Philippe Kahane

Whether the brain encodes the subjective value of pleasant and unpleasant situations via a single integrated circuit or through distinct pathways remains unresolved. How such signals guide choice across contexts also remains unclear. To address these questions, we recorded intracerebral activity from participants who rated pleasant and unpleasant scenarios and then made binary choices across domains and frames. Ventromedial prefrontal cortex (vmPFC) broadband gamma activity (BGA, 50–150 Hz) tracked the value of pleasant items during rating and 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 choice, correlating negatively with the most unpleasant item irrespective of choice frame. Relative value signals in the vmPFC and aIns diverged most strongly when item domain and choice frame conflicted. These findings reveal complementary neural systems for pleasant and unpleasant valuation and their role in value-based choices.

The decision-making field has long attempted to understand the origins of human choice variability. A potential source of variability lies in spontaneous fluctuations of ongoing neural activity, which may influence behavior across several cognitive domains. Here, we developed a closed-loop intracranial brain-computer interface that detects transient fluctuations of broadband gamma activity (BGA, 70-150 Hz) to trigger stimulus presentations contingent on high or low neural states. Using this approach, we examined how spontaneous activity in the anterior insula influences accept/reject decisions involving multi-attribute offers combining pleasant and unpleasant components. Offers preceded by high BGA in the anterior insula were associated with a transient post-offer suppression in anterior insula activity, which biased decisions toward accepting an unpleasant item in exchange for a pleasant one in hypothetical multi-attribute choices. These findings demonstrate that sub-second endogenous neural fluctuations directly modulate choice behavior, challenging current neuro-computational models by emphasizing the critical role of intrinsic brain states in shaping decision variability.

High‐frequency oscillations (HFOs) were discovered more than 20 years ago, and since then they have been studied intensively in the context of epilepsy. HFOs encompass a broad spectrum of oscillations, typically ranging from 80 Hz to several kHz, that include both normal and pathological oscillations, documented in people with epilepsy and animal models. HFOs have drawn considerable attention due to their prominent roles in epileptogenesis, ictogenesis, and functional organization of epileptic tissue. We provide historical background on HFOs in epilepsy and summarize the current state of knowledge, synthesizing clinical and basic science content from the Third International Workshop on HFOs in Epilepsy. Over the years, the field has evolved from single‐center analysis of HFOs on invasive electroencephalographic recordings to recent multicenter studies and meta‐analysis, which have tempered the conviction or hope that HFOs are uniform, “one event fits all,” stand‐alone biomarkers. Instead, association of HFOs with other electrophysiological phenomena such as interictal spikes, seizures, and signal features like entropy have highlighted new ways to identify epileptogenic tissue. Advances in recording and analytical tools have significantly expanded their potential applications in both clinical and basic science settings. Several recent publications focus on how scalp HFOs can illuminate disease propensity, severity, and therapy responses. Moreover, it was recently discovered that HFOs are also present in experimental models of Alzheimer's disease, and research is ongoing regarding their relations to the HFOs found in epilepsy. Together, these developments highlight that HFOs represent an evolving research area, with significant inroads made over the years. Yet, key gaps in knowledge remain, and we propose five benchmark areas that warrant future research and advancement.

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.

The decision-making field has long attempted to understand the origins of human choice variability. A potential source of variability lies in the ongoing fluctuations of brain oscillations, which might influence behavior across several cognitive domains. Here, we developed a closed-loop intracranial brain-computer interface that detects transient fluctuations of broadband gamma activity (BGA, 70–150 Hz) to trigger stimulus presentations contingent on high or low neural states. Using this approach, we examined how spontaneous activity in the anterior insula influences accept/reject decisions involving multi-attribute offers combining pleasant and unpleasant components. Offers preceded by high pre-offer BGA in the anterior insula were associated with a transient post-offer suppression in aIns activity, which biased decisions toward accepting an unpleasant item in exchange for a pleasant one in hypothetical multi-attribute choices. These findings demonstrate that sub-second endogenous neural fluctuations directly modulate choice behavior, challenging current neuro-computational models by emphasizing the critical role of intrinsic brain states in decision variability.

Zusammenfassung: Die Internationale Liga gegen Epilepsie (ILAE) hat die operationale Klassifikation epileptischer Anfälle 2017 auf der Grundlage der damals entwickelten Rahmenbedingungen aktualisiert. In diese Überarbeitung wurden die publizierten Erfahrungen mit der Umsetzung der 2017er-Klassifikation einbezogen. Eine 37 Personen zählende Arbeitsgruppe wurde vom ILAE-Exekutivausschuss eingesetzt. Die internationalen Expertinnen und Experten aus allen ILAE-Regionen wandten ein modifiziertes Delphi-Verfahren an, bei dem für jeden Vorschlag ein Konsens von mehr als zwei Dritteln erforderlich war. Nach Veröffentlichung auf der ILAE-Homepage mit dem Ersuchen, Kommentare zu dem Entwurf einzugeben, ernannte der Exekutivausschuss 7 zusätzliche Sachverständige für die Arbeitsgruppe zur Überarbeitung des Positionspapieres, um die eingegangenen Kommentare zu diskutieren und gegebenenfalls einzubeziehen. Die aktualisierte Klassifikation behält die Hauptklassen von Anfällen bei: fokal, generalisiert, unbekannt (ob fokal oder generalisiert) und nicht klassifiziert. Taxonomische Regeln unterscheiden zwischen Klassifikatoren , die biologische Klassen widerspiegeln und sich direkt auf die klinische Behandlung auswirken, und Deskriptoren , die andere wichtige Anfallsmerkmale angeben. Fokale Anfälle und Anfälle unbekannten Ursprungs werden darüber hinaus nach dem Bewusstseinszustand des Patienten während des Anfalls klassifiziert, je nachdem ob eine Bewusstseinsstörung vorliegt oder nicht. Die Bewertung, ob eine Bewusstseinsstörung vorliegt, wird durch Gewahrsein (engl. „awareness“) und Reaktionsfähigkeit (engl. „responsiveness“) während eines Anfalls und erhaltene Erinnerung (engl. „recall“) nach einem Anfall klinisch operationalisiert. Wenn der Bewusstseinszustand nicht bestimmbar ist, wird der Anfall unter dem übergeordneten Begriff, d. h. der Hauptanfallsklasse (fokaler Anfall oder Anfall unbekannten Ursprungs) klassifiziert. Generalisierte Anfälle werden in Absencen, generalisierte tonisch-klonische Anfälle und andere generalisierte Anfälle eingeteilt, wobei jetzt auch der negative Myoklonus als Anfallstyp beschrieben wird. Anfälle werden in der Grundversion als solche mit oder ohne beobachtbare Manifestationen beschrieben, während eine erweiterte Version die chronologische Abfolge der Anfallssemiologie verwendet. Diese aktualisierte Klassifikation umfasst 4 Hauptklassen und nur noch 21 Anfallstypen (und nicht mehr 63 wie in der 2017 Klassifikation; Anm. d. Übersetzer). Besonderer Wert wurde auf die Übersetzbarkeit in andere Sprachen jenseits von Englisch gelegt. Ziel ist es, eine gemeinsame Sprache für alle im Bereich Epilepsie tätigen Gesundheitsfachkräfte zu schaffen – von ressourcenarmen Gebieten bis hin zu hoch spezialisierten Zentren – und leicht zugängliche Begriffe für Patientinnen und Patienten sowie Betreuungspersonen bereitzustellen.

Perception is a multi-faceted, dynamical process that can be tackled empirically through measures of stimulus detectability and confidence. We recorded stereo-electroencephalographic data of 29 participants partaking in three pre-registered experiments to assess if evidence accumulation, a form of sequential sampling of sensory evidence, can explain perception. In an immediate-response experiment, high-gamma activity from individual channels and decoded multivariate latent variables in the visual, inferior frontal, and anterior insular cortices display a correlation between the slope of their increase and reaction times. In two further experiments, this signal in the ventral visual cortex differentiates between (1) stimuli reported as seen vs. unseen in delayed detection, (2) high and low intensity stimuli during passive viewing, and (3) levels of confidence when stimuli were detected. A computational model of leaky evidence accumulation can successfully reproduce both behavioral and neural data. Overall, we show that evidence accumulation explains subjective aspects of visual perception.

Evidence accumulation is a powerful mechanism 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. We report intracranial high-gamma activity in patients with epilepsy recorded while they perform a visual discrimination task and estimate their confidence level. Our results indicate 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. Behavioural and electrophysiological results are reproduced with a post-decisional evidence accumulation model, and the temporal dynamics of decision-making is characterized 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.