Modulation of Posterior Default Mode Network Activity During Interoceptive Attention and Relation to Mindfulness.

Background: Interoceptive attention to internal sensory signals, such as the breath, is fundamental to mindfulness. However, interoceptive attention can be difficult to study, with many studies relying on subjective and retrospective measures. Response consistency is an established method for evaluating variability of attention on exteroceptive attention tasks, but it has rarely been applied to interoceptive attention tasks. Methods: In this study, we measured consistency of response times on a breath-monitoring task with simultaneous electroencephalography in individuals across the life span (15-91 years of age, N = 324). Results: We found that consistency on the breath-monitoring task was positively correlated with attentive performance on an exteroceptive inhibitory control task. Electroencephalography source reconstruction showed that on-task alpha band (8-12 Hz) activity was greater than that measured at rest. Low-consistency/longer breath responses were associated with elevated brain activity compared with high-consistency responses, particularly in posterior default mode network (pDMN) brain regions. pDMN activity was inversely linked with functional connectivity to the frontoparietal network and the cingulo-opercular network on task but not at rest, suggesting a role for these frontal networks in on-task regulation of pDMN activity. pDMN activity within the precuneus region was greater in participants who reported low subjective mindfulness and was adaptively modulated by task difficulty in an independent experiment. Conclusions: Elevated pDMN alpha activity serves as an objective neural marker for low-consistency responding during interoceptive breath attention, scales with task difficulty, and is associated with low subjective mindfulness.

We assessed interoceptive attention on a breath attention task in individuals across the life span. Participants monitored and responded every 2 breath cycles, and electroencephalography was measured simultaneously. We found that consistency of responding on the interoceptive task was related to attentive performance on an exteroceptive inhibitory control task. On-task EEG alpha band activity was greater than that measured at rest. Low-consistency/longer breath responses were associated with elevated brain activity compared with high-consistency responses, particularly in posterior default mode network (pDMN) brain regions. Participants reporting low mindfulness had greater activity in the precuneus region of the pDMN.

Intermittent Theta Burst Stimulation Drives Bi-Directional Changes in Excitability in Prefrontal Cortex.

Theta burst stimulation (TBS), an FDA-cleared treatment for depression, is hypothesized to modulate excitability in the prefrontal cortex, though this has not definitively been shown in vivo. We performed calcium imaging on glutamatergic neurons in awake rodents to understand the effects of theta burst stimulation at a cellular level. Our findings provide the first direct evidence that TBS bidirectionally modulates glutamatergic activity when delivered in vivo and directly links calcium activity changes during stimulation with post-stimulation plasticity.

BBPT attenuated 6-OHDA-induced toxicity by modulating oxidative stress, apoptotic, and inflammatory proteins in primary neurons and rat models of Parkinson's disease.

Parkinson's disease (PD) results from the degeneration of dopaminergic neurons in substantia nigra pars compacta (SNpc). Adenosine A2AR acting through the striato-pallidal pathway has emerged as a non-dopaminergic target in the therapy of PD. In the present work, the anti-parkinsonian potential of (4E)-4-(4-bromobenzylideneamino)-3-phenyl-2,3-dihydro-2-thioxo- thiazole-5-carbonitrile (BBPT) was explored. BBPT exhibited significant antioxidant activity in situ. In the MTT assay, the BBPT treatment showed insignificant toxicity to the primary midbrain neuronal (PMDN) cells. 6-OHDA induced PMDN cells, 3 h post-treated with BBPT showed 80-85 % survival of the cells and restoration of dopamine and TNF-α levels. The acute and sub-acute toxicity test for BBPT was performed with Sprague Dawley (SD) rats. In toxicity assay, any significant physical, hematological, or biochemical changes in the rats were not observed. To evaluate the effect of BBPT in vivo, a 6-OHDA-induced unilaterally lesioned SD rat model of PD was established. We observed that the BBPT treatment improved the behavioral symptoms in 6-OHDA-induced unilaterally lesioned rats. The proteins of 6-OHDA-induced BBPT-treated rats were isolated from the brain tissue to assess the antioxidant effect (GSH, catalase, SOD, lipid-peroxidation, nitrite), dopamine levels, and the restoration in the apoptosis and inflammation. Our results demonstrated that BBPT increased the anti-oxidant enzyme levels, restored the caspase-3/Bcl-2 levels to arrest apoptosis, and attenuated the TNF-α/IL-6 levels, thus restoring the neuronal damage in unilaterally lesioned 6-OHDA-induced SD rats. Precisely, the findings suggested that BBPT possessed significant anti-parkinsonian activity and has the potential to prevent dopaminergic neurodegeneration.

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.

Aperiodic and periodic components of oscillatory brain activity in relation to cognition and symptoms in pediatric ADHD.

Children with attention-deficit/hyperactivity disorder show deficits in processing speed, as well as aberrant neural oscillations, including both periodic (oscillatory) and aperiodic (1/f-like) activity, reflecting the pattern of power across frequencies. Both components were suggested as underlying neural mechanisms of cognitive dysfunctions in attention-deficit/hyperactivity disorder. Here, we examined differences in processing speed and resting-state-Electroencephalogram neural oscillations and their associations between 6- and 12-year-old children with (n = 33) and without (n = 33) attention-deficit/hyperactivity disorder. Spectral analyses of the resting-state EEG signal using fast Fourier transform revealed increased power in fronto-central theta and beta oscillations for the attention-deficit/hyperactivity disorder group, but no differences in the theta/beta ratio. Using the parameterization method, we found a higher aperiodic exponent, which has been suggested to reflect lower neuronal excitation-inhibition, in the attention-deficit/hyperactivity disorder group. While fast Fourier transform-based theta power correlated with clinical symptoms for the attention-deficit/hyperactivity disorder group only, the aperiodic exponent was negatively correlated with processing speed across the entire sample. Finally, the aperiodic exponent was correlated with fast Fourier transform-based beta power. These results highlight the different and complementary contribution of periodic and aperiodic components of the neural spectrum as metrics for evaluation of processing speed in attention-deficit/hyperactivity disorder. Future studies should further clarify the roles of periodic and aperiodic components in additional cognitive functions and in relation to clinical status.

A2AR antagonists triggered the AMPK/m-TOR autophagic pathway to reverse the calcium-dependent cell damage in 6-OHDA induced model of PD.

Calcium dyshomeostasis, oxidative stress, autophagy and apoptosis are the pathogenesis of selective dopaminergic neuronal loss in Parkinson's disease (PD). Earlier, we reported that A2A R modulates IP3-dependent intracellular Ca2+ signalling via PKA. Moreover, A2A R antagonist has been reported to reduce oxidative stress and apoptosis in PD models, however intracellular Ca2+ ([Ca2+]i) dependent autophagy regulation in the 6-OHDA model of PD has not been explored. In the present study, we investigated the A2A R antagonists mediated neuroprotective effects in 6-OHDA-induced primary midbrain neuronal (PMN) cells and unilateral lesioned rat model of PD. 6-OHDA-induced oxidative stress (ROS and superoxide) and [Ca2+]i was measured using Fluo4AM, DCFDA and DHE dye respectively. Furthermore, autophagy was assessed by Western blot of p-m-TOR/mTOR, p-AMPK/AMPK, LC3I/II, Beclin and ß-actin. Apoptosis was measured by Annexin V-APC-PI detection and Western blot of Bcl2, Bax, caspase3 and ß-actin. Dopamine levels were measured by Dopamine ELISA kit and Western blot of tyrosine hydroxylase. Our results suggest that 6-OHDA-induced PMN cell death occurred due to the interruption of [Ca2+]i homeostasis, accompanied by activation of autophagy and apoptosis. A2A R antagonists prevented 6-OHDA-induced neuronal cell death by decreasing [Ca2+]i overload and oxidative stress. In addition, we found that A2A R antagonists upregulated mTOR phosphorylation and downregulated AMPK phosphorylation thereby reducing autophagy and apoptosis both in 6-OHDA induced PMN cells and 6-OHDA unilateral lesioned rat model. In conclusion, A2A R antagonists alleviated 6-OHDA toxicity by modulating [Ca2+]i signalling to inhibit autophagy mediated by the AMPK/mTOR pathway.

Neuroecological links of the exposome and One Health.

This NeuroView assesses the interplay among exposome, One Health, and brain capital in health and disease. Physical and social exposomes affect brain health, and green brain skills are required for environmental health strategies. Ibanez et al. address current gaps and strategies needed in research, policy, and technology, offering a road map for stakeholders.

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.

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.

Analysis of nonlinear ordinary differential equations with the generalized Mittag-Leffler kernel.

The Picard iterative approach used in the paper to derive conditions under which nonlinear ordinary differential equations based on the derivative with the Mittag-Leffler kernel admit a unique solution. Using a simple Euler approximation and Heun's approach, we solved this nonlinear equation numerically. Some examples of a nonlinear linear differential equation were considered to present the existence and uniqueness of their solutions as well as their numerical solutions. A chaotic model was also considered to show the extension of this in the case of nonlinear systems.

Quantifying attention span across the lifespan.

Introduction: Studies examining sustained attention abilities typically utilize metrics that quantify performance on vigilance tasks, such as response time and response time variability. However, approaches that assess the duration that an individual can maintain their attention over time are lacking. Methods: Here we developed an objective attention span metric that quantified the maximum amount of time that a participant continuously maintained an optimal "in the zone" sustained attention state while performing a continuous performance task. Results: In a population of 262 individuals aged 7-85, we showed that attention span was longer in young adults than in children and older adults. Furthermore, declines in attention span over time during task engagement were related to clinical symptoms of inattention in children. Discussion: These results suggest that quantifying attention span is a unique and meaningful method of assessing sustained attention across the lifespan and in populations with inattention symptoms.

Better together: novel methods for measuring and modeling development of executive function diversity while accounting for unity.

Introduction: Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented precise understanding of the developmental trajectory of their organization. Methods: We introduce novel methods to address challenges for both measuring and modeling EFs using an accelerated longitudinal design with a large, diverse sample of students in middle childhood (N = 1,286; ages 8 to 14). We used eight adaptive assessments hypothesized to measure three EFs, working memory, context monitoring, and interference resolution. We deployed adaptive assessments to equate EF challenge across ages and a data-driven, network analytic approach to reveal the evolving diversity of EFs while simultaneously accounting for their unity. Results and discussion: Using this methodological paradigm shift brought new precision and clarity to the development of these EFs, showing these eight tasks are organized into three stable components by age 10, but refinement of composition of these components continues through at least age 14.

A generalized reinforcement learning based deep neural network agent model for diverse cognitive constructs.

Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modeled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.

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.

Parietal alpha underlies slower cognitive responses during interference processing in adolescents.

Adolescence is a critical period when cognitive control is rapidly maturing across several core dimensions. Here, we evaluated how healthy adolescents (13-17 years of age, n = 44) versus young adults (18-25 years of age, n = 49) differ across a series of cognitive assessments with simultaneous electroencephalography (EEG) recordings. Cognitive tasks included selective attention, inhibitory control, working memory, as well as both non-emotional and emotional interference processing. We found that adolescents displayed significantly slower responses than young adults specifically on the interference processing tasks. Evaluation of EEG event-related spectral perturbations (ERSPs) on the interference tasks showed that adolescents consistently had greater event-related desynchronization in alpha/beta frequencies in parietal regions. Midline frontal theta activity was also greater in the flanker interference task in adolescents, suggesting greater cognitive effort. Parietal alpha activity predicted age-related speed differences during non-emotional flanker interference processing, and frontoparietal connectivity, specifically midfrontal theta - parietal alpha functional connectivity predicted speed effects during emotional interference. Overall, our neuro-cognitive results illustrate developing cognitive control in adolescents particularly for interference processing, predicted by differential alpha band activity and connectivity in parietal brain regions.

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.

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.