Biology subjects

Bastin, J.

Publications and source records attributed to Bastin, J..

11 recordsLinked to original sources

bioRxiv2025

Multi-task Intracranial EEG recordings reveal a Comprehensive Role of the Human Dorsal Anterior Insula in High-Level Cognition

The dorsal anterior insula (dAI) is widely recognized as a cornerstone of human cognition. However, its functional characterization remains the subject of considerable debate and competing theories. Embracing a "shift in viewpoint" approach--defining a brain regions function through the observation of the same neural population across diverse experimental contexts--this study used intracranial EEG (iEEG) recordings from a large cohort of patients to investigate the sub-second neural response dynamics of the human dAI across a broad range of cognitive tasks, spanning visual category discrimination, phonological and semantic processing, attentive visual search, and verbal and visuospatial working memory. This within-subject, multi-task iEEG framework offered a robust basis for decoding the structure-function relationship of the dAI and for empirically testing key predictions of prevailing theoretical models concerning its role. Our findings confirmed the dAIs involvement in the Action-Mode Network and shed new light on the large-scale spatiotemporal organization of that network, while providing novel insights into the interplay between cognitive effort and efficiency.

neuroscience↗

bioRxiv2025

Choosing the best or avoiding the worst: complementary and opponent value signals in the human brain

Whether the brain encodes the subjective value of pleasant and unpleasant situations via a single integrated circuit or through distinct pathways remains unresolved. It is also unclear how neural value signals guide choices when the context involves choosing the most favorable option or avoiding the most unfavorable one. To address these questions, we recorded intracerebral activity from 27 participants as they first rated pleasant and unpleasant scenarios and then made binary choices across all combinations of item domain (pleasant/unpleasant) and choice frame (choose best/worst). Ventromedial prefrontal cortex (vmPFC) broadband gamma activity (BGA, 50-150 Hz) tracked the value of pleasant items during rating and pre-choice, correlating positively with the most pleasant item irrespective of choice frame. Similarly, anterior insula (aIns) BGA tracked the value of unpleasant items during rating and pre-choice, correlating negatively with the most unpleasant item irrespective of choice frame. The difference in relative value correlations between the vmPFC and aIns was larger for choices involving a conflict between item domain and choice frame, consistent with slower response times and choices less aligned with value differences in these trials. These findings reveal dissociable yet complementary neural systems for pleasant and unpleasant valuation and highlight how such signals may guide flexible decision-making across contexts.

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bioRxiv2024

Subthalamic nucleus activity modifications prior to clinical impairment in a progressive model of Parkinson's disease

Parkinsons disease (PD) is diagnosed after motor symptoms appear, although non-motor symptoms emerge years earlier. Following years of pharmacological treatment, high-frequency stimulation (HFS) of the subthalamic nucleus (STN), a key hub in goal-direct behaviors, can be proposed. While HFS-STN reliably improves motor symptoms, it does not specifically address non-motor symptoms. Clarifying how STN dysfunction contributes to non-motor symptoms could thus improve STN stimulation strategies. Here, we longitudinally recorded STN local field potentials in two macaques performing a counter-demanding task during chronic low-dose MPTP treatment. This progressive model, evolving from an asymptomatic stage to motivational, cognitive and ultimately motor deficits, enabled detailed examination of non-motor stages preceding motor impairment. Each stage was associated with distinct STN electrophysiological alterations, including early loss of reward-related theta activity, subsequent disappearance of decision-related theta oscillations, and later reduction of movement-related beta rebound. In the stable parkinsonian stage, stimulation of different STN territories provided complementary behavioral effect: dorsal HFS-STN improved motor performances, whereas ventral low-frequency stimulation alleviated motivational deficits. These findings reveal a temporal link between STN dysfunction and symptom onset, and suggest site and frequency-specific stimulation as a strategy to address both motor and non-motor symptoms in PD.

neuroscience↗

bioRxiv2023

Too much or not enough? Optimal level of human intracranial theta activity for rule-switching in the subthalamo-medio-prefrontal circuit

The ability to strategically switch between rules associating stimuli and responses as a function of changing environmental demands critically depend on a neural circuit including the dorsomedial prefrontal cortex (dmPFC) and the basal ganglia. However, the precise neural implementations of rule switching remain unclear. To address this issue, we recorded local field potentials from two groups of rare patients performing a rule-switching paradigm: (1) deep brain recordings of the subthalamic nucleus (STN) in patients with obsessive-compulsive disorder, and (2) stereo-electroencephalogram from dmPFC of drug-resistant epileptic patients. We fitted a hierarchical drift-diffusion model (HDDM) to patients choice behavior and found that rule-switching was associated with a shift in the starting point of evidence accumulation (z), effectively disentangling rule switches from the selection of a new response. At the neural level, we found that theta band (5-10 Hz) activity increased in dmPFC and STN during switch compared to non-switch trials, while temporally delayed and excessive levels of theta activity led to premature switch errors. This seemingly opposing impact of increased theta rhythms in successful and unsuccessful switching could be explained mechanistically using a neural HDDM, as trial-by-trial fluctuations in theta power negatively correlated with the subjects starting point parameter. Together, these results shed a new light on the neural mechanisms underlying the rapid reconfiguration of stimulus-response associations, revealing a Goldilocks effect of theta band activity on rule switching behavior.

neuroscience↗

bioRxiv2023

Direct stimulation of anterior insula and ventromedial prefrontal cortex disrupts economic choices

Neural activities within the ventromedial prefrontal cortex and anterior insula are associated with economic choices. However, whether these brain regions are causally related to these processes remains unclear. To address this issue, we leveraged rare intracerebral electrical stimulation (iES) data in epileptic patients. We show that opposite effects of iES on choice depend on the location of stimulation on a dorso-ventral axis within each area, thus demonstrating dissociable neural circuits causally involved in accepting versus avoiding challenges.

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bioRxiv2023

Neural interactions in the human frontal cortex dissociate reward and punishment learning

How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.

neuroscience↗

bioRxiv2022

The role of the thalamus in human reinforcement learning

Although the thalamus is supposed to be involved in reinforcement-based decision-making, there is no direct evidence regarding the involvement of this subcortical structure in humans. To fill this gap, we leveraged rare intra-thalamic electrophysiological recordings in patients and found that temporally structured thalamic oscillations encode key learning signals. Our findings also provide neural insight into the computational mechanisms of action inhibition in punishment avoidance learning.

neuroscience↗

bioRxiv2021

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites1 that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY HighlightsO_LIGroup-level statistics for extracting neurophysiological cognitive brain networks C_LIO_LICombining non-parametric permutations with measures of information C_LIO_LIFixed- and random-effect models, test- and cluster-wise corrections C_LIO_LIMulti-level inferences, from local regions to inter-areal functional connectivity C_LIO_LIA Python open-source toolbox called Frites includes the proposed statistical methods C_LI

neuroscience↗

bioRxiv2021

Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice

Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of participants (n = 30), while they were performing interleaved quiz and choice tasks. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that 1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, 2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain versus loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the comparison of safe and risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.

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bioRxiv2020

Opponent intracerebral signals for reward and punishment prediction errors in humans

Whether maximizing rewards and minimizing punishments rely on distinct brain systems remains debated, inconsistent results coming from human neuroimaging and animal electrophysiology studies. Bridging the gap across species and techniques, we recorded intracerebral activity from twenty patients with epilepsy while they performed an instrumental learning task. We found that both reward and punishment prediction errors (PE), estimated from computational modeling of choice behavior, correlated positively with broadband gamma activity (BGA) in several brain regions. In all cases, BGA increased with both outcome (reward or punishment versus nothing) and surprise (how unexpected the outcome is). However, some regions (such as the ventromedial prefrontal and lateral orbitofrontal cortex) were more sensitive to reward PE, whereas others (such as the anterior insula and dorsolateral prefrontal cortex) were more sensitive to punishment PE. Thus, opponent systems in the human brain might mediate the repetition of rewarded choices and the avoidance of punished choices.

neuroscience↗

bioRxiv2019

Decoding the neural dynamics of oculomotor decision making in humans

How do we choose a particular action among equally valid alternatives? Non-human primate findings have shown that decision-making implicates modulations in unit firing rates and local field potentials (LFPs) across frontal and parietal cortices. Yet the electrophysiological brain mechanisms that underlie free choice in humans remain ill defined. Here, we address this question using rare intracerebral EEG recordings in surgical epilepsy patients performing a delayed oculomotor decision task. We find that the temporal dynamics of high gamma (HG, 60-140 Hz) neural activity in distinct frontal and parietal brain areas robustly discriminate free choice from instructed saccade planning at the level of single trials. Classification analysis was applied to the LFP signals to isolate decision-related activity from sensory and motor planning processes. Compared to instructed saccades, free choice trials exhibited delayed and longer-lasting HG activity. The temporal dynamics of these sustained decision-related responses distinguished deliberation-related from working memory processes. Taken together, these findings provide the first direct electrophysiological evidence in humans for the role of sustained high-frequency neural activation in fronto-parietal cortex in mediating the intrinsically driven process of freely choosing among competing behavioral alternatives. HighlightsO_LIFirst intracerebral recordings in humans performing an oculomotor decision-making task C_LIO_LIMachine learning analytics unravel underlying spectral and temporal brain dynamics C_LIO_LIFree choice trials exhibit sustained fronto-parietal high gamma (HG) activity during the delay C_LIO_LIMaking a decision and maintaining it in working memory are associated with distinct sustained HG dynamics C_LI

neuroscience↗