources

bioRxiv2026

A Unified World Model in Human Posterior Parietal Cortex

How does the brain understand the physical world, predict future events, and plan actions? Are these problems solved by a unified world model, as hypothesized by cognitive scientists and artificial intelligence researchers? Posterior parietal cortex has been implicated in all these functions, but whether the same neurons support each of them is unknown. We recorded from the superior parietal lobule (SPL) of two tetraplegic participants implanted with microelectrode arrays. SPL units preferentially engaged by intuitive physics, judging block-tower stability and viewing naturalistic physical events, also predicted whether moving objects would collide hundreds of milliseconds before contact, generalizing between different physical scenarios. These populations also encoded which hand action the participant intended to perform. These findings identify SPL as a substrate for physical world modeling.

neuroscience↗

bioRxiv2026

The Compositional Encoding of Hand-Eye Coordinated Movements for Single Neurons in the Posterior Parietal Cortex

Human posterior parietal cortex (PPC) is thought to play an important role in hand-eye coordination, yet the underlying encoding mechanisms remain uncertain. We recorded 412 single neurons across 11 sessions from motor cortex (MC; n=251) and PPC (n=161) in a single human participant performing a hand-eye (H-E) coordinated center-out task. While MC neurons showed little to no modulation by eye movements, 79% of PPC neurons had neural representations that were additively separable into independent hand- and eye-movement tuning curves. Due to this separability, neural representations could be separated and additively recomposed while maintaining structure similarity. Consequently, compositional decoders trained solely on single-effector movements could match the performance of decoders trained on coordinated H-E movements (hand: 66% vs 69%; eye: 34% vs 36%). These results show that, during simple center-out tasks, MC hand movement codes are unaffected by eye movements and that compositionality can be used to modularly decode H-E coordinated movements in PPC.

neuroscience↗

bioRxiv2026

Depressive status modulates hippocampal-cortical dynamics during acute nicotine use

Nicotine use disorder shows heterogeneity in treatment response, potentially reflecting differences in underlying neural circuitry, particularly in the presence of depression. We examined real-time neural dynamics during nicotine inhalation in two chronic users - one with depression and one without - using simultaneous hippocampal recordings from responsive neurostimulation (RNS) electrodes and scalp EEG. Oscillatory activity and hippocampal-cortical connectivity were analyzed in relation to mood and craving. Oscillatory activity tracked mood in the non-depressed individual but was attenuated or reversed in the depressed individual, suggesting reduced reward-related neural responsiveness. In contrast, both participants showed reduced alpha hippocampal-cortical connectivity following nicotine use, suggesting a shift from reward-seeking to reward and relief processing. These findings support a network-based framework of nicotine-driven neural dynamics and provide preliminary evidence that depressive status may modulate these processes. Although limited to two cases, this work highlights the potential for identifying neurophysiological subtypes of nicotine users and informs future efforts toward personalized treatment approaches.

neuroscience↗

bioRxiv2025

Hierarchical and Context-Dependent Encoding of Actions in Human Posterior Parietal and Motor Cortex

Action understanding requires internal models that link vision to motor goals. In monkeys, mirror neurons demonstrate motor resonance during observation, but single-unit evidence in humans is limited, leaving open whether such representations rely solely on motor resonance. We recorded neural activity from motor cortex (MC) and superior parietal lobule (SPL) in two tetraplegic participants implanted with Utah arrays while they intended or observed hand actions. MC strongly encoded intention but showed only weak, feature-specific overlap during observation, evident primarily at the population level. SPL, in contrast, supported shared models across intended movement and observation formats at both single-unit and population levels. In variants with incongruent instructed and observed actions, SPL encoded observed actions only when behaviorally relevant, whereas MC remained intention-dominant. Our results identify a context-dependent gating mechanism in SPL and suggest a hierarchical organization in which MC maintains intention-specific codes while SPL flexibly links observed input with internal goals to support action understanding.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

Multiregional Representations of Intertemporal Decision Making in Human Single Neurons

Characterization of the neural basis of delay discounting provides insight into the origin of impulsive decision-making, which underlies several psychiatric diseases including substance use disorder. Here, we identify human single unit representations of decisions, and their level of difficulty, in the orbitofrontal cortex, hippocampus and amygdala that are related to preferences for immediate vs. delayed rewards. Results provide an initial account of multiregional intracranial computations related to impulsive behaviors in humans.

neuroscience↗

bioRxiv2024

Spontaneous pain dynamics characterized by stochasticity in awake human LFP with chronic pain

Chronic pain involves persistent fluctuations lasting seconds to minutes, yet there are limited studies on spontaneous pain fluctuations utilizing high-temporal-resolution electrophysiological signals in humans. This study addresses the gap, capturing data during awake deep brain stimulation (DBS) surgery in five chronic pain patients. Patients continuously reported pain levels using the visual analog scale (VAS), and local field potentials (LFP) from key pain-processing structures (ventral parietal medial of the thalamus, VPM; subgenual cingulate cortex, SCC; periaqueductal gray, PVG) were recorded. Our novel AMI analysis revealed that regular spike-like events in the theta/alpha band was associated with higher pain; and regular events in the gamma band was associated with opioid effects. We demonstrate a novel methodology that successfully characterizes spontaneous pain dynamics with human electrophysiological signals, holding potential for advancing closed-loop DBS treatments for chronic pain.

neuroscience↗

bioRxiv2023

Common roles for serotonin in rats and humans for computations underlying flexible decision-making

Serotonin is critical for adapting behavior flexibly to meet changing environmental demands. Cognitive flexibility is important both for successful attainment of goals, as well as for social interactions, and is frequently impaired in neuropsychiatric disorders, including obsessive-compulsive disorder (OCD). However, a unifying mechanistic framework accounting for the role of serotonin in behavioral flexibility has remained elusive. Here, we demonstrate common effects of manipulating serotonin function across two species (rats and humans) on latent processes supporting choice behavior during probabilistic reversal learning using computational modelling. The findings support a role of serotonin in behavioral flexibility and plasticity, indicated, respectively, by increases or decreases in choice repetition ( stickiness) or reinforcement learning rates depending upon manipulations intended to increase or decrease serotonin function. More specifically, the rate at which expected value increased following reward and decreased following punishment (reward and punishment learning rates) was greatest after sub-chronic administration of the selective serotonin reuptake (SSRI) citalopram (5 mg/kg for 7 days followed by 10 mg/kg twice a day for 5 days) in rats. Conversely, humans given a single dose of an SSRI (20mg escitalopram), which can decrease post-synaptic serotonin signalling, and rats that received the neurotoxin 5,7-dihydroxytryptamine (5,7-DHT), which destroys forebrain serotonergic neurons, exhibited decreased reward learning rates. A basic perseverative tendency ( stickiness), or choice repetition irrespective of the outcome produced, was likewise increased in rats after the 12-day SSRI regimen and decreased after single dose SSRI in humans and 5,7-DHT in rats. These common effects of serotonergic manipulations on rats and humans - identified via computational modelling - suggest an evolutionarily conserved role for serotonin in plasticity and behavioral flexibility and have clinical relevance transdiagnostically for neuropsychiatric disorders.

neuroscience↗

bioRxiv2022

A wearable platform for closed-loop stimulation and recording of single- neuron and local field potential activity in freely-moving humans

Advances in technologies that can record and stimulate deep-brain activity in humans have led to impactful discoveries within the field of neuroscience and contributed to the development of novel therapies for neurological and psychiatric disorders. Further progress, however, has been hindered by device limitations in that recording of single-neuron activity during freely-moving behaviors in humans has not been possible. Additionally, implantable neurostimulation devices, currently approved for human use, have limited stimulation programmability and lack full-duplex bi-directional capability. Here, we developed a wearable bi-directional closed-loop neuromodulation system (Neuro-stack) and used it to record single-neuron and local field potential activity during stationary and ambulatory behavior in humans. Together with a highly flexible and customizable stimulation capability, the Neuro-stack provides an opportunity to investigate the neurophysiological basis of disease, develop improved responsive neuromodulation therapies, explore brain function during naturalistic behaviors in humans, and consequently, bridge decades of neuroscientific findings across species.

neuroscience↗

bioRxiv2021

Machine Learning Speeding Up the Development of Portfolio of New Crop Varieties to Adapt to and Mitigate Climate Change

Climate change poses serious challenges to achieving food security in a time of a need to produce more food to keep up with the worlds increasing demand for food. There is an urgent need to speed up the development of new high yielding varieties with traits of adaptation and mitigation to climate change. Mathematical approaches, including ML approaches, have been used to search for such traits, leading to unprecedented results as some of the traits, including heat traits that have been long sought-for, have been found within a short period of time.

plant biology↗