Background:Deep brain stimulation (DBS) of the anterior limb of the internal capsule (ALIC) is an effective treatment for severe obsessive-compulsive disorder (OCD). Identifying brain readouts of positive response may guide further DBS optimization. Methods:We measured local field potential (LFP) changes from bilateral DBS leads in 10 OCD patients implanted at a uniform tractographic network target derived from prior DBS responders. We consistently stimulated dorsal lead contacts in the ALIC white matter, while recording LFP from the ventral lead contacts in grey matter of the anterior globus pallidus externus (GPe), a key node in the basal ganglia non-motor indirect pathway. Results:After six months of DBS, OCD symptoms decreased on average by 40% across subjects, along with a significant decrease in alpha activity across both hemispheres. Only one patient did not have an improvement of symptoms, and this was also the only patient to never exhibit an alpha decrease in either hemisphere. Conclusions:Our findings suggest that therapeutic ALIC DBS coincides with a stable decrease in limbic-cognitive GPe alpha power, which should be further investigated as a potential biomarker of sustained response.

Dopamine (DA) signals from substantia nigra (SN) neurons encode reward prediction errors (RPEs) and have been implicated in motor control, reward processing, and motivational vigor. However, how recent reward history, related to reward expectations, is represented within the dopaminergic system remains poorly understood, particularly in humans, due to the difficulty of recording DA neuron activity directly. To address this, we performed single-unit recordings from the SN of patients with Parkinson's disease undergoing neurosurgery while they played a two-armed bandit decision-making task. We found that the firing rates (FRs) of putative DA neurons during reward expectation were modulated by previous trial outcomes, with higher FRs following positive outcomes. This increase in FRs was associated with faster subsequent reaction times (RTs), suggesting a link between neural signals reflecting prior reward and behavioral response vigor. These results provide a potential physiological substrate for how reward history influences behavior through the modulation of human dopaminergic activity.

Working memory (WM) enables us to maintain and manipulate information over time, but how the brain organizes sequential information locally and across networks remains unclear. Recent work suggests that slow and fast theta oscillations serve different roles in memory, yet their distinct contributions to sequential WM are unknown. Based on evidence that the hippocampus (HC) and orbitofrontal cortex (OFC) support sequential WM and that slower theta cycles provide optimal temporal windows for organizing items in WM, we predicted that these regions would coordinate via slow theta dynamics. We analyzed intracranial EEG from the HC, OFC, and amygdala (AMY) in 21 neurosurgical patients (7 female, 13-54 years of age; M ± SD, 30 ± 11.2 years) performing a delayed match-to-sample WM task. We assessed phase locking between regions, phase-amplitude coupling within regions, and neuronal phase coding for slow (~1-4.5 Hz) and fast (~4.5-8 Hz) theta oscillations. We found significant slow and fast theta synchrony between all regions, but identical anatomical pathways produced opposing behavioral effects depending on oscillatory frequency, particularly during higher cognitive demand. Slow theta synchrony was associated with faster response times (RTs), while fast theta synchrony between HC and OFC hindered both accuracy and RTs. Unexpectedly, AMY modulated RT through demand-dependent slow theta synchrony, where AMY-OFC synchrony predicted faster RTs during maintenance and HC-AMY synchrony predicted faster RTs during higher cognitive demand. Sustained coupling between slow theta oscillations and high-frequency broadband activity within each region suggests that local organization coincides with beneficial network behavioral effects. These results establish a frequency-opponent mechanism in which theta oscillation frequencies determine whether HC-OFC circuits facilitate or impair sequential WM.

Brain-computer interfaces (BCIs) have achieved transformative success in restoring movement and communication. However, extending these approaches to decoding or recovery of cognitive function, such as attention or memory, poses fundamentally new challenges. Cognitive BCIs will need to contend with distributed and dynamic neural processes that differ sharply from the more localized, stable representations underlying motor and language control, imposing new technical and conceptual demands. Conversely, neuromodulation, long used in neurological and psychiatric therapies, offers a complementary methodological path and initial translational applications through causal modulation of cognitive circuits. Integrating these approaches into adaptive, closed-loop systems could allow cognitive BCIs to restore mental function and bridge systems neuroscience and next-generation neurotherapeutics capable of monitoring and shaping human cognition in real time.

At the foundation of neurotransmission, and by extension at the foundation of brain function, are coordinated programs of gene expression involving many thousands of genes. These programs are poorly defined in humans because most modern studies that characterize human brain gene expression use tissue obtained in the postmortem state when neurotransmission and brain function have ceased. Here, to advance knowledge of the gene expression programs at the foundation of neurotransmission in the human brain, gene expression was characterized in 130 prefrontal cortex (PFC) samples obtained from participants of the Living Brain Project (LBP) during neurosurgical procedures in conjunction with intracranial recordings of neurotransmission traits in deep brain structures. In a group of 15 procedures, participants performed a cognitive task during intracranial recordings of the substantia nigra; in the remaining group of 115 procedures, participants were at rest during intracranial recordings of either the subthalamic nucleus or the globus pallidus. Analyses of the data obtained from the group of 15 procedures, though underpowered to identify individual gene-trait associations, uncovered evidence of transcriptome-wide signatures of PFC gene expression that associated with neurotransmission traits. These signatures were reproduced in analyses of data from the group of 115 procedures and in analyses of data from a third independent human cohort. A set of genes with evidence of association to neurotransmission in multiple cohorts was termed the “transcriptional program associated with neurotransmission” (TPAWN) and analyses of data from studies of model systems and genetic variation in human populations validated the role of TPAWN genes in neurotransmission and brain function. In PFC excitatory neurons of LBP participants, higher expression of TPAWN genes tracked with higher expression of genes that in mouse frontal cortex are markers of excitatory neurons that connect the frontal cortex to deep brain structures. Taken together, the findings of this report help advance knowledge of the transcriptomic foundations of neurotransmission in the living human brain.

Depressive symptoms could affect decision-making in multiple ways, yet it remains unclear whether these effects are context-sensitive or stable over time. In this longitudinal study, 236 online participants (n=129 at 1-month follow-up) completed a social exchange task (Ultimatum Game) and a reward learning task (Reversal Learning). Mixed-effects regression analyses revealed consistent effects of depressive, but not anhedonic, symptoms on social decision-making. Specifically, participants with elevated depressive symptoms (BDI-II > 13) exhibited slower response times and lower acceptance of unfair offers in the Ultimatum Game across both time points relative to participants below the clinical threshold, regardless of anhedonic symptoms. In contrast, symptom-related effects in the Reversal Learning task were less consistent across time. Analyses using continuous symptom measures yielded weaker and less stable effects than the primary categorical models, suggesting that symptom–behavior relationships may be non-linear. Overall, these findings indicate that the effects of depressive symptoms on decision-making are context-sensitive and robustly expressed during social exchange. This work highlights the importance of considering both symptom dimension and decision context when identifying behavioral phenotypes relevant to depression.

The ability to quickly learn and generalize is one of the brain's most impressive feats and recreating it remains a major challenge for modern artificial intelligence research. One of the most mysterious one-shot learning abilities displayed by humans is one-shot perceptual learning, whereby a single viewing experience drastically alters visual perception in a long-lasting manner. Where in the brain one-shot perceptual learning occurs and what mechanisms support it remain enigmatic. Combining psychophysics, 7 T fMRI, and intracranial recordings, we identify the high-level visual cortex as the most likely neural substrate wherein neural plasticity supports one-shot perceptual learning. We further develop a deep neural network model incorporating top-down feedback into a vision transformer, which recapitulates and predicts human behavior. The prior knowledge learnt by this model is highly similar to the neural code in the human high-level visual cortex. These results reveal the neurocomputational mechanisms underlying one-shot perceptual learning in humans.

BACKGROUND:Central sulcus identification using phase reversal on electrocorticography (ECoG) is a critical tool for neurosurgical intervention around the primary motor and somatosensory cortices. This mapping is typically performed using cortical arrays with a resolution of several millimeters. OBSERVATIONS:A 30-year-old female underwent a right frontoparietal craniotomy for resection of a 4-cm contrast-enhancing lesion within the central sulcus. Central sulcus localization was performed using a standard ECoG array. High-resolution micro-ECoG (µECoG) arrays were then placed over the pre- and postcentral gyri, giving 2048-electrode recordings across the central sulcus. Combining this high-resolution µECoG with an augmented reality imaging overlay to identify the tumor, the central sulcus was split, revealing the underlying tumor. A safe, gross-total resection was obtained with no postoperative complications. Through the use of µECoG arrays spanning into the central sulcus, a high-resolution phase-reversal contour was identified across the central sulcus. LESSONS:The authors demonstrate the feasibility and utility of µECoG for sensorimotor mapping within the central sulcus, revealing a phase reversal at a resolution of approximately 400 microns. Compared to standard mapping, which records gyral surface electrophysiology, they further demonstrate phase-reversal electrophysiology within a dissected central sulcus. High-resolution cortical mapping from µECoG may foster several neurosurgical advancements, from tumor resection to brain-computer interfaces. https://thejns.org/doi/10.3171/CASE25534.

Depressive symptoms could affect decision-making in multiple ways. Yet it remains elusive whether such effects are context-invariant or stable over time. In this longitudinal study, 236 online participants (129 at one-month follow-up) completed a social exchange task (ultimatum game) and a reward learning task (reversal learning). Mixed-effects regression analyses revealed consistent effects of depressive, but not anhedonic symptoms, on social decision-making. Specifically, more severe depressive symptoms predicted slower response times and reduced acceptance of unfair offers in the ultimatum game across time. In contrast, symptom-related effects in the reversal learning task were less stable. Together, these findings highlight the context-sensitivity and longitudinal stability of the effects of depressive symptoms on social decision-making.

Human decision-making involves the coordinated activity of multiple brain areas, acting in concert, to enable humans to make choices. Most decisions are carried out under conditions of uncertainty, where the desired outcome may not be achieved if the wrong decision is made. In these cases, humans deliberate before making a choice. The neural dynamics underlying deliberation are unknown and intracranial recordings in clinical settings present a unique opportunity to record high temporal resolution electrophysiological data from many (hundreds) brain locations during behavior. Combined with dynamic systems modeling, these allow identification of latent brain states that describe the neural dynamics during decision-making, providing insight into these neural dynamics and computations. Results show that the neural dynamics underlying risky decisions, but not decisions without risk, converge to separate subspaces depending on the subject's preferred choice and that the degree of overlap between these subspaces declines as choice approaches, suggesting a network level representation of evidence accumulation. These results bridge the gap between regression analyses and data driven models of latent states and suggest that during risky decisions, deliberation and evidence accumulation toward a final decision are represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice.

Meditation is an accessible mental practice associated with emotional regulation and well-being. Loving-kindness meditation (LKM), a specific subtype of meditative practice, involves focusing one's attention on thoughts of well-being for oneself and others. Meditation has been proven to be beneficial in a variety of settings, including therapeutic applications, but the neural activity underlying meditative practices and their positive effects are not well understood. It has been difficult to understand the contribution of deep limbic structures given the difficulty of studying neural activity directly in the human brain. Here, we leverage a unique patient population, epilepsy patients chronically implanted with responsive neurostimulation devices that allow chronic, invasive electrophysiology recording to investigate the physiological correlates of LKM in the amygdala and hippocampus of novice meditators. We find that LKM-associated changes in physiological activity were specific to periodic, but not aperiodic, features of neural activity. LKM was associated with an increase in γ (30 to 55 Hz) power and an alternation in the duration of β (13 to 30 Hz) and γ oscillatory bursts in both the amygdala and hippocampus, two regions associated with mood disorders. These findings reveal the nature of LKM-induced modulation of limbic activity in first-time meditators.

Recent advances in deep brain stimulation (DBS) of the subcallosal cingulate (SCC) show promise in mitigating the symptoms of treatment-resistant depression (TRD) in humans 1-3 . Monoamines, such as dopamine and serotonin, mediate the effects of pharmacological treatments of depression. However, their roles in recovery following DBS remain elusive, largely due to technical limitations of measuring these neurotransmitters in the living human brain. Here, by leveraging machine learning-enhanced electrochemistry 4-7 , we show that dopamine and serotonin signaling following DBS to the SCC predicted later depressive symptom relief in humans with TRD. We found that both dopamine and serotonin levels increased following subtherapeutic intraoperative SCC stimulation, with each neurotransmitter showing selective responses to distinct decision-making tasks. Furthermore, acute dopamine increases predicted later mood improvements during a social decision-making task, while serotonin enhancement predicted faster responses during a non-social learning task longitudinally. Critically, changes in dopamine and serotonin levels during the social decision-making task jointly predicted depressive symptom remission at 6-month follow-up. These findings illustrate the contribution of both dopamine and serotonin signaling in predicting behavioral improvement and depressive symptom remission in humans with TRD. Such neurochemical plasticity may serve as potential mechanistic biomarkers for SCC DBS mechanism and TRD treatment response. Significance statement:Dopamine and serotonin levels increased following acute DBS to the SCC in humans.Acute dopamine and serotonin changes predicted later mood and response speed changes.Sustained TRD recovery was predicted by acute increases in both dopamine and serotonin estimates.

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.

To learn from decisions, individuals form cognitive representations of the outcomes of their choices. Prediction error (PE) is a metric that represents the difference between a choice’s expected and actual reward in its magnitude and valence. Previous work has demonstrated changes in frequency band-specific power and functional connectivity (FC) that correspond to changes in PE during decision-making tasks. However, few studies have compared the neural representations of choice across these encoding modalities (band power vs. FC). We address this gap in knowledge by analyzing the intracranial EEG (iEEG) data from 15 participants during a decision-making task, during which participants chose between a guaranteed small reward or a chance of receiving a larger reward, then received feedback about their actual reward values. We calculated average power and coherence (a metric of functional connectivity) during the second following feedback. To compare PE-relevant information across encoding modalities, we used power and coherence feature sets to classify the PE magnitude and valence for each trial using logistic regression. Additionally, we examined the regression coefficients to identify the frequency bands of power and coherence that carried the most PE-relevant information. For most participants, both feature sets resulted in above-chance classification of PE magnitude and valence. PE-relevant information was concentrated in the higher frequency bands in the power feature set, and to a lesser degree in the coherence feature set. Although the power feature set outperformed the coherence feature set using this simple classification approach, our results suggest that both modalities play roles in encoding PE.

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

We combined scalp EEG and intracranial EEG (iEEG) to identify spectral and network-level signatures of executive control during a delayed match-to-sample task working memory task. To isolate executive processes, we contrasted test and sample phases, matched in perceptual input but differing in cognitive demand. Scalp EEG revealed increased frontal midline theta event-related spectral perturbations (ERSPs), dynamic increases and decreases in posterior theta-alpha ERSPs, and decreased central alpha-beta ERSPs during the test phase. These local spectral changes were accompanied by enhanced frontoposterior theta phase synchrony and network hub strength, predicting higher behavioral accuracy. Using a novel cross-modal scalp EEG-iEEG ERSP similarity approach, we localized the sources of scalp-derived frontal midline, posterior, and central control effects to medial frontal, parietal, temporal, and occipital regions. Our results integrate power and connectivity measures across scalp and iEEG, linking local spectral fluctuations to broader network organization. Together, they support a model in which executive control emerges from flexible, temporally precise coordination between medial frontal control hubs and posterior representational systems. ### Competing Interest Statement The authors have declared no competing interest. National Institute of Neurological Disorders and Stroke, R00NS115918, T32NS047987, R01NS021135