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.

Brain-wide human oscillatory local field potential activity during visual working memory.

Oscillatory activity in the local field potential (LFP) is thought to be a marker of cognitive processes. To understand how it differentiates tasks and brain areas in humans, we recorded LFPs in 15 adults with intracranial depth electrodes, as they performed visual-spatial and shape working memory tasks. Stimulus appearance produced widespread, broad-band activation, including in occipital, parietal, temporal, insular, and prefrontal cortex, and the amygdala and hippocampus. Occipital cortex was characterized by most elevated power in the high-gamma (100-150 Hz) range during the visual stimulus presentation. The most consistent feature of the delay period was a systematic pattern of modulation in the beta frequency (16-40 Hz), which included a decrease in power of variable timing across areas, and rebound during the delay period. These results reveal the widespread nature of oscillatory activity across a broad brain network and region-specific signatures of oscillatory processes associated with visual working memory.

Brain-wide human oscillatory LFP activity during visual working memory.

Oscillatory activity is thought to be a marker of cognitive processes, although its role and distribution across the brain during working memory has been a matter of debate. To understand how oscillatory activity differentiates tasks and brain areas in humans, we recorded local field potentials (LFPs) in 12 adults as they performed visual-spatial and shape-matching memory tasks. Tasks were designed to engage working memory processes at a range of delay intervals between stimulus delivery and response initiation. LFPs were recorded using intracranial depth electrodes implanted to localize seizures for management of intractable epilepsy. Task-related LFP power analyses revealed an extensive network of cortical regions that were activated during the presentation of visual stimuli and during their maintenance in working memory, including occipital, parietal, temporal, insular, and prefrontal cortical areas, and subcortical structures including the amygdala and hippocampus. Across most brain areas, the appearance of a stimulus produced broadband power increase, while gamma power was evident during the delay interval of the working memory task. Notable differences between areas included that occipital cortex was characterized by elevated power in the high gamma (100-150 Hz) range during the 500 ms of visual stimulus presentation, which was less pronounced or absent in other areas. A decrease in power centered in beta frequency (16-40 Hz) was also observed after the stimulus presentation, whose magnitude differed across areas. These results reveal the interplay of oscillatory activity across a broad network, and region-specific signatures of oscillatory processes associated with visual working memory.

Challenges of Delirium Management in Patients with Traumatic Brain Injury: From Pathophysiology to Clinical Practice.

Traumatic brain injury (TBI) can initiate a very complex disease of the central nervous system (CNS), starting with the primary pathology of the inciting trauma and subsequent inflammatory and CNS tissue response. Delirium has long been regarded as an almost inevitable consequence of moderate to severe TBI, but more recently has been recognized as an organ dysfunction syndrome with potentially mitigating interventions. The diagnosis of delirium is independently associated with prolonged hospitalization, increased mortality and worse cognitive outcome across critically ill populations. Investigation of the unique problems and management challenges of TBI patients is needed to reduce the burden of delirium in this population. In this narrative review, possible etiologic mechanisms behind post-traumatic delirium are discussed, including primary injury to structures mediating arousal and attention and secondary injury due to progressive inflammatory destruction of the brain parenchyma. Other potential etiologic contributors include dysregulation of neurotransmission due to intravenous sedatives, seizures, organ failure, sleep cycle disruption or other delirium risk factors. Delirium screening can be accomplished in TBI patients and the presence of delirium portends worse outcomes. There is evidence that multi-component care bundles including an analgesia-prioritized sedation algorithm, regular spontaneous awakening and breathing trials, protocolized delirium assessment, early mobility and family engagement can reduce the burden of ICU delirium. The aim of this review is to summarize the approach to delirium in TBI patients with an emphasis on pathogenesis and management. Emerging CNS-active drug therapies that show promise in preclinical studies are highlighted.

IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit.

OBJECTIVE: New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification. METHODS: IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application. RESULTS: Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%. CONCLUSION: This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers. SIGNIFICANCE: This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.