Adaptive behavior relies on the ability to translate abstract rules and goals into actions suited to the current context. Neural population activity in the prefrontal cortex (PFC) has been proposed to support such flexible computations through high-dimensional dynamics, whereas activity in the primary motor cortex (M1) is related more directly to movement execution. How contextual representations in PFC are transformed into ensuing action plans within M1 remains unknown. Previous work suggests that low-dimensional coding subspaces might organize interareal communication, but direct evidence for such population-level communication mechanisms in humans is lacking. Here we use intracranial recordings from human PFC and M1 to identify a communication subspace embedded within high-dimensional PFC activity, that selectively relays behaviorally relevant information at the single-trial level. Activity in this subspace predicts context-dependent action more strongly than either region, revealing a fundamental coding principle by which coordinated interareal population dynamics filter and relay predictive information to guide context-dependent actions.

Objective. Depressive symptoms are common in individuals with temporal lobe epilepsy (TLE), yet the network dynamics linking limbic circuitry to mood disturbance in TLE remain poorly understood. Here, we investigated whether the temporal organization of amygdala-hippocampal beta synchrony reflects depressive symptom burden in individuals with TLE undergoing intracranial EEG (iEEG) monitoring.Approach. We analyzed iEEG recordings from 14 adults with TLE who underwent intracranial monitoring as part of routine pre-surgical evaluation. Dynamic functional connectivity (dFC) in the beta band was calculated between the amygdala and the hippocampus, and the temporal structure of the dFC was quantified using detrended fluctuation analysis. Random permutation of epochs was performed to assess the influence of interictal epileptiform discharges (IEDs) on the network dynamics. We additionally extracted the ultra-slow fluctuations in dFC and assessed whether the timing of the IEDs was phase-locked to these ultra-slow rhythms.Main results. TLE patients with depression exhibited higherαthan non-depressed TLE subjects (P= 0.007), indicating greater temporal persistence of limbic beta synchrony. Across individuals,αwas positively associated with depressive symptom severity (r= 0.73,P= 0.003). Disrupting the temporal alignment of IED-containing epochs reducedα, whereas permuting IED-containing epochs among themselves preservedα, suggesting that the timing of IEDs contributes to the observed long-range temporal structure. In depressed TLE subjects, IEDs preferentially occurred during the rising phase of ultra-slow dFC fluctuations (P= 0.004).Significance. Depressive symptoms in TLE are associated with more temporally persistent, less flexible beta band limbic network dynamics. These findings highlight the temporal organization of amygdala-hippocampal synchrony as a potential network-level signature of affective dysfunction within epilepsy and may inform future approaches to monitoring and modulating mood-related circuit activity in this population.

Singing and speaking often dissociate clinically-people who stutter can sing fluently, and individuals with aphasia and speech output problems from stroke may express sentences fluently in song-yet the neural mechanisms of this centuries-old clinical phenomenon remain unclear. We recorded intracranial EEG while neurosurgical patients produced matched sentences by singing or speaking, sampling millimeter- and millisecond-scale activity across bilateral sensorimotor cortex (SMC). During articulation, high-frequency activity (70-150 Hz) lateralized oppositely across behaviors, with right-dominant SMC activation for singing and left-dominant activation for speaking. Mu-band (∼10 Hz) synchrony further differentiated the dynamics: speaking showed an early left-led pattern, whereas singing exhibited a ramping of synchrony within the left sensorimotor cortex and between the two motor cortices, supporting progressive interhemispheric recruitment. Frequency-domain Granger-Geweke causality revealed that the left primary somatosensory cortex drives both motor cortices at speech onset. In contrast in singing, control over motor cortices relied on both hemispheres. These results provide evidence that song and speech rely on dissociable sensorimotor dynamics. This bilaterally coordinated SMC pathway may enable preserved singing in individuals with impaired speech output.

Abstract Choosing to approach or avoid is common in everyday life and excessive avoidance is a cardinal feature of anxiety disorders. We use intracranial EEG to define a prefrontal-limbic circuit supporting approach and avoidance. Presurgical epilepsy patients (n = 20) performed an approach-avoidance conflict decision-making task inspired by the arcade game Pac-Man, where patients trade off rewards against losses from ghost attack. During approach, theta power increases across a limbic circuit including the hippocampus, amygdala, orbitofrontal cortex and anterior cingulate cortex, which drops during avoidance. Theta connectivity between this circuit and lateral prefrontal cortex increases during approach and falls during avoidance. Network connectivity tracks how long patients approach, with enhanced synchronicity extending approach times. During imminent threat, the system switches to sustained increase in high-frequency activity in the lateral prefrontal cortex. The results provide evidence of a distributed prefrontal-limbic circuit, mediated by theta oscillations and high frequency activity, underlying approach-avoidance conflict in humans.

BackgroundFor patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive presurgical approaches such as seizure semiology interpretation, electroencephalography (EEG), magnetic resonance imaging (MRI), and intracranial EEG (iEEG). However, interpreting seizure semiology is challenging because it heavily relies on expert knowledge. The semiologies are often inconsistent and incoherent, leading to variability and potential limitations in presurgical evaluation. To overcome these challenges, advanced technologies like large language models (LLMs)—with ChatGPT being a notable example—offer valuable tools for analyzing complex textual information, making them well-suited to interpret detailed seizure semiology descriptions and accurately localize the EZ. ObjectiveThis study evaluates the clinical value of ChatGPT for interpreting seizure semiology to localize EZs in presurgical assessments for patients with focal epilepsy and compares its performance with that of epileptologists. MethodsWe compiled 2 data cohorts: a publicly sourced cohort of 852 semiology-EZ pairs from 193 peer-reviewed journal publications and a private cohort of 184 semiology-EZ pairs collected from Far Eastern Memorial Hospital (FEMH) in Taiwan. ChatGPT was evaluated to predict the most likely EZ locations using 2 prompt methods: zero-shot prompting (ZSP) and few-shot prompting (FSP). To compare the performance of ChatGPT, 8 epileptologists were recruited to participate in an online survey to interpret 100 randomly selected semiology records. The responses from ChatGPT and epileptologists were compared using 3 metrics: regional sensitivity (RSens), weighted sensitivity (WSens), and net positive inference rate (NPIR). ResultsIn the publicly sourced cohort, ChatGPT demonstrated high RSens reliability, achieving 80% to 90% for the frontal and temporal lobes; 20% to 40% for the parietal lobe, occipital lobe, and insular cortex; and only 3% for the cingulate cortex. The WSens, which accounts for biased data distribution, consistently exceeded 67%, while the mean NPIR remained around 0. These evaluation results based on the private FEMH cohort are consistent with those from the publicly sourced cohort. A group t test with 1000 bootstrap samples revealed that ChatGPT-4 significantly outperformed epileptologists in RSens for the most frequently implicated EZs, such as the frontal and temporal lobes (P<.001). Additionally, ChatGPT-4 demonstrated superior overall performance in WSens (P<.001). However, no significant differences were observed between ChatGPT and the epileptologists in NPIR, highlighting comparable performance in this metric. ConclusionsChatGPT demonstrated clinical value as a tool to assist decision-making during epilepsy preoperative workups. With ongoing advancements in LLMs, their reliability and accuracy are anticipated to improve.

Neurodevelopmental disorders often impair multiple cognitive domains. For instance, a genetic epilepsy syndrome might cause seizures due to cortical hyperexcitability and present with memory impairments arising from hippocampal dysfunction. This study examines how a single disorder differentially affects distinct brain regions using induced pluripotent stem cell (iPSC)-derived cortical- and hippocampal-ganglionic eminence assembloids to model developmental and epileptic encephalopathy 13, a condition arising from gain-of-function mutations in the SCN8A gene encoding the sodium channel Nav1.6. While cortical assembloids showed network hyperexcitability akin to epileptogenic tissue, hippocampal assembloids did not, and instead displayed network dysregulation patterns similar to in vivo hippocampal recordings from epilepsy patients. Predictive computational modeling, immunohistochemistry, and single-nucleus RNA sequencing revealed changes in excitatory and inhibitory neuron organization that were specific to hippocampal assembloids. These findings highlight the unique impacts of a single pathogenic variant across brain regions and establish hippocampal assembloids as a platform for studying neurodevelopmental disorders.

Brain-wide communication supporting flexible behavior requires coordination between sensory and associative regions but how brain networks route sensory information at fast timescales to guide action remains unclear. Using spiking neural networks and human intracranial electrophysiology during spatial attention tasks, where participants detected targets at cued locations, we show that high-frequency activity bursts (HFAb) serve as information-carrying events, facilitating fast, long-range communications. HFAbs were evoked by sensory cues and targets, dynamically linked to low-frequency rhythms. Notably, both HFAb responses following cues and their decoupling from slow rhythms predicted performance accuracy. HFAbs were synchronized at the network-level, identifying distinct cue- and target-activated subnetworks. These subnetworks exhibited a temporal lead-lag organization following target onset, with cue-sactivated subnetworks preceding target-activated subnetworks when the cue provided relevant target information. Computational modeling indicated that HFAbs reflect transitions to coherent population spiking and are coordinated across networks through distinct mechanisms. Together, these findings establish HFAbs as neural mechanisms for fast, large-scale communication supporting attentional performance.

Cognitive flexibility relies on the continuous accumulation and integration of sensory evidence to guide adaptive behavior. In natural environments, behaviorally relevant information unfolds sequentially over time and is constantly evaluated against prior knowledge, task rules, and current demands. Integration of these inputs poses a computational challenge: How is temporally unfolding, predictive information integrated into a stable representation, while preserving the discriminability and flexibility to map individual stimuli to competing context-specific actions? Using large-scale human intracranial electroencephalography, we assessed how neural population activity integrates behaviorally relevant information across multiple sensory events that sequentially unfold over time and jointly determine the current context. The results uncover that the population geometry supports the emergence of conjunctive coding subspaces that integrate prior information with current sensory evidence and jointly define the temporal context that mediates behavioral benefits. Evidence accumulation diversifies the population responses distributed across the cortex, increasing the representational space that embeds context-dependent stimulus-action mappings. Hence, context-dependent sensory coding might constitute the neural basis underlying adaptive human behavior. In sum, these results demonstrate how neural population activity balances integrating predictive information with preserving stimulus discriminability to enable flexibility, while minimizing interference.

Declarative memory depends on the coordination of local processing, indexed by high-frequency broadband (HFB) activity, with global network organization, indexed by theta oscillations. However, theta and HFB exhibit asynchronous timing, raising the question of how results of local processing are communicated throughout the network. Using intracranial EEG in patients performing a recognition memory task, we examined this coordination across the medial temporal lobe (MTL) and prefrontal cortex (PFC). HFB peak activity was earlier in the MTL than PFC. Anchoring analyses of theta phase clustering and connectivity to HFB peaks revealed strong phase clustering locked to HFB peaks in the PFC, as well as connectivity between the PFC and MTL that predicted individual memory performance. Graph analysis revealed specific connections amidst sparse network connectivity during memory success. This study demonstrates that transient brain states linked to internal physiological events support memory and refines our understanding of local and network-level process interactions. ### Competing Interest Statement The authors have declared no competing interest.

Brain-wide communication supporting flexible behavior requires coordination between sensory and associative regions but how brain networks route sensory information at fast timescales to guide action remains unclear. Using human intracranial electrophysiology and spiking neural networks during spatial attention tasks, where participants detected targets at cued locations, we show that high-frequency activity bursts (HFAbs) mark temporal windows of elevated population firing that enable fast, long-range communications. HFAbs were evoked by sensory cues and targets, dynamically coupled to low-frequency rhythms. Notably, both the strength of cue-evoked HFAbs and their decoupling from slow rhythms predicted behavioral accuracy. HFAbs synchronized across the brain, revealing distinct cue- and target-activated subnetworks. These subnetworks exhibited lead-lag dynamics following target onset, with cue-activated subnetworks preceding target-activated subnetworks when cues were informative. Computational modeling suggested that HFAbs reflect transitions to population spiking, denoting temporal windows for network communications supporting attentional performance. These findings establish HFAbs as signatures of population state transitions, supporting information routing across distributed brain networks.

Attention samples visual space sequentially to enhance behaviorally relevant sensory representations. While traditionally conceptualized as a static continuous spotlight, contemporary models of attention highlight its discrete nature. But which neural mechanisms govern the temporally precise allocation of attention? Periodic brain activity as exemplified by neuronal oscillations as well as aperiodic temporal structure in the form of intrinsic neural timescales have been proposed to orchestrate the attentional sampling process in space and time. However, both mechanisms have been largely studied in isolation. To date, it remains unclear whether periodic and aperiodic temporal structure reflect distinct neural mechanisms. Here, we combined computational simulations with a multimodal approach encompassing five experiments, and three different variants of classic spatial attention paradigms, to differentiate aperiodic from oscillatory-based sampling. Converging evidence across behavior as well as scalp and intracranial electroencephalography (EEG) revealed that periodic and aperiodic temporal regularities can theoretically and experimentally be distinguished. Our results extend the rhythmic sampling framework of attention by demonstrating that aperiodic neural timescales predict behavior in a spatially-, context-, and demand-dependent manner. Aperiodic timescales increased from sensory to association cortex, decreased during sensory processing or action execution, and were prolonged with increasing behavioral demands. These results reveal that multiple, concurrent temporal regularities govern attentional sampling.

Achieving goals in real-life situations—from fetching a glass of water to landing a dream job—often requires planning based on experience and executing a sequence of actions. Neurophysiological research in animal models has indicated that the orbitofrontal cortex (OFC) mediates relationships between memory, actions, and outcomes and the hippocampus and other medial temporal lobe (MTL) regions are known to be critical for rapid learning, but little is known about how these areas interact to support rapid learning and retrieval of goal-directed action sequences in humans. Here, we leverage a rare opportunity to investigate human OFC gamma oscillations and examine the coordination between the OFC and MTL during a continuous multi-step task that requires applying recently acquired experience to guide behavior. We used multisite intracranial electroencephalography (iEEG) recordings while participants searched for a hidden goal in an animated game to study neural activity in both brain areas during goal-directed behavior. Hippocampal ripples—brief high-frequency oscillations reflecting synchronized neuronal firing—are known to support memory consolidation during sleep, but their role during active memory retrieval and updating remains unclear. We found that OFC gamma activity was modulated by both memory demands and ripples in the hippocampus and adjacent structures. Notably, ripple-coupled OFC gamma during exploration was associated with subsequent task performance. We propose that hippocampal ripples mark a narrow window, supporting hippocampal-cortical communication required for successful goal encoding for future behaviors. ### Competing Interest Statement R. C. O'Reilly is Chief Scientist at the Astera Obelisk lab and eCortex Inc., which may derive indirect benefit from the work presented here. The other authors declare no competing interests. Office of Naval Research, N00014-20-1- 2578

The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study ( https://osf.io/gsru7 ), we analysed intracranial electroencephalography recordings from widespread brain regions in a large developmental cohort. Using task-based (that is, attention to to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93-54.00 years, 63 males; n electrodes = 5,691), we mapped aperiodic (1/ƒ-like) activity, a proxy of neural noise, where steeper slopes indicate less noise and flatter slopes indicate more noise. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In the prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in grey matter volume are similarly associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC control during adolescence in the development of attention and memory.

Episodic memory relies on the formation and retrieval of content-specific memory traces. In addition to their veridical reactivation, previous studies have indicated that traces may undergo substantial transformations. However, the exact time course and regional distribution of reinstatement and transformation during recognition memory have remained unclear. We applied representational similarity analysis to human intracranial electroencephalography to track the spatiotemporal dynamics underlying the reinstatement and transformation of memory traces. Specifically, we examined how reinstatement and transformation of item-specific representations across occipital, ventral visual, and lateral parietal cortices contribute to successful memory formation and recognition. Our findings suggest that reinstatement in temporal cortex and transformation in parietal cortex coexist and provide complementary strategies for recognition. Further, we find that generalization and differentiation of neural representations contribute to memory and probe memory-specific correspondence with deep neural network (DNN) model features. Our results suggest that memory formation is particularly supported by generalized and mnemonic representational formats beyond the visual features of a DNN.

Value-based decision-making involves multiple cortical and subcortical brain areas, but the distributed nature of neurophysiological activity underlying economic choices in the human brain remains largely unexplored. Specifically, the nature of the neurophysiological representation of reward-guided choices, as well as whether they are represented in a subset of reward-related regions or in a more distributed fashion, is unknown. Here, we hypothesize that reward choices, as well as choice-related computations (win probability, risk), are primarily represented in high-frequency neural activity reflecting local cortical processing and that they are highly distributed throughout the human brain, engaging multiple brain regions. To test these hypotheses, we used intracranial recordings from multiple areas (including orbitofrontal, lateral prefrontal, parietal, cingulate cortices as well as subcortical regions such as the hippocampus and amygdala) from neurosurgical patients of both sexes playing a decision-making game. We show that high-frequency activity (HFA; ɣ and HFA) represents both individual choice-related computations (e.g., risk, win probability) and choice information with different prevalence and regional representation. Choice-related computations are locally and unevenly present in multiple brain regions, whereas choice information is widely distributed and more prevalent and appears later across all regions examined. These results suggest brain-wide reward processing, with local HFA reflecting the coalescence of choice-related information into a final choice, and shed light on the distributed nature of neural activity underlying economic choices in the human brain.

Episodic memory depends on the coordination of local processing, indexed by high-frequency broadband (HFB) activity, with global organization, indexed by theta oscillations. However, theta and HFB exhibit asynchronous timing, raising the question of how results of local processing are communicated. Using intracranial EEG in patients performing a recognition memory task, we examined this coordination across medial temporal (MTL) and prefrontal (PFC) regions. HFB peaks occurred earlier in the MTL than in the PFC. Contrasting analyses were anchored either to these internally driven HFB peaks or to the external event of stimulus presentation. We discovered three key results. First, the role of the PFC changed from encoding to retrieval. Specifically, PFC-MTL theta connectivity was aligned with internal PFC peaks during encoding, suggesting top-down initiation. By contrast, this connection was aligned with external stimulus presentation during retrieval, suggesting bottom-up initiation. Second, the anterior cingulate cortex exhibited connectivity that was aligned to internal HFB peaks only, suggesting that its role is evaluative, devoid of direct stimulus processing. Third, graph theoretic analysis of whole-brain connectivity patterns revealed that the connections predicting successful memory performance were embedded in transient, sparse network states. These results reveal that analyses triggered from internally-generated events yield different results when compared to classic analyses triggered using external events. The picture that emerges is a sequence of specific, short-lived, internally-generated states that drive episodic memory success.

Humans effortlessly juggle their internal thoughts, but the neuronal dynamics that support mental manipulation are largely unknown. Leveraging the high spatiotemporal fidelity of intracranial recordings in humans (N = 30), we provide evidence that mental sound manipulation involves the inhibition of sensory cortex and the coordinated engagement of memory and control networks. This modulation manifests in two ways. First, there is a shift in the balance between faster (> 30 Hz) and slower (< 30 Hz) dynamics in primary and secondary auditory areas, suggesting a decrease in local excitability. Second, there is a distributed increase in oscillatory synchrony (6-10 Hz), which predicts imagery vividness and task performance. This evidence points to a key role of local excitability and inter-areal synchrony in the manipulation of thought.

Goal-directed behavior requires adjusting cognitive control, both in preparation for and in reaction to conflict. Theta oscillations and population activity in dorsomedial prefrontal cortex (dmPFC) and dorsolateral PFC (dlPFC) are known to support reactive control. Here, we investigated their role in proactive control using human intracranial electroencephalogram (EEG) recordings during a Stroop task that manipulated conflict expectations. During response selection, conflict processing enhanced dlPFC beta desynchronization, dmPFC theta increases, and high-frequency activity (HFA, which indexes local population activity) in both regions. After responses, conflict suppressed theta and boosted beta rebounds in both regions. Importantly, pre-trial dmPFC theta increased when conflict was anticipated, and within-trial theta, beta, and HFA dynamics were accentuated when conflict was rare. These findings reveal how the balance of reactive and proactive control modulates shared HFA and dissociable theta-beta conflict signals in dmPFC and dlPFC and identifies pre-trial dmPFC theta as a candidate substrate for proactive control.

Our brains extract structure from the environment and form predictions given past experience. Predictive circuits have been identified in wide-spread cortical regions. However, the contribution of medial temporal structures in predictions remains under-explored. The hippocampus underlies sequence detection and is sensitive to novel stimuli, sufficient to gain access to memory, while the amygdala to novelty. Yet, their electrophysiological profiles in detecting predictable and unpredictable deviant auditory events remain unknown. Here, we hypothesized that the hippocampus would be sensitive to predictability, while the amygdala to unexpected deviance. We presented epileptic patients undergoing presurgical monitoring with standard and deviant sounds, in predictable or unpredictable contexts. Onsets of auditory responses and unpredictable deviance effects were detected earlier in the temporal cortex compared with the amygdala and hippocampus. Deviance effects in 1-20 Hz local field potentials were detected in the lateral temporal cortex, irrespective of predictability. The amygdala showed stronger deviance in the unpredictable context. Low-frequency deviance responses in the hippocampus (1-8 Hz) were observed in the predictable but not in the unpredictable context. Our results reveal a distributed network underlying the generation of auditory predictions and suggest that the neural basis of sensory predictions and prediction error signals needs to be extended.

Sleep constitutes a brain state of disengagement from the external world that supports memory consolidation and restores cognitive resources. The precise mechanisms how sleep and its varied stages support information processing remain largely unknown. Synaptic scaling models imply that daytime learning accumulates neural information, which is then consolidated and downregulated during sleep. Currently, there is a lack of in-vivo data from humans and rodents that elucidate if, and how, sleep renormalizes information processing capacities. From an information-theoretical perspective, a consolidation process should entail a reduction in neural pattern variability over the course of a night. Here, in a cross-species intracranial study, we identify a tradeoff in the neural population code during sleep where information coding efficiency is higher in the neocortex than in hippocampal archicortex in humans than in rodents as well as during wakefulness compared to sleep. Critically, non-REM sleep selectively reduces information coding efficiency through pattern repetition in the neocortex in both species, indicating a transition to a more robust information coding regime. Conversely, the coding regime in the hippocampus remained consistent from wakefulness to non-REM sleep. These findings suggest that new information could be imprinted to the long-term mnemonic storage in the neocortex through pattern repetition during sleep. Lastly, our results show that task engagement increased coding efficiency, while medically-induced unconsciousness disrupted the population code. In sum, these findings suggest that neural pattern variability could constitute a fundamental principle underlying cognitive engagement and memory formation, while pattern repetition reflects robust coding, possibly underlying the consolidation process.