Beta activity in human anterior cingulate cortex mediates reward biases.

The rewards that we get from our choices and actions can have a major influence on our future behavior. Understanding how reward biasing of behavior is implemented in the brain is important for many reasons, including the fact that diminution in reward biasing is a hallmark of clinical depression. We hypothesized that reward biasing is mediated by the anterior cingulate cortex (ACC), a cortical hub region associated with the integration of reward and executive control and with the etiology of depression. To test this hypothesis, we recorded neural activity during a biased judgment task in patients undergoing intracranial monitoring for either epilepsy or major depressive disorder. We found that beta (12-30 Hz) oscillations in the ACC predicted both associated reward and the size of the choice bias, and also tracked reward receipt, thereby predicting bias on future trials. We found reduced magnitude of bias in depressed patients, in whom the beta-specific effects were correspondingly reduced. Our findings suggest that ACC beta oscillations may orchestrate the learning of reward information to guide adaptive choice, and, more broadly, suggest a potential biomarker for anhedonia and point to future development of interventions to enhance reward impact for therapeutic benefit.

Insula uses overlapping codes for emotion in self and others.

In daily life, we must recognize others' emotions so we can respond appropriately. This ability may rely, at least in part, on neural responses similar to those associated with our own emotions. We hypothesized that the insula, a cortical region near the junction of the temporal, parietal, and frontal lobes, may play a key role in this process. We recorded local field potential (LFP) activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one on identifying facial emotional responses in others. We found matching patterns of gamma- and high-gamma band activity for the two tasks in the insula. Three other regions (MTL, ACC, and OFC) clearly encoded both self- and other-emotions, but used orthogonal activity patterns to do so. These results support the hypothesis that the insula plays a particularly important role in mediating between experienced vs. observed emotions.

Stereo-EEG-guided network modulation for psychiatric disorders: Surgical considerations.

BACKGROUND: Deep brain stimulation (DBS) and other neuromodulatory techniques are being increasingly utilized to treat refractory neurologic and psychiatric disorders. OBJECTIVE: /Hypothesis: To better understand the circuit-level pathophysiology of treatment-resistant depression (TRD) and treat the network-level dysfunction inherent to this challenging disorder, we adopted an approach of inpatient intracranial monitoring borrowed from the epilepsy surgery field. METHODS: We implanted 3 patients with 4 DBS leads (bilateral pair in both the ventral capsule/ventral striatum and subcallosal cingulate) and 10 stereo-electroencephalography (sEEG) electrodes targeting depression-relevant network regions. For surgical planning, we used an interactive, holographic visualization platform to appreciate the 3D anatomy and connectivity. In the initial surgery, we placed the DBS leads and sEEG electrodes using robotic stereotaxy. Subjects were then admitted to an inpatient monitoring unit for depression-specific neurophysiological assessments. Following these investigations, subjects returned to the OR to remove the sEEG electrodes and internalize the DBS leads to implanted pulse generators. RESULTS: Intraoperative testing revealed positive valence responses in all 3 subjects that helped verify targeting. Given the importance of the network-based hypotheses we were testing, we required accurate adherence to the surgical plan (to engage DBS and sEEG targets) and stability of DBS lead rotational position (to ensure that stimulation field estimates of the directional leads used during inpatient monitoring were relevant chronically), both of which we confirmed (mean radial error 1.2±0.9 mm; mean rotation 3.6±2.6°). CONCLUSION: This novel hybrid sEEG-DBS approach allows detailed study of the neurophysiological substrates of complex neuropsychiatric disorders.

Brain mechanisms underlying the emotion processing bias in treatment-resistant depression.

Depression is associated with a cognitive bias towards negative information and away from positive information. This biased emotion processing may underlie core depression symptoms, including persistent feelings of sadness or low mood and a reduced capacity to experience pleasure. The neural mechanisms responsible for this biased emotion processing remain unknown. Here, we had a unique opportunity to record stereotactic electroencephalography (sEEG) signals in the amygdala and prefrontal cortex (PFC) from 5 treatment-resistant depression (TRD) patients and 12 epilepsy patients (as control) while they participated in an affective bias task in which happy and sad faces were rated. First, compared with the control group, patients with TRD showed increased amygdala responses to sad faces in the early stage (around 300 ms) and decreased amygdala responses to happy faces in the late stage (around 600 ms) following the onset of faces. Further, during the late stage of happy face processing, alpha-band activity in PFC as well as alpha-phase locking between the amygdala and PFC were significantly greater in TRD patients compared to the controls. Second, after deep brain stimulation (DBS) delivered to bilateral subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS), atypical amygdala and PFC processing of happy faces in TRD patients remitted toward the normative pattern. The increased amygdala activation during the early stage of sad face processing suggests an overactive bottom-up processing system in TRD. Meanwhile, the reduced amygdala response during the late stage of happy face processing could be attributed to inhibition by PFC through alpha-band oscillation, which can be released by DBS in SCC and VC/VS.

Learning-Based IRS-Assisted Secure Transmission for Mine IoTs.

Mine Internet of Things (MIoT) devices in intelligent mines often face substantial signal attenuation due to challenging operating conditions. The openness of wireless communication also makes it susceptible to smart attackers, such as active eavesdroppers. The attackers can disrupt equipment operations, compromise production safety, and exfiltrate sensitive environmental data. To address these challenges, we propose an intelligent reflecting surface (IRS)-assisted secure transmission system for an MIoT device which enhances the security and reliability of wireless communication in challenging mining environments. We develop a joint optimization problem for the IRS phase shifts and transmit power, with the goal of enhancing legitimate transmission while suppressing eavesdropping. To accommodate time-varying channel conditions, we propose a reinforcement learning (RL)-based IRS-assisted secure transmission scheme that enables MIoT device to optimize both the IRS reflecting coefficients and transmit power for optimal transmission policy in dynamic environments. We adopt the deep deterministic policy gradient (DDPG) algorithm to explore the optimal transmission policy in continuous space. This can reduce the discretization error caused by traditional RL methods. The simulation results indicate that our proposed scheme achieves superior system utility compared with both the IRS-free (IF) scheme and the IRS randomly configured (IRC) scheme. These results demonstrate the effectiveness and practical relevance of our contributions, proving that implementing IRS in MIoT wireless communication can enhance safety, security, and efficiency in the mining industry.

Decoding Depression Severity From Intracranial Neural Activity.

BACKGROUND: Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis. METHODS: We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings. RESULTS: Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression. CONCLUSIONS: The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.

Risk of Severe COVID-19 Disease and the Pandemic's Impact on Service Utilization Among a Longitudinal Cohort of Persons with HIV-Washington, DC.

People with HIV (PWH) have a high burden of medical comorbidities, potentially putting them at increased risk for severe COVID-19. Additionally, during the COVID-19 pandemic, HIV care delivery has been restructured and the impact on HIV outcomes is unknown. The objectives of this study were first, to examine the risk of severe COVID-19 among PWH, using a definition incorporating clinical risk factors, and second, to examine the pandemic's impact on HIV care. We used data from the DC Cohort, a large cohort of people receiving HIV care in Washington, DC. We found that a high proportion of participants across all age groups qualified as increased (58%) or high risk (34%) for severe COVID-19. Between 2019 and 2020, encounters increased (17.7%, increasing to 23.5% of active DC Cohort participants had an encounter) while laboratory utilization decreased (14.4%, decreasing to 11.4% of active DC Cohort participants had an HIV RNA test performed). Implications of our work include the importance of protecting vulnerable people with HIV from acquiring COVID-19 and potentially manifesting severe complications through strategies including vaccination. Additionally, acknowledging that HIV service delivery will likely be changed long-term by the pandemic, adaptation is required to ensure continued progress towards 90-90-90 goals.

Short Communication:Outcomes of Dual Versus Triple Antiretroviral Drug Regimens Among Virally Suppressed Adults in the DC Cohort.

This study explored virological outcomes of two-drug (2DRs) and three-drug (3DRs) antiretroviral regimens in adults with HIV in the DC Cohort. We analyzed 310 treatment-experienced adults with sustained HIV RNA ≤50 copies/mL at baseline, 53 of whom switched to 2DRs and 257 continued 3DRs. Adults on 2DRs and 3DRs had similar demographics (median age 53.3 years, 76.8% cisgender male, 76.1% Black). Adults on 2DRs had more participants with ≥2 comorbidities (62.3% vs. 42.8%, p = .019), had a longer time since HIV diagnosis (median years 20.4 vs. 13.2, p = .017), and received the regimen of interest for a shorter duration (median years 1.3 vs. 3.3, p < .001) compared with adults on 3DRs. Adults receiving 2DRs had a higher, although nonsignificant, risk for virological failure (two consecutive HIV RNA ≥50 copies/mL) at 24 months follow-up than adults on 3DRs (6.7% vs. 1.7%, respectively; p = .10). Future analysis of the effectiveness of 2DRs is needed.

Deep Brain Stimulation for Depression Informed by Intracranial Recordings.

The success of deep brain stimulation (DBS) for treating Parkinson's disease has led to its application to several other disorders, including treatment-resistant depression. Results with DBS for treatment-resistant depression have been heterogeneous, with inconsistencies largely driven by incomplete understanding of the brain networks regulating mood, especially on an individual basis. We report results from the first subject treated with DBS for treatment-resistant depression using an approach that incorporates intracranial recordings to personalize understanding of network behavior and its response to stimulation. These recordings enabled calculation of individually optimized DBS stimulation parameters using a novel inverse solution approach. In the ensuing double-blind, randomized phase incorporating these bespoke parameter sets, DBS led to remission of symptoms and dramatic improvement in quality of life. Results from this initial case demonstrate the feasibility of this personalized platform, which may be used to improve surgical neuromodulation for a vast array of neurologic and psychiatric disorders.

Brain cell type-specific enhancer-promoter interactome maps and disease-risk association.

Noncoding genetic variation is a major driver of phenotypic diversity, but functional interpretation is challenging. To better understand common genetic variation associated with brain diseases, we defined noncoding regulatory regions for major cell types of the human brain. Whereas psychiatric disorders were primarily associated with variants in transcriptional enhancers and promoters in neurons, sporadic Alzheimer's disease (AD) variants were largely confined to microglia enhancers. Interactome maps connecting disease-risk variants in cell-type-specific enhancers to promoters revealed an extended microglia gene network in AD. Deletion of a microglia-specific enhancer harboring AD-risk variants ablated BIN1 expression in microglia, but not in neurons or astrocytes. These findings revise and expand the list of genes likely to be influenced by noncoding variants in AD and suggest the probable cell types in which they function.

The combined administration of vancomycin IV, standard prophylactic antibiotics, and vancomycin powder in spinal instrumentation surgery: does the routine use affect infection rates and bacterial resistance?

BACKGROUND: Surgical site infections (SSI) poses significant risk following spinal instrumentation surgery. The 2013 North American Spine Society (NASS) Evidence-Based Clinical Guidelines found that the incidence of SSI in spine surgery ranged from 0.7-10%, with higher rates with medical comorbidities. National guidelines currently recommend first-generation cephalosporins as first line prophylaxis. Due to an increase in MRSA cases in our institution, a combined antibiotic strategy using vancomycin IV, standard prophylactic antibiotics, and vancomycin powder was implemented for all spinal instrumentation surgeries. METHODS: All spinal instrumentation surgeries performed at this institution from 2013-2016 were identified. Chart review was then performed to identify the inclusion and exclusion criteria, demographic data, diagnosis, type of surgery performed, and bacterial culture results. Rates of SSI, as defined by the Center for Disease Control (CDC), were calculated and antibiotic resistance was determined. As control, SSIs were identified and reviewed from 2010, prior to the implementation of the combined strategy. RESULTS: One thousand and seventy four subjects were identified in the combined cohort. Mean age was 52.3 years, 540 males (50.2%), 534 females (49.8%). There were 960 primary surgeries (89.4%), 114 cases revision surgeries (10.6%). Cervical myelopathy (27.9%), lumbar stenosis (16.2%), lumbar spondylolisthesis (14.0%), and scoliosis (pediatric and adult)/deformity (13.7%) were leading diagnoses. The standard prophylactic antibiotic was cefazolin IV in 524 cases (48.8%), gentamicin IV in 526 cases (49.0%), vancomycin powder was used in 72.3% of cases. Four SSI cases out of 1,074 were identified (0.37%), 3 deep and 1 superficial, with no antibiotic resistance. In the control group, there were 11 infections of 892 cases (1.23%). There were significantly lower rates of SSI in the combined group versus control (P=0.05). CONCLUSIONS: The combined antibiotic strategy led to low SSI rates in this retrospective case control study. Limitations of this study include retrospective design and small sample size. A large multicenter randomized clinical trial may provide further insight in the effectiveness of this strategy. Level of evidence 3. Clinical relevance: the combined antibiotic protocol may be considered in institutions with concern for SSI and methicillin resistant infections associated with spinal instrumentation surgeries.

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study.

OBJECTIVES: An estimated 25% of type two diabetes mellitus (DM2) patients in the United States are undiagnosed due to inadequate screening, because it is prohibitive to administer laboratory tests to everyone. We assess whether electronic health record (EHR) phenotyping could improve DM2 screening compared to conventional models, even when records are incomplete and not recorded systematically across patients and practice locations, as is typically seen in practice. METHODS: In this cross-sectional, retrospective study, EHR data from 9948 US patients were used to develop a pre-screening tool to predict current DM2, using multivariate logistic regression and a random-forests probabilistic model for out-of-sample validation. We compared (1) a full EHR model containing commonly prescribed medications, diagnoses (as ICD9 categories), and conventional predictors, (2) a restricted EHR DX model which excluded medications, and (3) a conventional model containing basic predictors and their interactions (BMI, age, sex, smoking status, hypertension). RESULTS: Using a patient's full EHR or restricted EHR was superior to using basic covariates alone for detecting individuals with diabetes (hierarchical X(2) test, p<0.001). Migraines, depot medroxyprogesterone acetate, and cardiac dysrhythmias were associated negatively with DM2, while sexual and gender identity disorder diagnosis, viral and chlamydial infections, and herpes zoster were associated positively. Adding EHR phenotypes improved classification; the AUC for the full EHR Model, EHR DX model, and conventional model using logistic regression, were 84.9%, 83.2%, and 75.0% respectively. For random forest machine learning out-of-sample prediction, accuracy also was improved when using EHR phenotypes; the AUC values were 81.3%, 79.6%, and 74.8%, respectively. Improved AUCs reflect better performance for most thresholds that balance sensitivity and specificity. CONCLUSIONS: EHR phenotyping resulted in markedly superior detection of DM2, even in the face of missing and unsystematically recorded data, based on the ROC curves. EHR phenotypes could more efficiently identify which patients do require, and don't require, further laboratory screening. When applied to the current number of undiagnosed individuals in the United States, we predict that incorporating EHR phenotype screening would identify an additional 400,000 patients with active, untreated diabetes compared to the conventional pre-screening models.