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Importance: Mental illnesses are a leading cause of disability globally, and functional disability is often in part caused by cognitive impairments across psychiatric disorders. However, studies have consistently reported seemingly opposite findings regarding the association between cognition and psychiatric symptoms. Objective: To determine if the association between general cognition and mental health symptoms diverges at different symptom severities in children. Design, Setting, and Participants: A total of 5175 children with complete data at 2 time points assessed 2 years apart (aged 9 to 11 years at the first assessment) from the ongoing Adolescent Brain and Cognitive Development (ABCD) study were evaluated for a general cognition factor and mental health symptoms from September 2016 to August 2020 at 21 sites across the US. Polynomial and generalized additive models afforded derivation of continuous associations between cognition and psychiatric symptoms across different ranges of symptom severity. Data were analyzed from December 2022 to April 2024. Main Outcomes and Measures: Aggregate cognitive test scores (general cognition) were primarily evaluated in relation to total and subscale-specific symptoms reported from the Child Behavioral Checklist. Results: The sample included 5175 children (2713 male [52.4%] and 2462 female [47.6%]; mean [SD] age, 10.9 [1.18] years). Previously reported mixed findings regarding the association between general cognition and symptoms may consist of several underlying, opposed associations that depend on the class and severity of symptoms. Linear models recovered differing associations between general cognition and mental health symptoms, depending on the range of symptom severities queried. Nonlinear models confirm that internalizing symptoms were significantly positively associated with cognition at low symptom burdens higher cognition = more symptoms) and significantly negatively associated with cognition at high symptom burdens. Conclusions and Relevance: The association between mental health symptoms and general cognition in this study was nonlinear. Internalizing symptoms were both positively and negatively associated with general cognition at a significant level, depending on the range of symptom severities queried in the analysis sample. These results appear to reconcile mixed findings in prior studies, which implicitly assume that symptom severity tracks linearly with cognitive ability across the entire spectrum of mental health. As the association between cognition and symptoms may be opposite in low vs high symptom severity samples, these results reveal the necessity of clinical enrichment in studies of cognitive impairment.
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Childhood environments are critical in shaping cognitive neurodevelopment. With the increasing availability of large-scale neuroimaging datasets with deep phenotyping of childhood environments, we can now build upon prior studies that have considered relationships between one or a handful of environmental and neuroimaging features at a time. Here, we characterize the combined effects of hundreds of inter-connected and co-occurring features of a child's environment ("exposome") and investigate associations with each child's unique, multidimensional pattern of functional brain network organization ("functional topography") and cognition. We apply data-driven computational models to measure the exposome and define personalized functional brain networks in pre-registered analyses. Across matched discovery (n=5139, 48.5% female) and replication (n=5137, 47.1% female) samples from the Adolescent Brain Cognitive Development study, the exposome was associated with current (ages 9-10) and future (ages 11-12) cognition. Changes in the exposome were also associated with changes in cognition after accounting for baseline scores. Cross-validated ridge regressions revealed that the exposome is reflected in functional topography and can predict performance across cognitive domains. Importantly, a single measure capturing a child's exposome could more accurately and parsimoniously predict cognition than a wealth of personalized neuroimaging data, highlighting the importance of children's complex, multidimensional environments in cognitive neurodevelopment.
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Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
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Conectoma , Imagen por Resonancia Magnética , Corteza Sensoriomotora , Humanos , Adolescente , Femenino , Masculino , Adulto Joven , Niño , Corteza Sensoriomotora/fisiología , Corteza Sensoriomotora/diagnóstico por imagen , Preescolar , Red Nerviosa/fisiología , Red Nerviosa/diagnóstico por imagen , Vías Nerviosas/fisiología , Adulto , Corteza Cerebral/diagnóstico por imagen , Corteza Cerebral/fisiología , Corteza Cerebral/crecimiento & desarrolloRESUMEN
BACKGROUND: Symptoms of borderline personality disorder (BPD) often manifest during adolescence, but the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here, we aimed to investigate how multivariate patterns of functional connectivity are associated with borderline personality traits in large samples of young adults and adolescents. METHODS: We used functional magnetic resonance imaging data from young adults and adolescents from the HCP-YA (Human Connectome Project Young Adult) (n = 870, ages 22-37 years, 457 female) and the HCP-D (Human Connectome Project Development) (n = 223, ages 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five-Factor Inventory. A ridge regression model with cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity. RESULTS: Multivariate functional connectivity patterns significantly predicted out-of-sample BPD scores in unseen data in young adults (HCP-YA ppermuted = .001) and older adolescents (HCP-D ppermuted = .001). Regional predictive capacity was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD scores aligned with those associated with development in youth. CONCLUSIONS: Individual differences in functional connectivity in developmentally sensitive regions are associated with borderline personality traits.
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Trastorno de Personalidad Limítrofe , Encéfalo , Conectoma , Imagen por Resonancia Magnética , Humanos , Trastorno de Personalidad Limítrofe/fisiopatología , Trastorno de Personalidad Limítrofe/diagnóstico por imagen , Femenino , Adulto Joven , Adulto , Masculino , Adolescente , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Red Nerviosa/diagnóstico por imagen , Red Nerviosa/fisiopatologíaRESUMEN
Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues. XCP-D is a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University at Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Singularity image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NifTI or CIFTI files, following pre-processing with fMRIPrep, HCP, and ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >3,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.
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Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Developmentâ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.
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Individualidad , Imagen por Resonancia Magnética , Humanos , Adolescente , Imagen por Resonancia Magnética/métodos , Encéfalo , Cognición , Pruebas Neuropsicológicas , Mapeo EncefálicoRESUMEN
Background |: Symptoms of borderline personality disorder (BPD) often manifest in adolescence, yet the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here we aimed to investigate how multivariate patterns of functional connectivity are associated with symptoms of BPD in a large sample of young adults and adolescents. Methods |: We used high-quality functional Magnetic Resonance Imaging (fMRI) data from young adults from the Human Connectome Project: Young Adults (HCP-YA; N = 870, ages 22-37 years, 457 female) and youth from the Human Connectome Project: Development (HCP-D; N = 223, age range 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five Factor Inventory (NEO-FFI). A ridge regression model with 10-fold cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity, while controlling for in-scanner motion, age, and sex. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity. Results |: Multivariate functional connectivity patterns significantly predicted out-of-sample BPD proxy scores in unseen data in both young adults (HCP-YA; pperm = 0.001) and older adolescents (HCP-D; pperm = 0.001). Predictive capacity of regions was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD proxy scores aligned with those associated with development in youth. Conclusion |: Individual differences in functional connectivity in developmentally-sensitive regions are associated with the symptoms of BPD.
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To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.
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Período Crítico Psicológico , Neurobiología , Animales , Humanos , Modelos Animales , NeuroimagenRESUMEN
Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.
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Desarrollo del Adolescente , Corteza Cerebral , Desarrollo Infantil , Neuroimagen Funcional , Adolescente , Adulto , Niño , Femenino , Humanos , Masculino , Adulto Joven , Corteza Cerebral/diagnóstico por imagen , Corteza Cerebral/fisiología , Cognición/fisiología , Estudios de Cohortes , Conjuntos de Datos como Asunto , Neuroimagen Funcional/métodos , Flujo OpticoRESUMEN
In this perspective, we describe how developmental improvements in youth executive function (EF) are supported by hierarchically organized maturational changes in functional brain systems. We first highlight evidence that functional brain systems are embedded within a hierarchical sensorimotor-association axis of cortical organization. We then review data showing that functional system developmental profiles vary along this axis: systems near the associative end become more functionally segregated, while those in the middle become more integrative. Developmental changes that strengthen the hierarchical organization of the cortex may support EF by facilitating top-down information flow and balancing within- and between-system communication. We propose a central role for attention and frontoparietal control systems in the maturation of healthy EF and suggest that reduced functional system differentiation across the sensorimotor-association axis contributes to transdiagnostic EF deficits.
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Encéfalo , Función Ejecutiva , Adolescente , Humanos , Imagen por Resonancia MagnéticaRESUMEN
Individual differences in cognitive abilities emerge early during development, and children with poorer cognition are at increased risk for adverse outcomes as they enter adolescence. Caregiving plays an important role in supporting cognitive development, yet it remains unclear how specific types of caregiving behaviors may shape cognition, highlighting the need for large-scale studies. In the present study, we characterized replicable yet specific associations between caregiving behaviors and cognition in two large sub-samples of children ages 9-10 years old from the Adolescent Brain Cognitive Development Study® (ABCD). Across both discovery and replication sub-samples, we found that child reports of caregiver monitoring (supervision or regular knowledge of the child's whereabouts) were positively associated with general cognition abilities, after covarying for age, sex, household income, neighborhood deprivation, and parental education. This association was specific to the type of caregiving behavior (caregiver monitoring, but not caregiver warmth), and was most strongly associated with a broad domain of general cognition (but not executive function or learning/memory). Additionally, we found that caregiver monitoring partially mediated the association between household income and cognition, furthering our understanding of how socioeconomic disparities may contribute to disadvantages in cognitive development. Together, these findings underscore the influence of differences in caregiving behavior in shaping youth cognition. RESEARCH HIGHLIGHTS: Caregiver monitoring, but not caregiver warmth, is associated with cognitive performance in youth Caregiver monitoring partially mediates the association between household income and cognition Results replicated across two large matched samples from the Adolescent Brain Cognitive Development Study® (ABCD).
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Cognición , Padres , Niño , Humanos , Adolescente , EscolaridadRESUMEN
How does attention enhance neural representations of goal-relevant stimuli while suppressing representations of ignored stimuli across regions of the brain? While prior studies have shown that attention enhances visual responses, we lack a cohesive understanding of how selective attention modulates visual representations across the brain. Here, we used functional magnetic resonance imaging (fMRI) while participants performed a selective attention task on superimposed stimuli from multiple categories and used a data-driven approach to test how attention affects both decodability of category information and residual correlations (after regressing out stimulus-driven variance) with category-selective regions of ventral temporal cortex (VTC). Our data reveal three main findings. First, when two objects are simultaneously viewed, the category of the attended object can be decoded more readily than the category of the ignored object, with the greatest attentional enhancements observed in occipital and temporal lobes. Second, after accounting for the response to the stimulus, the correlation in the residual brain activity between a cortical region and a category-selective region of VTC was elevated when that region's preferred category was attended vs. ignored, and more so in the right occipital, parietal, and frontal cortices. Third, we found that the stronger the residual correlations between a given region of cortex and VTC, the better visual category information could be decoded from that region. These findings suggest that heightened residual correlations by selective attention may reflect the sharing of information between sensory regions and higher-order cortical regions to provide attentional enhancement of goal-relevant information.
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Atención/fisiología , Formación de Concepto/fisiología , Reconocimiento Visual de Modelos/fisiología , Lóbulo Temporal/fisiología , Adolescente , Adulto , Reconocimiento Facial/fisiología , Femenino , Humanos , Imagen por Resonancia Magnética , Masculino , Lóbulo Temporal/diagnóstico por imagen , Adulto JovenRESUMEN
Although impaired attention is a diagnostic feature of anxiety disorders, we lack an understanding of which aspects of attention are impaired, the neurobiological basis of these impairments, and the contribution of stressors. To address these gaps in knowledge, we developed and tested behavioral tasks designed to parse the subdomains of attention impairments associated with anxiety symptoms and used electro-encephalographic (EEG) recordings to probe the neural basis of attentional performance. Participants were n = 55 individuals aged 18-35 with mild-to-moderate mood and anxiety symptoms. We also assessed stressful life events that may impact mental health and attention abilities, including stressors that occurred in early life before age 18 years. Severity of anxiety was found to be specifically associated with impairments in spatial attention but not feature-based attention. These impairments in spatial attention also partially mediated the association between early-life stressors and anxiety symptoms. Impairments in spatial selective attention were associated with decreased posterior alpha oscillations in EEG recordings in a subsample of participants, whereas spatial divided attention impairments were associated with decreased frontocentral theta oscillations. Our results provide a thorough characterization of attention impairments associated with anxiety, their EEG correlates, and the impact of stressors both in early life and adulthood.
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Experiencias Adversas de la Infancia , Adolescente , Adulto , Ansiedad , Trastornos de Ansiedad , Atención , Electroencefalografía , Humanos , Adulto JovenRESUMEN
BACKGROUND: Despite tremendous advances in characterizing human neural circuits that govern emotional and cognitive functions impaired in depression and anxiety, we lack a circuit-based taxonomy for depression and anxiety that captures transdiagnostic heterogeneity and informs clinical decision making. METHODS: We developed and tested a novel system for quantifying 6 brain circuits reproducibly and at the individual patient level. We implemented standardized circuit definitions relative to a healthy reference sample and algorithms to generate circuit clinical scores for the overall circuit and its constituent regions. RESULTS: In new data from primary and generalizability samples of depression and anxiety (N = 250), we demonstrated that overall disconnections within task-free salience and default mode circuits map onto symptoms of anxious avoidance, loss of pleasure, threat dysregulation, and negative emotional biases-core characteristics that transcend diagnoses-and poorer daily function. Regional dysfunctions within task-evoked cognitive control and affective circuits may implicate symptoms of cognitive and valence-congruent emotional functions. Circuit dysfunction scores also distinguished response to antidepressant and behavioral intervention treatments in an independent sample (n = 205). CONCLUSIONS: Our findings articulate circuit dimensions that relate to transdiagnostic symptoms across mood and anxiety disorders. Our novel system offers a foundation for deploying standardized circuit assessments across research groups, trials, and clinics to advance more precise classifications and treatment targets for psychiatry.
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Depresión , Psiquiatría , Ansiedad , Trastornos de Ansiedad , HumanosRESUMEN
BACKGROUND: There is limited research examining the natural trajectories of depression and anxiety, how these trajectories relate to baseline neural circuit function, and how symptom trajectory-circuit relationships are impacted by engagement in lifestyle activities including exercise, hobbies, and social interactions. To address these gaps, we assessed these relations over three months in untreated participants. METHODS: 262 adults (59.5% female, mean age 35) with symptoms of anxiety and depression, untreated with pharmacotherapy or behavioral therapy, completed the DASS-42, WHOQOL, and custom surveys at baseline and follow-up to assess symptoms, psychosocial function, and lifestyle activity engagement. At baseline, participants underwent fMRI under task-free and task-evoked conditions. We quantified six circuits implicated in these symptoms: default mode, salience, negative and positive affect, attention, and cognitive control. RESULTS: From baseline to 3 months, some participants demonstrated a natural improvement in anxiety (24%) and depression (26%) symptoms. Greater baseline salience circuit connectivity (pFDR=0.045), specifically between the left and right insula (pFDR=0.045), and greater negative affect circuit connectivity elicited by sad faces (pFDR=0.030) were associated with anxiety symptom improvement. While engagement in lifestyle activities were not associated with anxiety improvements, engagement in hobbies moderated the association between negative affect circuit connectivity and anxiety symptom improvement (p = 0.048). LIMITATIONS: The observational design limits causal inference. CONCLUSIONS: Our findings highlight the role of the salience and negative affect circuits as potential circuit markers of natural anxiety symptom improvements over time. Future studies that identify biomarkers associated with symptom improvements are critical for the development of personalized treatment targets.
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Trastornos de Ansiedad , Ansiedad , Adulto , Ansiedad/terapia , Trastornos de Ansiedad/terapia , Corteza Cerebral/diagnóstico por imagen , Femenino , Humanos , Imagen por Resonancia Magnética , MasculinoRESUMEN
Attention is thought to be the gateway between information and learning, yet there is much we do not understand about how students pay attention in the classroom. Leveraging ideas from cognitive neuroscience and psychology, we explore a framework for understanding attention in the classroom, organized along two key dimensions: internal/external attention and on-topic/off-topic attention. This framework helps us to build new theories for why active-learning strategies are effective teaching tools and how synchronized brain activity across students in a classroom may support learning. These ideas suggest new ways of thinking about how attention functions in the classroom and how different approaches to the same active-learning strategy may vary in how effectively they direct students' attention. We hypothesize that some teaching approaches are more effective than others because they leverage natural fluctuations in students' attention. We conclude by discussing implications for teaching and opportunities for future research.
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Atención , Aprendizaje , Humanos , Estudiantes , Encuestas y Cuestionarios , EnseñanzaRESUMEN
Importance: Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. Objective: To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures. Design, Setting, and Participants: This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019. Exposures: Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180). Main Outcomes and Measures: The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index. Results: The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]). Conclusions and Relevance: This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings. Trial Registration: ClinicalTrials.gov Identifier: NCT00693849.
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Antidepresivos/uso terapéutico , Citalopram/uso terapéutico , Trastorno Depresivo Mayor/tratamiento farmacológico , Electroencefalografía/métodos , Aprendizaje Automático/estadística & datos numéricos , Sertralina/uso terapéutico , Clorhidrato de Venlafaxina/uso terapéutico , Adulto , Algoritmos , Reglas de Decisión Clínica , Trastorno Depresivo Mayor/diagnóstico , Trastorno Depresivo Mayor/psicología , Femenino , Humanos , Masculino , Persona de Mediana Edad , Pronóstico , Estudios Prospectivos , Resultado del TratamientoRESUMEN
Alterations to electroencephalography (EEG) power have been reported for psychiatric conditions such as depression and anxiety, but not for mental wellbeing in a healthy population. This study examined the resting EEG profiles associated with mental wellbeing, and how genetics and environment contribute to these associations using twin modelling. Mental wellbeing was assessed using the COMPAS-W Wellbeing Scale which measures both subjective and psychological wellbeing. In 422 healthy adult monozygotic and dizygotic twins aged 18-61 years, we examined the association between mental wellbeing and EEG power (alpha, beta, theta, delta) using linear mixed models. This was followed by univariate and multivariate twin modelling to assess the heritability of wellbeing and EEG power, and whether the association was driven by shared genetics or environment. A significant association between wellbeing and an interaction of alpha, beta, and delta (ABD) power was found (ß = -0.33, p < 0.001) whereby a profile of high alpha and delta and low beta was associated with higher wellbeing, independent of depression and anxiety symptoms. This finding was supported by a five-fold cross-validation analysis. A significant genetic correlation (rG = -0.43) was found to account for 94% of the association between wellbeing and the EEG power interaction. Together, this study has identified a novel EEG profile with a common genetic component that may be a potential biomarker of mental wellbeing. Future studies need to clarify the causal direction of this association.