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Kahane, P.

Publications and source records attributed to Kahane, P..

At least 19 recordsLinked to original sources

Supramodal Coding of Detection and Confidence in the Human Cortex

Perception is inherently multisensory, yet it remains unclear whether the brain detects stimuli and evaluates confidence in these detections using circuits shared across senses or circuits specific to each modality. Prior evidence is limited: scalp electrophysiology cannot localize where a shared code operates, whereas functional imaging cannot resolve its timing. We recorded intracranial activity (stereoelectroencephalography) in eight patients with epilepsy who performed a near-threshold audiovisual detection task in which they reported, on each trial, whether a visual, auditory, or audiovisual stimulus was present and rated their confidence, both overall and separately for each modality. Analyzing high-gamma activity at the single-channel and region-of-interest levels, we found that stimulus detection involved a broadly distributed, largely supramodal set of cortical regions responding regardless of modality, including the insula, dorsomedial prefrontal cortex, and superior temporal gyrus. Multivariate decoders trained to distinguish detected from undetected stimuli in one modality generalized to the other modality, indicating a shared detection code within these regions. Confidence relied on a more mixed architecture but was also substantially supramodal. Finally, the auditory cortex encoded confidence information about its non-preferred (visual) modality, with a similar effect in the visual cortex for auditory confidence, suggesting that the involvement of sensory cortices in confidence is not confined to its preferred input.

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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.

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The Inferior Frontal Sulcus, not the Anterior Insula, acts as a late-stage attentional gate for the fast rejection of salient but irrelevant sensory inputs during cognitive tasks

The ability to prioritize behaviorally relevant stimuli is essential for efficient cognition, particularly in distracting environments. The anterior insula (AI), a key node of the salience network, has been proposed to support this selection process by detecting salient events. A similar function, however, has also been attributed to the inferior frontal sulcus (IFS), located at the intersection of the ventral and dorsal attention networks. Here, we directly compared the respective contributions of these regions to attentional gatekeeping--the identification and rejection of attention-grabbing yet task-irrelevant "false positives" to preserve cognitive resources. We recorded intracranial EEG from 63 patients performing an attentive reading task designed to dissociate salience from task relevance. High-Frequency Activity (HFA) responses between 50 Hz and 150 Hz revealed that the IFS was the only region--across an extensive sampling of the frontal and insular cortices--to exhibit consistent, subsecond response dynamics compatible with the filtering of false positives. In contrast, the AI did not participate in the analysis or rejection of distractors and showed only sparse, delayed responses to targets, suggesting a role in coordinating or energizing downstream cognitive or motor processes rather than in salience detection per se. These findings indicate that the IFS--rather than the AI--is the primary late-stage node for attentional gatekeeping during cognitive tasks performed under distracting conditions.

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Multiple task-demands flexibly optimize neural geometry in human ventral temporal cortex

A hallmark of human intelligence is the ability to perform multiple tasks immediately upon instruction. Yet, the neural processes that implement such flexibility remain unclear. Using intracranial electrophysiology in epilepsy patients, we examined how representational geometries evolve as participants switched among three tasks - individuation, categorization, and conceptualization - on a trial-by-trial basis. Although tasks differed in the required representational geometries, neural representations across cortex were not immediately optimized following task cues. Instead, task-tailored geometries emerged gradually over successive stimulus repetitions within a trial. Ventral temporal cortex was the only region to exhibit task-specific adjustments for all three tasks, dynamically transforming within- and between-category distances in line with task demands. Importantly, these gradual representational changes were behaviorally relevant, predicting trial-level performance. Our results show that behavioral flexibility is supported by incremental, task-dependent refinement of representational geometry already within sensory cortex - far earlier in the processing hierarchy than previously thought.

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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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Reading about sensations recruits the posterior insula: An intracranial EEG study

When we read, how do words and sentences become concepts? Contrary to theoretical perspectives that conceptualize meaning and concepts as symbolic and abstract, embodied approaches propose that semantic access relies on mental simulations of bodily experiences□associated with the word’s meaning (e.g., simulating the act of running when we read the word ‘run’ or feeling pain when reading the word ‘burn’). If this view of cognition underpins conceptual processing, then sensory and motor cortices should play an early role during language comprehension. To test this idea, we recorded intracranial neural activity (High-Frequency Activity, 50–150 Hz) in epileptic patients while they read short sentences from different semantic categories: abstract-, action- and sensation-related. We hypothesized that reading sensation-related sentences (e.g., “he burns me”) would preferentially involve the posterior insula — a key region for processing affective and painful sensations. Our results, revealed an early functional dissociation between the anterior and posterior insula: the anterior insula showed a non-selective response across all semantic categories, whereas the posterior insula presented an early selective response (around 170 ms) for the sensation-related ones. Furthermore, the intensity of this selective neural response in the posterior insula correlated with the intensity of subjective perceived pain. These results provide direct intracranial evidence suggesting that language is deeply rooted in body-environment interactions.

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A functional overlap between evidence accumulation, confidence, and changes of mind in the pre-supplementary motor area and insula

Evidence accumulation models have proven to be powerful tools to explain the temporal dynamics of decisions, as well as their metacognitive components such as confidence judgments and changes of mind. However, it is still unclear how and where in the brain evidence accumulation leads to these two metacognitive components. To better understand the functional overlap between evidence accumulation, confidence, and changes of mind, we recorded intracranial high-gamma activity in patients with focal epilepsy while they reported the motion direction of a random dot kinetogram with a computer mouse, and estimated their confidence level. We found an anatomical overlap between the neural correlates of evidence accumulation, confidence, and changes of mind in the pre-supplementary motor area, as well as in the orbitofrontal, inferior frontal, and insular cortices. Both mouse-tracking behaviour and electrophysiological results were reproduced with a post-decisional evidence accumulation model. After characterising the temporal dynamics of decision-making with mouse-tracking and intracranial electrophysiology, we conclude that confidence and changes of mind result from evidence accumulation instantiated before the decision in the pre-supplementary motor area, and after the decision in the insula.

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Cortical evidence accumulation for perceptual experience occurs irrespective of reports

Perceptual experience is a multi-faceted, dynamical process, tackled empirically through measures of stimulus detectability and confidence. To assess if stimulus detection and confidence can be explained by evidence accumulation, a form of sequential sampling of sensory evidence, we analyzed high-gamma activity from stereo-electroencephalographic data of 29 participants partaking in 3 pre-registered experiments. In an immediate-response experiment, individual channels and decoded multivariate latent variables in the visual, inferior frontal, and anterior insular cortices displayed functional markers of evidence accumulation. In two further experiments, this signal in the ventral visual cortex differentiated between (1) seen and unseen stimuli in delayed detection, (2) high and low intensity stimuli during passive viewing, and (3) levels of confidence when stimuli were seen. A computational model of leaky evidence accumulation successfully reproduced both behavioral and neural data. Overall, these results indicate that evidence accumulation explains key aspects of perceptual experience, encompassing both conscious access and monitoring.

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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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Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study

Limb movement direction can be inferred from motor cortex activity. In humans, such decoding has been predominantly demonstrated using the spectral power of electrophysiological signals recorded in sensorimotor areas during movement execution. Yet, it remains unclear to what extent intended hand movement direction can be predicted from brain signals recorded during movement planning. Furthermore, whether other oscillatory features beyond power are also involved in direction encoding is not fully understood. Here, we set out to probe the directional-tuning of oscillatory phase, amplitude and Phase-Amplitude Coupling (PAC) during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. To this end, we recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. We found that LFP power significantly predicted hand-movement direction at execution but also during planning. While successful classification during planning primarily involved low-frequency power in a fronto-parietal circuit, decoding during execution was largely mediated by higher frequency activity in motor and premotor areas. Interestingly, LFP phase at very low frequencies (<1.5 Hz) led to significant decoding in premotor brain regions during execution. The machine learning framework also showed PAC to be uniformly modulated across directions through the task. Cross-temporal generalization analyses revealed that several stable brain patterns in prefrontal and premotor brain regions encode directions across both planning and execution. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%) during both planning and execution. The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

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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.

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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.

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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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THE DUAL-TASK COST IS DUE TO NEURAL INTERFERENCES DISRUPTING THE OPTIMAL SPATIO-TEMPORAL DYNAMICS OF THE COMPETING TASKS

Dual-tasking is extremely prominent nowadays, despite ample evidence that it comes with a performance cost: the Dual-Task (DT) cost. Neuroimaging studies have established that tasks are more likely to interfere if they rely on common brain regions, but the precise neural origin of the DT cost has proven elusive so far, mostly because fMRI does not record neural activity directly and cannot reveal the key effect of timing, and how the spatio-temporal neural dynamics of the tasks coincide. Recently, DT electrophysiological studies in monkeys have recorded neural populations shared by the two tasks with millisecond precision to provide a much finer understanding of the origin of the DT cost. We used a similar approach in humans, with intracranial EEG, to assess the neural origin of the DT cost in a particularly challenging naturalistic paradigm which required accurate motor responses to frequent visual stimuli (task T1) and the retrieval of information from long-term memory (task T2), as when answering passengers questions while driving. We found that T2 elicited neuroelectric interferences in the gamma-band (>40 Hz), in key regions of the T1 network including the Multiple Demand Network. They reproduced the effect of disruptive electrocortical stimulations to create a situation of dynamical incompatibility, which might explain the DT cost. Yet, participants were able to flexibly adapt their strategy to minimize interference, and most surprisingly, reduce the reliance of T1 on key regions of the executive control network - the anterior insula and the dorsal anterior cingulate cortex - with no performance decrement. HIGHLIGHTS- First direct evidence in humans of neural interferences between two tasks. - First explanation of the Dual-Task cost at the neural level in humans. - First Dual-Tasking study with intracranial EEG in naturalistic conditions.

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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.

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Interactive mapping of language and memory with the GE2REC protocol

Previous studies have highlighted the importance of considering cognitive functions in a dynamic and interactive perspective and multiple evidence was brought for a language and memory interaction. In this study performed in healthy participants, we developed a new protocol entitled GE2REC that interactively accesses the neural representation of language-and-memory network. This protocol consists of three runs related to each other, providing a link between tasks, in order to assure an interactive measure of linguistic and episodic memory processes. GE2REC consists of a sentence generation (GE) in auditory modality and two recollecting (2REC) memory tasks, one recognition performed in visual modality, and another one recall performed in auditory modality. Its efficiency was evaluated in 20 healthy volunteers using a 3T MR imager. Our results corroborate the ability of GE2REC to robustly activate a fronto-temporo-parietal language network as well as temporal mesial, prefrontal and parietal cortices during encoding and recognition. GE2REC is useful because: (a) requires simultaneous and interactive language-and-memory processes and jointly maps their neural basis; (b) explores encoding and retrieval, managing to elicit activation of mesial temporal structures; (c) is short and easy to perform, hence being suitable for more restrictive settings, and (d) has an ecological dimension of tasks and stimuli. Overall, GE2REC can provide valuable information in terms of the practical foundation of exploration language and memory interconnection.

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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

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Acoustic contamination of electrophysiological brain signals during speech production and sound perception

A current challenge of neurotechnologies is the development of speech brain-computer interfaces to restore communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band (between 70 and 200 Hz), a range that happens to overlap the fundamental frequency of speech. Here, we analyzed human electrocorticographic (ECoG) and intracortical recordings during speech production and perception as well as rat microelectrocorticographic ({micro}-ECoG) recordings during sound perception. We observed that electrophysiological signals, recorded with different recording setups, often contain spectrotemporal features highly correlated with those of the sound, especially within the high-gamma band. The characteristics of these correlated spectrotemporal features support a contamination of electrophysiological recordings by sound. In a recording showing high contamination, using neural features within the high-gamma frequency band dramatically increased the performance of linear decoding of acoustic speech features, while such improvement was very limited for another recording showing weak contamination. Further analysis and in vitro replication suggest that the contamination is caused by a mechanical action of the sound waves onto the cables and connectors along the recording chain, transforming sound vibrations into an undesired electrical noise that contaminates the biopotential measurements. This study does not question the existence of relevant physiological neural information underlying speech production or sound perception in the high-gamma frequency band, but alerts on the fact that care should be taken to evaluate and eliminate any possible acoustic contamination of neural signals in order to investigate the cortical dynamics of these processes.

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