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.

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.

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.

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.

Dissociable neural mechanisms of cognition and well-being in youth versus healthy aging.

Mental health, cognition, and their underlying neural processes in healthy aging are rarely studied simultaneously. Here, in a sample of healthy younger (n = 62) and older (n = 54) adults, we compared subjective mental health as well as objective global cognition across several core cognitive domains with simultaneous electroencephalography (EEG). We found significantly greater symptoms of anxiety, depression, and loneliness in youth and in contrast, greater mental well-being in older adults. Yet, global performance across core cognitive domains was significantly worse in older adults. EEG-based source imaging of global cognitive task-evoked processing showed reduced suppression of activity in the anterior medial prefrontal default mode network (DMN) region in older adults relative to youth. Global cognitive performance efficiency was predicted by greater activity in the right dorsolateral prefrontal cortex in younger adults and in contrast, by greater activity in right inferior frontal cortex in older adults. Furthermore, greater mental well-being in older adults related to lesser global task-evoked activity in the posterior DMN. Overall, these results suggest dissociated neural mechanisms underlying global cognition and mental well-being in youth versus healthy aging. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Identifying protective socio-ecological factors for college students in California's deadliest wildfire.

Objectives: This case study examined multi-level social-ecological supports in promoting well-being through college students impacted by one of the deadliest wildfires in U.S. history.Participants: College students attending a large public university were surveyed (N = 354, Mage = 22.7, 76.2% female, 61% white).Methods: Measures included demographics, individual factors (mindfulness, sleep problems), social support (emotional support, family support, and friendship), and sense of community. Multiple linear regression models on well-being were constructed.Results: Findings indicated that mindfulness, sleep disturbances, emotional support, family support, number of close friends, and sense of community were significant predictors of well-being.Conclusion: Findings highlight the importance of universities in proactively bolstering critical social-ecological needs of college students living in communities vulnerable to climate-change accelerated environmental disasters.

Personalized machine learning of depressed mood using wearables.

Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual's best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

Cognitive and Neural Correlates of Loneliness and Wisdom during Emotional Bias.

Loneliness and wisdom have opposing impacts on health and well-being, yet their neuro-cognitive bases have never been simultaneously investigated. In this study of 147 healthy human subjects sampled across the adult lifespan, we simultaneously studied the cognitive and neural correlates of loneliness and wisdom in the context of an emotion bias task. Aligned with the social threat framework of loneliness, we found that loneliness was associated with reduced speed of processing when angry emotional stimuli were presented to bias cognition. In contrast, we found that wisdom was associated with greater speed of processing when happy emotions biased cognition. Source models of electroencephalographic data showed that loneliness was specifically associated with enhanced angry stimulus-driven theta activity in the left transverse temporal region of interest, which is located in the area of the temporoparietal junction (TPJ), while wisdom was specifically related to increased TPJ theta activity during happy stimulus processing. Additionally, enhanced attentiveness to threatening stimuli for lonelier individuals was observed as greater beta activity in left superior parietal cortex, while wisdom significantly related to enhanced happy stimulus-evoked alpha activity in the left insula. Our results demonstrate emotion-context driven modulations in cognitive neural circuits by loneliness versus wisdom.

Chronic Mental Health Sequelae of Climate Change Extremes: A Case Study of the Deadliest Californian Wildfire.

Introduction. Weather-related disasters, such as wildfires exacerbated by a rise in global temperatures, need to be better studied in terms of their mental health impacts. This study focuses on the mental health sequelae of the deadliest wildfire in California to date, the Camp Fire of 2018. Methods. We investigated a sample of 725 California residents with different degrees of disaster exposure and measured mental health using clinically validated scales for post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and generalized anxiety disorder (GAD). Data were collected at a chronic time-point, six months post-wildfire. We used multiple regression analyses to predict the mental health outcomes based on self-reported fire exposure. Additionally, we included vulnerability and resilience factors in hierarchical regression analyses. Results. Our primary finding is that direct exposure to large scale fires significantly increased the risk for mental health disorders, particularly for PTSD and depression. Additionally, the inclusion of vulnerability and resilience factors in the hierarchical regression analyses led to the significantly improved prediction of all mental health outcomes. Childhood trauma and sleep disturbances exacerbated mental health symptoms. Notably, self-reported resilience had a positive effect on mental health, and mindfulness was associated with significantly lower depression and anxiety symptoms. Conclusion. Overall, our study demonstrated that climate-related extreme events, such as wildfires, can have severe mental illness sequelae. Moreover, we found that pre-existing stressful life events, resilient personality traits and lifestyle factors can play an important role in the prevalence of psychopathology after such disasters. Unchecked climate change projected for the latter half of this century may severely impact the mental wellbeing of the global population, and we must find ways to foster individual resiliency.

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.

H-Ras Transformation of Mammary Epithelial Cells Induces ERK-Mediated Spreading on Low Stiffness Matrix.

Oncogenic transformation of mammary epithelial cells (MECs) is a critical step in epithelial-to-mesenchymal transition (EMT), but evidence also shows that MECs undergo EMT with increasing matrix stiffness; the interplay of genetic and environmental effects on EMT is not clear. To understand their combinatorial effects on EMT, premalignant MCF10A and isogenic Ras-transformed MCF10AT are cultured on polyacrylamide gels ranging from normal mammary stiffness, ≈150 Pa, to tumor stiffness, ≈5700 Pa. Though cells spread on stiff hydrogels independent of transformation, only 10AT cells exhibit heterogeneous spreading behavior on soft hydrogels. Within this mixed population, spread cells exhibit an elongated, mesenchymal-like morphology, disrupted localization of the basement membrane, and nuclear localization of the EMT transcription factor TWIST1. MCF10AT spreading is not driven by typical mechanosensitive pathways including YAP and TGF-ß or by myosin contraction. Rather, ERK activation induces spreading of MCF10AT cells on soft hydrogels and requires dynamic microtubules. These findings indicate the importance of oncogenic signals, and their hierarchy with substrate mechanics, in regulating MEC EMT.