Biology subjects

Constantinidis, C.

Publications and source records attributed to Constantinidis, C..

At least 19 recordsLinked to original sources

bioRxiv2026

The cost of efficiency in flexible neural representations

Working memory depends on the flexible representation of stimulus information in neural activity, which changes dynamically depending on task. Stimulus transformations are thought to be efficient in use of neural resources and optimal for task performance. However, these transformations are often opaque, and efficiency may conflict with optimal performance. Here we show that in a working memory task requiring selective recall of one of two stimuli based on a context cue, the prefrontal cortex of two male monkeys prioritized efficiency by overwriting information within a shared neural subspace rather than maintaining distinct subspaces for each stimulus. In neural activity and recurrent neural networks such efficiency incurs a cost, in that efficient representations are more prone to errors. Conversely, stimulation of the cholinergic forebrain which improves behavior altered this default mechanism by encoding distinct contexts in higher dimensions. These findings demonstrate a fundamental tradeoff between efficiency and effectiveness in flexibly updating working memory.

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bioRxiv2026

Trajectories of Response Inhibition Development in Adolescence

Adolescence is a pivotal developmental period during which cognitive control and the ability to suppress maladaptive impulses undergo profound refinement. Despite extensive behavioral and imaging studies in humans, the neural circuit mechanisms that drive the maturation of inhibitory control remain poorly understood. To address this gap, we performed a longitudinal multimodal study in macaque monkeys spanning the transition from puberty to adulthood, combining behavioral measurements, prefrontal neurophysiology, and structural neuroimaging within the same individuals. Behavioral performance in multiple variants of the antisaccade task improved dramatically across adolescence. In parallel, prefrontal cortical neurons exhibited robust developmental increases in task-related firing, particularly around the generation of goal-directed saccades, indicating maturation of neural representations supporting inhibitory control. Strikingly, trajectories of behavioral improvement and neural activity maturation were tightly predicted by the development of long-range white matter pathways linking frontal cortex with distributed brain networks. These findings identify white matter development as a key systems-level mechanism underlying the emergence of mature inhibitory control and prefrontal neural dynamics and establish a primate framework for understanding developmental vulnerabilities associated with neuropsychiatric disorders that emerge in adolescence and early adulthood.

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bioRxiv2026

Prefrontal Mechanisms of Rule Learning

Rule learning is associated with lasting changes in prefrontal activity. However, experiments typically focus on learning a single set of rules or task and there remains a significant gap regarding how related mechanisms may reflect behavioral improvements in different contexts, especially when examining across different tasks and modalities. We therefore recorded single units from chronic electrode arrays implanted in the prefrontal cortex of four monkeys as they were trained to perform spatial and object working memory tasks with the goal of assessing the resulting activity changes that would be induced by rule learning. Progression of training allowed behavioral improvements to be correlated with a variety of neural effects that could be observed across different task contexts, including increases or decreases of both firing rate and decoding, increases in the proportion of firing rate variance that was unexplained by sensory stimuli and motor actions, and the increased separation of population response trajectories in state space. Our results thus reveal how rule learning induces plasticity of prefrontal cortical activity, and the aspects of neural activity changes that were unique to individual tasks and modalities or common across them. Our results ultimately reveal new patterns of training effects, identifying the generalized prefrontal mechanisms that are responsible for rule learning.

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bioRxiv2026

Physiological, Histological, and Cognitive Characterization of a Macaque Model of Presbycusis

Age-related hearing loss (ARHL), or presbycusis, is one of the most prevalent sensory deficits in older adults and has been increasingly implicated in cognitive decline and dementia. This study characterizes ARHL in a rhesus macaque model by combining histological, physiological, and cognitive assessments. Aged macaques exhibited progressive cochlear degeneration, with marked outer hair cell loss at mid-to-high frequencies, elevated auditory thresholds, reduced distortion product otoacoustic emissions, and impaired auditory brainstem responses including amplitude reduction, latency prolongation, and diminished temporal precision. Despite modest reductions in inner hair cell ribbon synapse counts, hypertrophic changes were observed. These auditory deficits correlated with subtle impairments in visual working memory, as measured by a delayed match-to-sample task, underscoring a potential sensory-cognitive link. By capturing cross-domain aging markers in a translationally relevant primate model, this work lays a foundation for mechanistic studies and therapeutic interventions targeting both hearing and cognition in aging populations.

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bioRxiv2026

Discovering macroscale functional organization on the structure of brain-like recurrent neural networks

Linking structure and function is a central topic in both neurobiology and artificial intelligence. Human brain functions are organized across the macroscale cortex into parcellations, modules, and hierarchies that can be inferred from intrinsic structural architecture, providing an interpretable and clinically meaningful framework for linking structure to function. While artificial neural networks have been successfully aligned with human cognition at the representational level, it remains unclear whether the structural principles linking brain anatomy and function can extend to artificial neural networks, and whether imposing brain-like structural constraints can induce comparable functional organization to that observed in the human brain. Here, we introduce BrainRNN, a brain-like recurrent neural network architecture inspired by macroscale human cortical structure. We show that under structural constraints, BrainRNNs selectively regulate the distribution of connectivity and recruit more activated units in association regions for higher-order cognitive capacity. Moreover, we demonstrate structure-function coupling in BrainRNNs and show that structural constraints enable macroscale functional organization, including functional modules and gradients, to emerge along topographic and topological axes, closely mirroring empirical findings in the human cortex. Together, these results demonstrate how multiple brain-like structural constraints jointly shape functional organization and enable function to be inferred from structure, highlighting the potential of structurally grounded artificial intelligence for neuroscientific research.

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bioRxiv2026

Repeatable, low-drift recordings in behaving non-human primates using flexible microelectrodes

Neurophysiological recordings from non-human primates (NHPs) have traditionally relied on rigid microelectrode arrays made from stainless steel or silicon. While these devices enable high-quality recordings, a fundamental mechanical mismatch between rigid materials and soft brain tissue leads to inflammation, gliosis, and signal instability. In particular, brain micromotion causes continuous drifting of neurons relative to fixed electrodes, compromising single-unit tracking during both chronic and acute recordings. Flexible, penetrating electrodes offer a promising solution, but their adoption in NHPs has been hindered by the technical challenges of delivering ultra-thin polymers through thick dura mater. Here, we demonstrate a comprehensive approach for acute, repeated recordings in awake, behaving NHPs using flexible arrays. We fabricated a microelectrode array that spans cortical layers with 32 cellular-scale recording sites embedded in 7 m-thick Parylene-C. We developed a novel "telescopic" insertion method that combines concentric guide tubes with a retractable microwire shuttle. Our technique is compatible with standard chronic recording chambers and allowed for repeated penetration of free-floating arrays through intact dura over weeks without the need for a new craniotomy. Across two awake rhesus macaques, we optimized the electrode geometry and insertion procedure to achieve an 80% single-unit recording success rate. As animals performed an oculomotor delayed response task, we recorded task-responsive neurons from prefrontal and posterior parietal cortex with stable single-unit activity throughout 1-2-hour behavioral sessions. Critically, by comparing our flexible arrays to rigid probes in the same animals and recording chambers, we provide quantitative evidence that flexible electrodes reduce total single-unit drift from hundreds to tens of microns. Our work establishes flexible microelectrode arrays as a practical, dependable technology for NHP neuroscience and paves the way toward long-term, ultra-stable neurophysiology in large animal models.

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bioRxiv2025

Cross-species evidence for the refinement of intrinsic neural timescales supporting executive system maturation through adolescence

Brain functional, structural, and neurochemical maturation has been found to support the specialization of executive systems through adolescence that will lead to adult level processing. Importantly, animal models and initial EEG studies in humans indicate developmental improvements in neural processing of complex information that would be evident in changes in temporal dynamics, which are not well-understood. Intrinsic neural timescales (INTs), or the temporal windows over which neural populations integrate inputs, have been proposed to reflect circuit-level properties such as excitatory-inhibitory (E/I) balance, myelination, and functional properties supporting complex information processing such as in executive functioning. Here, we used a multimodal, cross-species approach to investigate how INTs develop across adolescence to support cognitive specialization. In parallel analyses using a large longitudinal human EEG cohort, adolescent intracranial sEEG recordings, and macaque local field potentials, we observed robust reductions in INTs through adolescence, particularly in frontal and parietal association cortices. These developmental reductions were shaped by local circuit physiology, as evidenced by associations between shorter INTs and both lower aperiodic exponents, indicative of increased E/I balance, and reduced spectral offsets, suggesting lower aggregate spiking. We also found structural contributions via developmental interactions between age and deep layer intracortical myelination which predicted shorter INTs, suggesting that long-range circuitry may play a key role in shaping spontaneous temporal dynamics. Functionally, shorter INTs in adolescence were linked to improved working memory accuracy and reduced response time variability, indicating a behavioral advantage of refined temporal integration windows through development. Together, these findings establish INTs as a conserved, biologically grounded signature of adolescent brain maturation, providing a mechanistic framework for how structural and physiological refinements reorganize temporal processing to support increasingly efficient cognitive function.

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bioRxiv2025

Asynchronous firing and off-states in working memory maintenance

Persistent spiking activity and activity-silent mechanisms have been proposed as neural correlates of working memory. To determine their relative contribution, we recorded neural activity from the lateral prefrontal and posterior parietal cortex of two male macaques using high-density microelectrode probes. We found that, when averaged across all neurons, persistent delay activity was observable throughout the duration of single trials in populations of prefrontal neurons with silent periods that did not deviate significantly from chance. However, temporal fluctuations in activity-dependent mnemonic information were present and weakly correlated between the prefrontal and posterior parietal cortices, suggesting at least partial, long-distance synchronization of off-states. Decoding accuracy of neurons recorded simultaneously was also reduced relatively to pseudo-populations constructed by splicing different trials together. Our results support an asynchronous state of working memory, maintained by the distributed pattern of persistent discharges across cortical neurons, which is subject to widely distributed fluctuations in information representation fidelity.

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bioRxiv2025

NERV: A Comprehensive Framework for Rapid, Reproducible, and Hardware-Synchronized Neuroscience Experiment Design and Execution

BackgroundBehavioral neuroscience experiments require precise stimulus control, millisecond timing, hardware integration, and robust data provenance. Increasing use of 3D environments and multimodal recordings adds challenges for development, accessibility, and reproducibility. Fragmented tools often separate presentation, synchronization, and logging, leading to inefficiencies. New MethodThe Neuroscience Experimental Runtime by Vanderbilt (NERV) is a Unity-based framework that unifies experiment design, execution, and data logging. It enables rapid, no-code prototyping by automating scene and script generation, event timing, state management, hardware-synchronized data acquisition, and archival of code and experimental configurations. The modular, open-source framework implements a "low floor, high ceiling" design that lowers barriers for non-programmers while remaining extensible for advanced customization. ResultsAcross 500 trials, Unity-to-TTL delay was 2.10 {+/-} 1.21 ms, TTL-to-photodiode delay was 28.93 {+/-} 0.76 ms, and Unity-to-screen delay was 31.04 {+/-} 1.41 ms. These results confirm stable millisecond precision and frame-locked timing, enabling reliable alignment of neural, behavioral, and visual events. Comparison with existing methodsExisting frameworks involve trade-offs. Some achieve precise timing but require advanced coding, while others improve accessibility but struggle with hardware or 3D graphics. Commercial platforms offer polish yet remain costly, closed-source, and inflexible. NERV combines millisecond precision, modular open-source design, and provenance in a single platform, reducing workflow fragmentation and enabling reproducible, scalable experiments. ConclusionNERV is an accessible yet extensible framework that unites rapid development, robust data provenance, and millisecond precision. It accelerates development, ensures reproducibility, and establishes a scalable foundation for next-generation neuro-science research.

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bioRxiv2025

Generalized brain-state modeling with KenazLBM

The large-scale functional state of a human brain remains difficult to characterize, much less predict. Regardless, techniques have been engineered to electrically neuromodulate the brain to treat a subset of neurologic and psychiatric disorders with moderate efficacy. Accurate characterization of a brains instantaneous functional state has stymied the development of more effective neuromodulation paradigms. Advanced computational methods are required to address this gap and enable large-scale neuroscience. Here we define the concept of generalized brain-state modeling across humans as Large Brain-State Modeling (LBM) and present KenazLBM as the worlds first example. KenazLBM can instantaneously characterize the functional state of a persons brain with raw iEEG data, and predict future brain-states. KenazLBM was trained on over 17.9 billion unique multichannel tokens from people undergoing intracranial electroencephalography (iEEG) recordings, and has learned to interrelate brain-states between people into a common interpretable topology. Most importantly, the model generalizes to unseen subject data with significant recording channel heterogeneity from the training set. We offer KenazLBM as a first generalized brain-state model to serve as a new paradigm of basic neuroscience inquiry and potential translation into neuromodulation therapeutics.

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bioRxiv2025

Integration of Audiovisual Motion in Dorsolateral Prefrontal Cortical Neurons

The dorsolateral prefrontal cortex is well recognized for its role in cognitive functions and activating action plans. In contrast, the properties of prefrontal neurons with respect to multisensory processing are less well studied. To address this question, we recorded single units from areas 8a and 46 of two female rhesus macaques while they were presented with visual, auditory, and audiovisual motion stimuli. The majority of dorsolateral prefrontal neurons responded to these sensory stimuli, with similar percentages of auditory-only, visual-only and audiovisual neurons. Approximately one third of responsive neurons exhibited significant super- or sub-additive interactions in response to the pairing of auditory and visual stimuli, revealing significant nonlinearities in their response profiles. Decoding motion signals from the population activity robustly differentiated multisensory from unisensory trials and also unisensory auditory and visual trials from each other. These results demonstrate that dorsolateral prefrontal neurons integrate auditory and visual motion signals, extending multisensory computations beyond sensory cortices into prefrontal circuits that support higher-order cognition. New & NoteworthyWe recorded single neurons in macaque dorsolateral prefrontal cortex during visual, auditory, and audiovisual motion. Nearly half of responsive neurons were multisensory and a third displayed significant super- or sub-additive interactions, while ensemble activity reliably decoded stimulus modality. These findings provide the strongest evidence to date that DLPFC performs rapid, nonlinear audiovisual integration, extending multisensory computations beyond classical posterior regions into the prefrontal circuits that support cognition.

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bioRxiv2025

Frontoparietal activity related to neuropsychological assessment of working memory

Executive functions, including working memory, are typically assessed clinically with neuropsychological instruments. In contrast, computerized tasks are used to test these cognitive functions in laboratory human and animal studies. Little is known of how neural activity captured by laboratory tasks relates to ability measured by clinical instruments and, by extension, clinical diagnoses of pathological conditions. We therefore sought to determine what aspects of neural activity elicited in laboratory tasks are predictive of performance in neuropsychological instruments. We recorded neural activity from intracranial electrodes implanted in human epilepsy patients as they performed laboratory working memory tasks. These patients had completed neuropsychological instruments preoperatively, including the Weschler Adult Intelligent Scale and the Wisconsin Card Sorting test. Our results revealed that increased high-gamma (70-150 Hz) power in the prefrontal and parietal cortex after presentation of visual stimuli to be remembered was indicative of lower performance in the neuropsychological tasks. On the other hand, we observed a positive correlation between high-frequency power amplitude in the delay period of the laboratory tasks and neuropsychological performance. Our results demonstrate how neural activity around task events relates to executive function and may be associated with clinical diagnosis of specific cognitive deficits.

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bioRxiv2024

Collapse of Interictal Suppressive Networks Permits Seizure Spread

How do networks in the brain limit seizure activity? In the Interictal Suppression Hypothesis (ISH), we recently postulated that high inward connectivity to seizure onset zones (SOZs) from non-involved zones (NIZs) is a sign of broader network suppression at rest. If broad networks appear to be responsible for interictal SOZ suppression, what changes during seizure initiation, spread, and termination? For patients with drug resistant epilepsy, intracranial monitoring offers a view into the electrographic networks which organize around and in response to the SOZ. In this manuscript, we investigate network dynamics in the peri-ictal periods to assess possible mechanisms of seizure suppression and the consequences of this suppression being overwhelmed. Peri-ictal network dynamics were derived from stereo electroencephalography (SEEG) recordings from 75 patients with drug-resistant epilepsy undergoing pre-surgical evaluation at Vanderbilt University Medical Center. We computed directed connectivity from 5-second windows in the periods between, immediately before, during, and after seizures. After aligning all network connectivity matrices between seizures and patients, we calculated net connectivity changes from the SOZ, propagative zone (PZ), and NIZ. Across all seizure types, we observed two distinct phases as seizures initiated and evolved: a large rapid increase in directed communication towards the SOZ from NIZ followed by a collapse in network connectivity. During this first phase, SOZs could be distinguished from all other regions (One-Way ANOVA, p=8.32x10-19 - 2.22x10-7, lower range to upper range of p-values). In the second phase and post-ictal period, SOZ inward connectivity decreased yet remained distinct (One-Way ANOVA, p= 2.58x10-10-1.66x10-2). Furthermore, NIZs appeared to drive the increase in inward SOZ connectivity while global connectivity between NIZs concordantly decreased. Stratifying by seizure subtype, we found that consciousness-impairing seizures show loss of inward connectivity from the NIZ earlier than conscious sparing seizures (one-way ANOVA, p<0.01 after false discovery correction). Tracking network reorganization against a surrogate for seizure involvement highlighted a possible antagonism between seizure propagation to the NIZ and the NIZs ability to maintain high connectivity to the SOZ. Finally, we found that inclusion of peri-ictal connectivity improved SOZ classification accuracy from previous models to a combined area under the curve of 93%. Overall, NIZs appear to actively respond to seizure onset and increase inhibitory signaling towards the SOZ, possibly in an attempt to thwart seizure activity. This inhibition appears to be insufficient to prevent seizure onset, and furthermore, loss of normal communication in the rest of the brain between NIZs may contribute to loss of consciousness during larger seizures. Dynamic connectivity patterns uncovered in this work may: i) allow more accurate delineation of surgical targets in focal epilepsy, ii) reveal why inward suppression of SOZs interictally may nonetheless be insufficient to prevent all seizures, and iii) provide insight into mechanisms of loss of consciousness during certain seizures.

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bioRxiv2024

Brain Charts for the Rhesus Macaque Lifespan

Recent efforts to chart human brain growth across the lifespan using large-scale MRI data have provided reference standards for human brain development. However, similar models for nonhuman primate (NHP) growth are lacking. The rhesus macaque, a widely used NHP in translational neuroscience due to its similarities in brain anatomy, phylogenetics, cognitive, and social behaviors to humans, serves as an ideal NHP model. This study aimed to create normative growth charts for brain structure across the macaque lifespan, enhancing our understanding of neurodevelopment and aging, and facilitating cross-species translational research. Leveraging data from the PRIMatE Data Exchange (PRIME-DE) and other sources, we aggregated 1,522 MRI scans from 1,024 rhesus macaques. We mapped non-linear developmental trajectories for global and regional brain structural changes in volume, cortical thickness, and surface area over the lifespan. Our findings provided normative charts with centile scores for macaque brain structures and revealed key developmental milestones from prenatal stages to aging, highlighting both species-specific and comparable brain maturation patterns between macaques and humans. The charts offer a valuable resource for future NHP studies, particularly those with small sample sizes. Furthermore, the interactive open resource (https://interspeciesmap.childmind.org) supports cross-species comparisons to advance translational neuroscience research.

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bioRxiv2024

Brain structure and activity predicting cognitive maturation in adolescence

Cognitive abilities of primates, including humans, continue to improve through adolescence 1,2. While a range of changes in brain structure and connectivity have been documented 3,4, how they affect neuronal activity that ultimately determines performance of cognitive functions remains unknown. Here, we conducted a multilevel longitudinal study of monkey adolescent neurocognitive development. The developmental trajectory of neural activity in the prefrontal cortex accounted remarkably well for working memory improvements. While complex aspects of activity changed progressively during adolescence, such as the rotation of stimulus representation in multidimensional neuronal space, which has been implicated in cognitive flexibility, even simpler attributes, such as the baseline firing rate in the period preceding a stimulus appearance had predictive power over behavior. Unexpectedly, decreases in brain volume and thickness, which are widely thought to underlie cognitive changes in humans 5 did not predict well the trajectory of neural activity or cognitive performance changes. Whole brain cortical volume in particular, exhibited an increase and reached a local maximum in late adolescence, at a time of rapid behavioral improvement. Maturation of long-distance white matter tracts linking the frontal lobe with areas of the association cortex and subcortical regions best predicted changes in neuronal activity and behavior. Our results provide evidence that optimization of neural activity depending on widely distributed circuitry effects cognitive development in adolescence.

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bioRxiv2024

Mesolimbic local field potentials are modulated by motor control

Background and ObjectivesWhile historically cortico-basal ganglia-thalamocortical loops were believed to process limbic and sensorimotor data in parallel, there is now evidence to suggest that the two information streams can be processed in a single open loop. However, the limbic-motor interface remains insufficiently characterized. We sought to further investigate how extrastriatal regions may regulate motor output by examining electrophysiological activity in these areas during a response inhibition paradigm. MethodsWe recorded local field potentials (LFPs) from epilepsy patients implanted with intracranial depth electrodes for seizure localization purposes. Participants performed the stop-signal task, during which they made speeded choice reactions to "go" stimuli and occasionally inhibited their reactions in the incident of a "stop" signal. To compare power during movement and the absence of movement, we applied a Wilcoxon signed-rank test. Additionally, we performed a linear mixed-effects model to relate power in limbic regions to power in the motor cortex. Finally, we implemented exploratory analyses to identify power differences for correct go versus correct stop trials and for correct stop versus incorrect stop trials using cluster-based permutation testing. Results14 patients participated. A comparison between movement and baseline fixation revealed that motor response is associated with reduced beta (15-35 Hz) power in the amygdala, hippocampus, and motor cortex and reduced gamma (35-100 Hz) power in the amygdala and hippocampus. Moreover, average beta and gamma power in the amygdala and hippocampus during motor execution were positively associated with average beta and gamma power in the motor cortex. Additionally, we identified significant differences between correct go and stop trials in delta (1-4 Hz) power for all three regions and in theta (4-8 Hz) power for the amygdala and motor cortex. Likewise, we identified significant differences between correct and incorrect stop trials in delta power for the hippocampus and motor cortex, in theta power for the motor cortex, in alpha (8-15 Hz) and beta power for the amygdala and motor cortex, and in gamma power for all three areas. DiscussionThese correlations between neural oscillations in the hippocampus and amygdala and movement strengthen the notion of mesolimbic modulation of motor activity.

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bioRxiv2024

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

The progress of developing an effective closed-loop neuromodulation system for many neurological pathologies is hindered by the difficulties in accurately capturing a useful representation of a brains instantaneous functional state. Existing approaches rely on expert labeling of electroencephalography data to develop biomarkers of neurophysiological pathology. These techniques do not capture the highly complex functional states of the brain that are presumed to exist between labeled states or allow for the likely possibility of variation among identically labeled states. Thus, we propose BrainState, a self-supervised technique to model an arbitrarily complex instantaneous functional state of a brain using neural multivariate timeseries data. Application of BrainState to intracranial electroencephalography data from patients with epilepsy was able to capture diverse pre-seizure states and quantify nuanced effects of neuromodulation. We anticipate that BrainState will enable the development of sophisticated closed-loop neuromodulation systems for a diverse array of neurological pathologies.

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bioRxiv2024

Reward Circuit Local Field Potential Modulations Precede Risk Taking

Risk taking behavior is a symptom of multiple neuropsychiatric disorders and often lacks effective treatments. Reward circuitry regions including the amygdala, orbitofrontal cortex, insula, and anterior cingulate have been implicated in risk-taking by neuroimaging studies. Electrophysiological activity associated with risk taking in these regions is not well understood in humans. Further characterizing the neural signalling that underlies risk-taking may provide therapeutic insight into disorders associated with risk-taking. Eleven patients with pharmacoresistant epilepsy who underwent stereotactic electroencephalography with electrodes in the amygdala, orbitofrontal cortex, insula, and/or anterior cingulate participated. Patients participated in a gambling task where they wagered on a visible playing card being higher than a hidden card, betting $5 or $20 on this outcome, while local field potentials were recorded from implanted electrodes. We used cluster-based permutation testing to identify reward prediction error signals by comparing oscillatory power following unexpected and expected rewards. We also used cluster-based permutation testing to compare power preceding high and low bets in high-risk (<50% chance of winning) trials and two-way ANOVA with bet and risk level to identify signals associated with risky, risk averse, and optimized decisions. We used linear mixed effects models to evaluate the relationship between reward prediction error and risky decision signals across trials, and a linear regression model for associations between risky decision signal power and Barratt Impulsiveness Scale scores for each patient. Reward prediction error signals were identified in the amygdala (p=0.0066), anterior cingulate (p=0.0092), and orbitofrontal cortex (p=6.0E-4, p=4.0E-4). Risky decisions were predicted by increased oscillatory power in high-gamma frequency range during card presentation in the orbitofrontal cortex (p=0.0022), and by increased power following bet cue presentation across the theta-to-beta range in the orbitofrontal cortex (p=0.0022), high-gamma in the anterior cingulate (p=0.0004), and high-gamma in the insula (p=0.0014). Risk averse decisions were predicted by decreased orbitofrontal cortex gamma power (p=2.0E-4). Optimized decisions that maximized earnings were preceded by decreases within the theta to beta range in orbitofrontal cortex (p=2.0E-4), broad frequencies in amygdala (p=2.0E-4), and theta to low-gamma in insula (p=4.0E-4). Insula risky decision power was associated with orbitofrontal cortex high-gamma reward prediction error signal (p=0.0048) and with patient impulsivity (p=0.00478). Our findings identify and help characterize reward circuitry activity predictive of risk-taking in humans. These findings may serve as potential biomarkers to inform the development of novel treatment strategies such as closed loop neuromodulation for disorders of risk taking.

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