Effects of Chronic Pain Diagnoses on the Antidepressant Efficacy of Transcranial Magnetic Stimulation.

OBJECTIVE: Major depressive disorder (MDD) and chronic pain are highly comorbid and bidirectionally related. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex is effective in treating MDD, but additional research is needed to determine if chronic pain interferes with rTMS for MDD. METHODS: Participants were 124 veterans ( Mage = 49.14, SD = 13.83) scheduled for 30 sessions of rTMS across 6 weeks. Depression severity was monitored weekly using the Patient Health Questionnaire-9 (PHQ-9). Having any pain diagnosis, low back pain, or headache/migraine were assessed by chart review. We fit latent basis models to estimate total change by pain diagnosis in depression scores and quadratic latent growth models to examine differences in growth rates. Then, we computed χ2 tests of group differences in response (PHQ-9 reduction ≥50%) and remission rates (final PHQ-9 < 5). RESULTS: A total of 92 participants (74%) had a documented pain diagnosis, 58 (47%) had low back pain, and 32 (26%) had headache/migraine. In growth models, depression scores initially decreased (linear slope estimate = -2.04, SE = 0.26, p < .0001), but the rate of decrease slowed over time (quadratic slope estimate = 0.18, SE = 0.04, p < .001). Overall change was not different as a function of any pain diagnosis ( p = .42), low back pain (p = .11 ), or headache/migraine ( p = .28). However, we found that low back pain was a negative predictor of response ( p = .032). CONCLUSIONS: These data support rTMS as a viable treatment option for comorbid populations. Although patients with comorbid chronic pain conditions are likely to receive benefit from rTMS for depression, adjunctive pain treatment may be indicated.

Distinct neural activations correlate with maximization of reward magnitude versus frequency.

Choice selection strategies and decision-making are typically investigated using multiple-choice gambling paradigms that require participants to maximize expected value of rewards. However, research shows that performance in such paradigms suffers from individual biases towards the frequency of gains such that users often choose smaller frequent gains over larger rarely occurring gains, also referred to as melioration. To understand the basis of this subjective tradeoff, we used a simple 2-choice reward task paradigm in 186 healthy human adult subjects sampled across the adult lifespan. Cortical source reconstruction of simultaneously recorded electroencephalography suggested distinct neural correlates for maximizing reward magnitude versus frequency. We found that activations in the parahippocampal and entorhinal areas, which are typically linked to memory function, specifically correlated with maximization of reward magnitude. In contrast, maximization of reward frequency was correlated with activations in the lateral orbitofrontal cortices and operculum, typical areas involved in reward processing. These findings reveal distinct neural processes serving reward frequency versus magnitude maximization that can have clinical translational utility to optimize decision-making.

Modulation of ventromedial orbitofrontal cortical glutamatergic activity affects the explore-exploit balance and influences value-based decision-making.

The balance between exploration and exploitation is essential for decision-making. The present study investigated the role of ventromedial orbitofrontal cortex (vmOFC) glutamate neurons in mediating value-based decision-making by first using optogenetics to manipulate vmOFC glutamate activity in rats during a probabilistic reversal learning (PRL) task. Rats that received vmOFC activation during informative feedback completed fewer reversals and exhibited reduced reward sensitivity relative to rats. Analysis with a Q-learning computational model revealed that increased vmOFC activity did not affect the learning rate but instead promoted maladaptive exploration. By contrast, vmOFC inhibition increased the number of completed reversals and increased exploitative behavior. In a separate group of animals, calcium activity of vmOFC glutamate neurons was recorded using fiber photometry. Complementing our results above, we found that suppression of vmOFC activity during the latter part of rewarded trials was associated with improved PRL performance, greater win-stay responding and selecting the correct choice on the next trial. These data demonstrate that excessive vmOFC activity during reward feedback disrupted value-based decision-making by increasing the maladaptive exploration of lower-valued options. Our findings support the premise that pharmacological interventions that normalize aberrant vmOFC glutamate activity during reward feedback processing may attenuate deficits in value-based decision-making.

Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals.

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.

Theta Burst Stimulation Is Not Inferior to High-Frequency Repetitive Transcranial Magnetic Stimulation in Reducing Symptoms of Posttraumatic Stress Disorder in Veterans With Depression: A Retrospective Case Series.

OBJECTIVES: Two commonly used forms of repetitive transcranial magnetic stimulation (rTMS) were recently shown to be equivalent for the treatment of depression: high-frequency stimulation (10 Hz), a protocol that lasts between 19 and 38 minutes, and intermittent theta burst stimulation (iTBS), a protocol that can be delivered in just three minutes. However, it is unclear whether iTBS treatment offers the same benefits as those of standard 10-Hz rTMS for comorbid symptoms such as those seen in posttraumatic stress disorder (PTSD). MATERIALS AND METHODS: In this retrospective case series, we analyzed treatment outcomes in veterans from the Veterans Affairs San Diego Healthcare System who received 10-Hz (n = 47) or iTBS (n = 51)-rTMS treatments for treatment-resistant depression between February 2018 and June 2022. We compared outcomes between these two stimulation protocols in symptoms of depression (using changes in the Patient Health Questionnaire-9 [PHQ-9]) and PTSD (using changes in the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, or Patient Checklist [PCL]-5). RESULTS: There was an imbalance of sex between groups (p < 0.05). After controlling for sex, we found no significant difference by stimulation protocol for depression (PHQ-9, F [1,94] = 0.16, p = 0.69, eta-squared = 0.002), confirming the original study previously noted. We also showed no difference by stimulation protocol of changes in PTSD symptoms (PCL-5, F [1,94] = 3.46, p = 0.067, eta-squared = 0.036). The iTBS group showed a decrease from 41.9 ± 4.4 to 25.1 ± 4.9 (a difference of 16.8 points) on the PCL-5 scale whereas the 10-Hz group showed a decrease from 43.6 ± 2.9 to 35.2 ± 3.2 on this scale (a difference of 8.4 points). Follow-up analyses restricting the sample in various ways did not meaningfully change these results (no follow-up analyses showed that there was a significant difference between stimulation protocols). CONCLUSIONS: Although limited by small sample size, nonblind, and pseudorandomized assignment, our data suggest that iTBS is similar to 10-Hz stimulation in inducing reductions in PTSD symptoms and depression in military veterans.

Repetitive Transcranial Magnetic Stimulation for Depression and Posttraumatic Stress Disorder in Veterans With Mild Traumatic Brain Injury.

OBJECTIVES: Mild traumatic brain injury (mTBI) is a signature injury of military conflicts and is prevalent in veterans with major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). Although therapeutic transcranial magnetic stimulation (TMS) can reduce symptoms of depression and PTSD, whether traumatic brain injury (TBI) affects TMS responsiveness is not yet known. We hypothesized mTBI would be associated with higher pretreatment symptom burden and poorer TMS response. MATERIALS AND METHODS: We investigated a registry of veterans (N = 770) who received TMS for depression across the US Veterans Affairs system. Of these, 665 (86.4%) had data on TBI and lifetime number of head injuries while 658 had complete data related to depression outcomes. Depression symptoms were assessed using the nine-item Patient Health Questionnaire and PTSD symptoms using the PTSD Checklist for DSM-5. Linear mixed effects models and t-tests evaluated whether head injuries predicted symptom severity before treatment, and how TBI status affected clinical TMS outcomes. RESULTS: Of the 658 veterans included, 337 (50.7%) reported previous mTBI, with a mean of three head injuries (range 1-20). TBI status did not predict depressive symptom severity or TMS-associated changes in depression (all p's > 0.1). TBI status was associated with a modest attenuation of TMS-associated improvement in PTSD (in patients with PTSD Checklist for DSM-5 scores > 33). There was no correlation between the number of head injuries and TMS response (p > 0.1). CONCLUSIONS: Contrary to our hypothesis, presence of mTBI did not meaningfully change TMS outcomes. Veterans with mTBI had greater PTSD symptoms, yet neither TBI status nor cumulative head injuries reduced TMS effectiveness. Limitations include those inherent to retrospective registry studies and self-reporting. Although these findings are contrary to our hypotheses, they support the safety and effectiveness of TMS for MDD and PTSD in patients who have comorbid mTBI.

Autism-linked Cullin3 germline haploinsufficiency impacts cytoskeletal dynamics and cortical neurogenesis through RhoA signaling.

E3-ubiquitin ligase Cullin3 (Cul3) is a high confidence risk gene for autism spectrum disorder (ASD) and developmental delay (DD). To investigate how Cul3 mutations impact brain development, we generated a haploinsufficient Cul3 mouse model using CRISPR/Cas9 genome engineering. Cul3 mutant mice exhibited social and cognitive deficits and hyperactive behavior. Brain MRI found decreased volume of cortical regions and changes in many other brain regions of Cul3 mutant mice starting from early postnatal development. Spatiotemporal transcriptomic and proteomic profiling of embryonic, early postnatal and adult brain implicated neurogenesis and cytoskeletal defects as key drivers of Cul3 functional impact. Specifically, dendritic growth, filamentous actin puncta, and spontaneous network activity were reduced in Cul3 mutant mice. Inhibition of small GTPase RhoA, a molecular substrate of Cul3 ligase, rescued dendrite length and network activity phenotypes. Our study identified defects in neuronal cytoskeleton and Rho signaling as the primary targets of Cul3 mutation during brain development.

Utility of Cognitive Neural Features for Predicting Mental Health Behaviors.

Cognitive dysfunction underlies common mental health behavioral symptoms including depression, anxiety, inattention, and hyperactivity. In this study of 97 healthy adults, we aimed to classify healthy vs. mild-to-moderate self-reported symptoms of each disorder using cognitive neural markers measured with an electroencephalography (EEG). We analyzed source-reconstructed EEG data for event-related spectral perturbations in the theta, alpha, and beta frequency bands in five tasks, a selective attention and response inhibition task, a visuospatial working memory task, a Flanker interference processing task, and an emotion interference task. From the cortical source activation features, we derived augmented features involving co-activations between any two sources. Logistic regression on the augmented feature set, but not the original feature set, predicted the presence of psychiatric symptoms, particularly for anxiety and inattention with >80% sensitivity and specificity. We also computed current flow closeness and betweenness centralities to identify the "hub" source signal predictors. We found that the Flanker interference processing task was the most useful for assessing the connectivity hubs in general, followed by the inhibitory control go-nogo paradigm. Overall, these interpretable machine learning analyses suggest that EEG biomarkers collected on a rapid suite of cognitive assessments may have utility in classifying diverse self-reported mental health symptoms.

Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling.

Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors.

Mapping cognitive brain functions at scale.

A fundamental set of cognitive abilities enable humans to efficiently process goal-relevant information, suppress irrelevant distractions, maintain information in working memory, and act flexibly in different behavioral contexts. Yet, studies of human cognition and their underlying neural mechanisms usually evaluate these cognitive constructs in silos, instead of comprehensively in-tandem within the same individual. Here, we developed a scalable, mobile platform, "BrainE" (short for Brain Engagement), to rapidly assay several essential aspects of cognition simultaneous with wireless electroencephalography (EEG) recordings. Using BrainE, we rapidly assessed five aspects of cognition including (1) selective attention, (2) response inhibition, (3) working memory, (4) flanker interference and (5) emotion interference processing, in 102 healthy young adults. We evaluated stimulus encoding in all tasks using the EEG neural recordings, and isolated the cortical sources of the spectrotemporal EEG dynamics. Additionally, we used BrainE in a two-visit study in 24 young adults to investigate the reliability of the neuro-cognitive data as well as its plasticity to transcranial magnetic stimulation (TMS). We found that stimulus encoding on multiple cognitive tasks could be rapidly assessed, identifying common as well as distinct task processes in both sensory and cognitive control brain regions. Event related synchronization (ERS) in the theta (3-7 Hz) and alpha (8-12 Hz) frequencies as well as event related desynchronization (ERD) in the beta frequencies (13-30 Hz) were distinctly observed in each task. The observed ERS/ERD effects were overall anticorrelated. The two-visit study confirmed high test-retest reliability for both cognitive and neural data, and neural responses showed specific TMS protocol driven modulation. We also show that the global cognitive neural responses are sensitive to mental health symptom self-reports. This first study with the BrainE platform showcases its utility in studying neuro-cognitive dynamics in a rapid and scalable fashion.

Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation.

Despite many prior studies demonstrating offline behavioral gains in motor skills after sleep, the underlying neural mechanisms remain poorly understood. To investigate the neurophysiological basis for offline gains, we performed single-unit recordings in motor cortex as rats learned a skilled upper-limb task. We found that sleep improved movement speed with preservation of accuracy. These offline improvements were linked to both replay of task-related ensembles during non-rapid eye movement (NREM) sleep and temporal shifts that more tightly bound motor cortical ensembles to movements; such offline gains and temporal shifts were not evident with sleep restriction. Interestingly, replay was linked to the coincidence of slow-wave events and bursts of spindle activity. Neurons that experienced the most consistent replay also underwent the most significant temporal shift and binding to the motor task. Significantly, replay and the associated performance gains after sleep only occurred when animals first learned the skill; continued practice during later stages of learning (i.e., after motor kinematics had stabilized) did not show evidence of replay. Our results highlight how replay of synchronous neural activity during sleep mediates large-scale neural plasticity and stabilizes kinematics during early motor learning.

Robust neuroprosthetic control from the stroke perilesional cortex.

Intracortical brain-machine interfaces (BMIs) may eventually restore function in those with motor disability after stroke. However, current research into the development of intracortical BMIs has focused on subjects with largely intact cortical structures, such as those with spinal cord injury. Although the stroke perilesional cortex (PLC) has been hypothesized as a potential site for a BMI, it remains unclear whether the injured motor cortical network can support neuroprosthetic control directly. Using chronic electrophysiological recordings in a rat stroke model, we demonstrate here the PLC's capacity for neuroprosthetic control and physiological plasticity. We initially found that the perilesional network demonstrated abnormally increased slow oscillations that also modulated neural firing. Despite these striking abnormalities, neurons in the perilesional network could be modulated volitionally to learn neuroprosthetic control. The rate of learning was surprisingly similar regardless of the electrode distance from the stroke site and was not significantly different from intact animals. Moreover, neurons achieved similar task-related modulation and, as an ensemble, formed cell assemblies with learning. Such control was even achieved in animals with poor motor recovery, suggesting that neuroprosthetic control is possible even in the absence of motor recovery. Interestingly, achieving successful control also reduced locking to abnormal oscillations significantly. Our results thus suggest that, despite the disrupted connectivity in the PLC, it may serve as an effective target for neuroprosthetic control in those with poor motor recovery after stroke.

Cholinergic systems are essential for late-stage maturation and refinement of motor cortical circuits.

Previous studies reported that early postnatal cholinergic lesions severely perturb early cortical development, impairing neuronal cortical migration and the formation of cortical dendrites and synapses. These severe effects of early postnatal cholinergic lesions preclude our ability to understand the contribution of cholinergic systems to the later-stage maturation of topographic cortical representations. To study cholinergic mechanisms contributing to the later maturation of motor cortical circuits, we first characterized the temporal course of cortical motor map development and maturation in rats. In this study, we focused our attention on the maturation of cortical motor representations after postnatal day 25 (PND 25), a time after neuronal migration has been accomplished and cortical volume has reached adult size. We found significant maturation of cortical motor representations after this time, including both an expansion of forelimb representations in motor cortex and a shift from proximal to distal forelimb representations to an extent unexplainable by simple volume enlargement of the neocortex. Specific cholinergic lesions placed at PND 24 impaired enlargement of distal forelimb representations in particular and markedly reduced the ability to learn skilled motor tasks as adults. These results identify a novel and essential role for cholinergic systems in the late refinement and maturation of cortical circuits. Dysfunctions in this system may constitute a mechanism of late-onset neurodevelopmental disorders such as Rett syndrome and schizophrenia.

Neural activity during inhibitory control predicts suicidal ideation with machine learning.

Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (n = 38 each), who completed a neuro-cognitive assessment session synchronized with electroencephalography (EEG). SI+/- groups were matched for age, sex, and mental health symptoms of depression and anxiety. EEG was recorded at rest and while subjects engaged in four cognitive tasks of inhibitory control, interference processing, working memory, and emotion bias. We parsed EEG signals in physiologically relevant theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies and performed cortical source imaging on the neural signals. These data served as SI predictors in ML models. The best ML model was obtained for beta band power during the inhibitory control (IC) task, demonstrating high sensitivity (89%), specificity (98%). Shapley explainer plots further showed top neural predictors as feedback-related power in the visual and posterior default mode networks and response-related power in the ventral attention, fronto-parietal, and sensory-motor networks. We further tested the external validity of the model in an independent clinically depressed sample (n = 35, 12 SI+) that engaged in an adaptive test version of the IC task, demonstrating 50% sensitivity and 61% specificity in this sample. Overall, the study suggests a promising, scalable EEG-based biomarker approach to predict SI that may serve as a target for risk identification and intervention.

This study achieves a high-accuracy machine learning model that can classify an individual as having suicidal ideation or not from source-localized EEG signals captured during an inhibitory control task. In addition, we have identified key brain regions that drive this model.

Methylphenidate, but not citalopram, decreases impulsive choice in rats performing a temporal discounting task.

Introduction: Drugs targeting monoamine systems remain the most common treatment for disorders with impulse control impairments. There is a body of literature suggesting that drugs affecting serotonin reuptake and dopamine reuptake can modulate distinct aspects of impulsivity - though such tests are often performed using distinct behavioral tasks prohibiting easy comparisons. Methods: Here, we directly compare pharmacologic agents that affect dopamine (methylphenidate) vs serotonin (citalopram) manipulations on choice impulsivity in a temporal discounting task where rats could choose between a small, immediate reward or a large reward delayed at either 2 or 10s. In control conditions, rats preferred the large reward at a small (2s) delay and discounted the large reward at a long (10s) delay. Results: Methylphenidate, a dopamine transport inhibitor that blocks reuptake of dopamine, dose-dependently increased large reward preference in the long delay (10s) block. Citalopram, a selective serotonin reuptake inhibitor, had no effect on temporal discounting behavior. Impulsive behavior on the temporal discounting task was at least partially mediated by the nucleus accumbens shell. Bilateral lesions to the nucleus accumbens shell reduced choice impulsivity during the long delay (10s) block. Following lesions, methylphenidate did not impact impulsivity. Discussion: Our results suggest that striatal dopaminergic systems modulate choice impulsivity via actions within the nucleus accumbens shell, whereas serotonin systems may regulate different aspects of behavioral inhibition/impulsivity.

Predicting non-response to ketamine for depression: An exploratory symptom-level analysis of real-world data among military veterans.

Ketamine helps some patients with treatment resistant depression (TRD), but reliable methods for predicting which patients will, or will not, respond to treatment are lacking. Herein, we aim to inform prediction models of non-response to ketamine/esketamine in adults with TRD. This is a retrospective analysis of PHQ-9 item response data from 120 patients with TRD who received repeated doses of intravenous racemic ketamine or intranasal eskatamine in a real-world clinic. Regression models were fit to patients' symptom trajectories, showing that all symptoms improved on average, but depressed mood improved relatively faster than low energy. Principal component analysis revealed a first principal component (PC) representing overall treatment response, and a second PC that reflects variance across affective versus somatic symptom subdomains. We then trained logistic regression classifiers to predict overall response (improvement on PC1) better than chance using patients' baseline symptoms alone. Finally, by parametrically adjusting the classifier decision thresholds, we identified optimal models for predicting non-response with a negative predictive value of over 96 %, while retaining a specificity of 22 %. Thus, we could identify 22 % of patients who would not respond based purely on their baseline symptoms. This approach could inform rational treatment recommendations to avoid additional treatment failures.

Design and Implementation of a Brief Digital Mindfulness and Compassion Training App for Health Care Professionals: Cluster Randomized Controlled Trial.

BACKGROUND: Several studies show that intense work schedules make health care professionals particularly vulnerable to emotional exhaustion and burnout. OBJECTIVE: In this scenario, promoting self-compassion and mindfulness may be beneficial for well-being. Notably, scalable, digital app-based methods may have the potential to enhance self-compassion and mindfulness in health care professionals. METHODS: In this study, we designed and implemented a scalable, digital app-based, brief mindfulness and compassion training program called "WellMind" for health care professionals. A total of 22 adult participants completed up to 60 sessions of WellMind training, 5-10 minutes in duration each, over 3 months. Participants completed behavioral assessments measuring self-compassion and mindfulness at baseline (preintervention), 3 months (postintervention), and 6 months (follow-up). In order to control for practice effects on the repeat assessments and calculate effect sizes, we also studied a no-contact control group of 21 health care professionals who only completed the repeated assessments but were not provided any training. Additionally, we evaluated pre- and postintervention neural activity in core brain networks using electroencephalography source imaging as an objective neurophysiological training outcome. RESULTS: Findings showed a post- versus preintervention increase in self-compassion (Cohen d=0.57; P=.007) and state-mindfulness (d=0.52; P=.02) only in the WellMind training group, with improvements in self-compassion sustained at follow-up (d=0.8; P=.01). Additionally, WellMind training durations correlated with the magnitude of improvement in self-compassion across human participants (ρ=0.52; P=.01). Training-related neurophysiological results revealed plasticity specific to the default mode network (DMN) that is implicated in mind-wandering and rumination, with DMN network suppression selectively observed at the postintervention time point in the WellMind group (d=-0.87; P=.03). We also found that improvement in self-compassion was directly related to the extent of DMN suppression (ρ=-0.368; P=.04). CONCLUSIONS: Overall, promising behavioral and neurophysiological findings from this first study demonstrate the benefits of brief digital mindfulness and compassion training for health care professionals and compel the scale-up of the digital intervention. TRIAL REGISTRATION: Trial Registration: International Standard Randomized Controlled Trial Number Registry ISRCTN94766568, https://www.isrctn.com/ISRCTN94766568.

Behavioral Interventions Can Improve Brain Injury-Induced Deficits in Behavioral Flexibility and Impulsivity Linked to Impaired Reward-Feedback Beta Oscillations.

Traumatic brain injury (TBI) affects a large population, resulting in severe cognitive impairments. Although cognitive rehabilitation is an accepted treatment for some deficits, studies in patients are limited in ability to probe physiological and behavioral mechanisms. Therefore, animal models are needed to optimize strategies. Frontal TBI in a rat model results in robust and replicable cognitive deficits, making this an ideal candidate for investigating various behavioral interventions. In this study, we report three distinct frontal TBI experiments assessing behavior well into the chronic post-injury period using male Long-Evans rats. First, we evaluated the impact of frontal injury on local field potentials recorded simultaneously from 12 brain regions during a probabilistic reversal learning (PbR) task. Next, a set of rats were tested on a similar PbR task or an impulsivity task (differential reinforcement of low-rate behavior [DRL]) and half received salient cues associated with reinforcement contingencies to encourage engagement in the target behavior. After intervention on the PbR task, brains were stained for markers of activity. On the DRL task, cue relevance was decoupled from outcomes to determine if beneficial effects persisted on impulsive behavior. TBI decreased the ability to detect reinforced outcomes; this was evident in task performance and reward-feedback signals occurring at beta frequencies in lateral orbitofrontal cortex (OFC) and associated frontostriatal regions. The behavioral intervention improved flexibility and increased OFC activity. Intervention also reduced impulsivity, even after cues were decoupled, which was partially mediated by improvements in timing behavior. The current study established a platform to begin investigating cognitive rehabilitation in rats and identified a strong role for dysfunctional OFC signaling in probabilistic learning after frontal TBI.

Ventral pallidum GABA and glutamate neurons drive approach and avoidance through distinct modulation of VTA cell types.

The ventral pallidum (VP) contains GABA and glutamate neurons projecting to ventral tegmental area (VTA) whose stimulation drives approach and avoidance, respectively. Yet little is known about the mechanisms by which VP cell types shape VTA activity and drive behavior. Here, we found that both VP GABA and glutamate neurons were activated during approach to reward or by delivery of an aversive stimulus. Stimulation of VP GABA neurons inhibited VTA GABA, but activated dopamine and glutamate neurons. Remarkably, stimulation-evoked activation was behavior-contingent such that VTA recruitment was inhibited when evoked by the subject's own action. Conversely, VP glutamate neurons activated VTA GABA, as well as dopamine and glutamate neurons, despite driving aversion. However, VP glutamate neurons evoked dopamine in aversion-associated ventromedial nucleus accumbens (NAc), but reduced dopamine release in reward-associated dorsomedial NAc. These findings show how heterogeneous VP projections to VTA can be engaged to shape approach and avoidance behaviors.

EEG source derived salience network coupling supports real-world attention switching.

While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.