Panic disorder is a prevalent and disabling condition marked by recurrent panic attacks and high treatment resistance. While previous research has focused on dysfunction within canonical fear circuits, the neurobiological basis of panic disorder may involve broader alterations across multiple brain systems. Here, we conducted a meta-analysis of functional neuroimaging experiments to identify consistent patterns of altered brain activity in panic disorder. We found increased activity in a prefrontal-hippocampus-brainstem axis, which was not confined to traditional fear-related regions. This pattern showed robust spatial associations with serotonergic and dopaminergic receptor distributions and was significantly explained by gene expression profiles of candidate genes, accounting for over one-third of the variance and supporting a polygenic model of the disorder. Further enrichment analyses revealed that the brain pattern is characterized by low neurodevelopmental and evolutionary expansion and reduced oxygen metabolism, consistent with theories of brainstem-based hypersensitivity. Functional annotation linked the identified brain pattern to emotional arousal, memory, learning, and goal-directed behavior, suggesting that panic disorder reflects psychophysiological interactions of higher-order cognitive systems and evolutionary older biological processes. These findings suggest that panic disorder involves widespread neural alterations beyond fear circuitry and highlight potential molecular and functional targets for future mechanistic and therapeutic research.

Promoting brain health is vital for well-being and reducing healthcare burdens. Brain health as measured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age- relates to many factors. However, a holistic view, integrating the range of factors an individual brain is exposed to, is missing for understanding how the exposome shapes brain health. After computing BAG as an indicator of grey matter (GM) health, we predicted it using machine learning based on 261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early life domains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining to cardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greater contribution to the prediction. In such domains, life period and duration of exposure appeared crucial. These findings call for early prevention in cardiovascular and metabolic health to promote life-long brain health.

Substantial brain volume loss is well-documented during acute anorexia nervosa (AN); however, longitudinal outcomes are unclear. Our comprehensive meta-analysis investigated global and regional structural brain alterations in adult and adolescent individuals with AN by extracting reported brain volume scores and neuroimaging coordinates from the literature. Results showed significant global brain volume reductions in gray matter (GM), white matter (WM), and increases in cerebrospinal fluid (CSF) in acute AN (N = 1130 patients; N = 40 papers), gradually improving upon weight rehabilitation. However, even after 1.5 years of recovery, significantly lower global GM volume compared to healthy controls was found (N = 232 patients; N = 12 papers). Regarding potential regional changes, our search identified 35 eligible papers with neuroimaging coordinates for 412 foci as input for our anatomical likelihood estimation (ALE) analyses. The results revealed widespread reductions of GM volume and cortical thickness, but notably also identified consistently affected brain regions including the cingulate gyrus, precentral gyrus, and precuneus. Spatial colocalization analyses using the Neurosynth data base indicated brain areas associated with eating, food, threat, and reinforcement to be relatively preserved. The findings of our meta-analysis contribute to a better understanding of the underlying pathophysiology of AN, the time course and residuals of brain structural alterations during recovery and clinical implications potentially relevant for more-targeted treatment options.

A range of environmental, lifestyle and biological exposures across the lifespan - varying in timing, duration and intensity - interact with genetic factors to shape an individual's neurocognitive phenotype. By referring to the totality of exposures that an individual has experienced in their life so far, the exposome offers a valuable concept to better understand interindividual variability in not only brain-behaviour phenotype but also vulnerability and resilience to brain diseases. Numerous large-scale neuroimaging projects are enriched with extended exposomic data including sociodemographic, biomedical, lifestyle and environmental measurements. Yet, deciphering how this complex web of influences collectively and dynamically shapes individual brain-behaviour phenotypes comes with substantial challenges. In this Perspective, we first emphasize how the exposome concept refers to a set of interrelated and interacting factors and outline how multivariate pattern learning enables us to account for this complexity. We also highlight temporality as a key challenge, as the timing, duration and sequencing of exposures importantly shape their associations with brain-behaviour phenotypes, and we discuss existing approaches to address these dynamics and their limitations. We further underscore the challenge of causal inferences in population datasets, especially given bidirectional exposome-brain relationships. We then refer to traditional statistical design, as well as generative models and causal machine learning, as promising perspectives to approach data entanglement. Finally, we conclude that, to truly benefit global health, the field also needs to address the lack of global diversity in brain-behaviour exposomic research.

Sustained affect shapes well-being, yet its neural architecture across externally elicited and internally generated experience remains unclear. Using whole-brain functional connectivity during minutes-long naturalistic movie viewing, we derived positive and negative affective experience signatures and their underlying neural architecture. These signatures predicted valence-specific affective intensity and generalized to independent movie-viewing data and internally generated affect, discriminating sad memory and rumination from neutral distraction while tracking subjective experience. Importantly, their expression showed little relation to vigilance or cognitive demand. Characterization of these signatures revealed coherent community structure and a shared distributed backbone, alongside valence-preferential components, consistent with a partially separable architecture. Extending beyond experimentally evoked states, in four resting-state depression cohorts, these signatures distinguished patients from controls with reduced positive and elevated negative signature expression, and predicted symptom burden and anhedonia. These findings identify a generalizable distributed architecture bridging external and internal affective experience and extending to clinically relevant affective dysregulation.

Voxel-based meta-analyses—also known as coordinate-based meta-analyses (CBMAs)—are powerful tools for synthesizing evidence from neuroimaging studies in human neuroscience, including investigations of psychological functions and differences in brain disorders. To achieve their full potential in accurately assessing the evidence, CBMAs should adhere to established best-practicmpe guidelines, such as the “Ten Simple Rules” published in 2018. Yet, even when studies report following these recommendations, the degree to which individual items are applicable or fully addressed is often unclear. To better support the evaluation of methodological rigor—which the 10 rules already promote but are not always consistently applied—, the developers of the most used CBMA methods followed a Delphi-style iterative process to create a reporting checklist focused on the methodological quality of CBMAs (Qual-CBMA). Qual-CBMA comprises criteria (e.g., preregistration, systematic search, homogeneous study characteristics, etc.) that authors should verify and comment on explicitly in the checklist (and, when unmet, also in the manuscript). The checklist encourages rigor and transparency by prompting authors to identify potential methodological limitations and to discuss their relevance—or irrelevance—in the context of their specific study. The checklist is designed as an aid to make reporting clearer and more transparent, not as a tool for evaluating whether authors have done something incorrectly. In this context, a high-quality CBMA is not defined by meeting every criterion, but by clearly commenting on the criteria—and explaining when unmet criteria are appropriately not applicable given the study’s objectives. We encourage authors to submit the Qual-CBMA checklist, together with their accompanying comments, when publishing new CBMAs, thereby reinforcing transparency and rigorous methodology and advancing understanding in cognitive neuroscience and clinical conditions.

Abstract Background The brain age gap (BAG), the difference between neuroimaging-predicted and chronological age, captures inter-individual variation in brain aging. Although sensitive to Alzheimer’s disease (AD) pathology, its longitudinal patterns across the clinical AD continuum and prognostic relevance remain unclear. Methods 577 participants from the DELCODE cohort (>2,100 MRI scans) were analysed: healthy controls individuals (HC, N=202), and patients with subjective cognitive decline (SCD, N=248), mild cognitive impairment (N=93), and AD dementia (N=34). All underwent structural MRI, amyloid (Aβ 42/40 ) and phosphorylated tau181 assessment, and lifestyle-related dementia risk profiling (LIBRA). BAG was derived using brainageR. Associations with baseline cognition, cognitive decline, and clinical progression (up to eight years) were examined using mixed-effects and Cox models. Mediation analyses tested whether BAG accounted for LIBRA-cognition associations. Biomarker-related and clinical findings were replicated in ADNI (N=461). Findings BAG showed excellent short-term reliability, increased stepwise across the clinical spectrum and was elevated in amyloid-positive SCD, but not in asymptomatic amyloid-positive HC. Longitudinal BAG increases were strongest in amyloid- and tau-positive participants (Aβ+T+). Higher BAG was associated with poorer baseline cognition and predicted cognitive decline, with strongest effects in Aβ+T+. All main findings replicated in ADNI. BAG was associated with LIBRA only in biomarker-negative participants and partly mediated associations with cognitive outcomes in DELCODE. Interpretation BAG is a reliable non-invasive marker of structural brain health sensitive to AD pathology and to modifiable AD risk. Detectable divergence prior to objective cognitive impairment supports its relevance for early risk stratification and prevention-oriented research. Funding Helmholtz AI Cooperation Unit (ZT-I-PF-5-163).

Abstract Elucidating the neurobiological basis of neurodevelopmental and psychiatric conditions (NDPCs) remains challenging because brain alterations vary within diagnoses and overlap across them. Whether diverse alterations follow a systematic organization that may reflect shared vulnerabilities remains unknown. Here, we assembled 10,135 individuals with schizophrenia, autism, bipolar, obsessive-compulsive, generalized anxiety, and major depressive disorders, and 11,998 reference participants across six continents through the ENIGMA consortium. Using normative modeling, we quantified individual deviations in cortical thickness, surface area, and subcortical volumes relative to lifespan reference trajectories (5 to 80 years). We show that structural deviations converged along cortical axes reflecting connectome organization, maturation, and cytoarchitectonic diversity. These axes mirrored typical population variation, but their expression differed across diagnoses and partly scaled with symptom severity. Even rare and highly individualized extreme deviations followed this organization, concentrating in densely connected regions. Finally, brain structural deviations overlapped substantially across diagnoses, while differences between them increased toward the association cortex. Together, we provide large-scale evidence that structural deviations across NDPCs are systematically constrained by the brain’s intrinsic architecture. This shared organization provides a framework for reconciling individual variability with transdiagnostic similarities and motivates an integrative, systems-level understanding of mental health.

BACKGROUND:Executive function (EF) impairments are often seen in mental disorders, particularly schizophrenia (SZ), where they relate to adverse outcomes. As a heterogeneous construct, how specifically each dimension of EF to characterize the diagnostic and prognostic aspects of SZ remains opaque. STUDY DESIGN:We used classification models with a stacking approach on systematically measured EFs using 6 tasks to discriminate 195 patients with SZ from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with 4 symptom dimensions. STUDY RESULTS:The models highlighted the importance of inhibitory control (interference and response inhibitions) or working memory (WM) in accurately identifying individuals with SZ (area under the curve [AUC] = 0.87) or those in remission (AUC = 0.81). Patients who are correctly classified, in the association with the contribution of interference inhibition function to our diagnostic classifier, present more severe baseline negative symptoms compared to those who are more likely to be misclassified. Also, linked to the function of WM updating, patients who are successfully classified as remitted display milder cognitive symptoms at follow-up. Remitted patients do not differ significantly from non-remitted cases in baseline EF assessments or overall symptom severity. CONCLUSIONS:Our work indicates that impairments in specific EF dimensions in SZ are differentially linked to individual symptom-load and prognostic outcomes. Thus, assessments and models based on EF may be promising in the clinical evaluation of this disorder.

Lesion network mapping (LNM) links focal brain lesions to distributed neural circuits by projecting lesion locations through a normative functional connectome. van den Heuvel and colleagues recently showed how commonly used LNM procedures generate maps that converge on nonspecific, low-dimensional properties of the connectome, introducing a bias. Consequently, many published maps of different conditions appear strikingly similar. Here, we offer an alternative approach that does highlight distinct symptom-specific signals in LNM. In a multicenter dataset of 2,950 stroke patients, we replicate the expected convergence under the standard procedures, but also demonstrate how permuting symptom labels provides an appropriate null model that delivers distinct, biologically plausible networks for specific cognitive functions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project is part of the Timely, Accurate, and Personalized Diagnosis of Dementia (TAP-dementia) program, which receives funding from ZonMw (#10510032120003) in the context of Onderzoeksprogramma Dementie, which is part of the Dutch National Dementia Strategy. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All cohorts received the requisite ethical and institutional approval in accordance with local regulations, which included informed consent, to allow data acquisition and sharing. All the data investigated were anonymous. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The domain-level parametric and permutation-based lesion network maps are provided on Open Science Framework (https://osf.io/puq36). The individual-level data that support the findings of this study are available from the project leads on reasonable request (https://metavcimap.org/group/become-a-member/). Restrictions related to privacy and personal data sharing regulations and informed consent may apply.

Group-level studies have highlighted the roles of aging, poor sleep, and brain atrophy in cognitive performance (CP) but have overlooked inter-individual variability. We predict CP from feature sets (demographic, subjective/objective sleep parameters, and regional brain morphometry) using multisite ENIGMA-Sleep data (n = 2,372). Linear and non-linear machine learning models were trained on the largest cohort (n = 845), and the best-performing models were validated on independent cohorts. Subsequently, based on the best-performing model on the largest cohort, we characterized feature importance and interactions across all cohorts. We observed that a combination of demographic, sleep, and brain parameters moderately predicted CP, with age emerging as the key predictor. Model explanations further suggested that age was the primary driver of prediction models, while sleep played a smaller role that varied across subgroups. These findings endorsed inter-individual variability and complex interaction between aging, sleep, brain, and CP.

Handgrip strength (HGS) is a significant biomarker for overall health, offering a simple, cost-effective method for assessing muscle function. Lower HGS is linked to higher mortality, functional decline, cognitive impairments, and chronic diseases. Considering the influence of anthropometrics and demographics on HGS, this study aims to develop a corrected HGS score using machine learning (ML) models to enhance its utility in understanding brain health and disease. Using UK Biobank data, sex-specific ML models were developed to predict HGS based on three anthropometric variables and age. A novel biomarker,, was introduced as the difference between true HGS (i.e. directly measured HGS) and bias-free predicted HGS. The neural basis of true HGS andwas investigated by correlating them with regional gray matter volume (GMV). Statistical analyses were performed to test their sensitivity to longitudinal changes in stroke and major depressive disorder patients compared to matched healthy controls (HC). HGS could be accurately predicted using anthropometric and demographic features with high accuracy using linear support vector machine. Compared to true HGS,showed high reassessment reliability and stronger, widespread associations with GMV, especially in motor-related regions. Longitudinal analysis revealed that neither HGS noreffectively differentiated patients from matched HC at post time-point. The proposedscore exhibited stronger correlations with GMV compared to true HGS, suggesting it better represents the relationship between muscle strength and brain structure. While not effective in differentiating patients from HC at post time-point, the increase infrom pre to post time-point in patient cohorts may indicate improved utility for monitoring disease progression, treatment efficacy, or rehabilitation effects, warranting further longitudinal validation.

The brain's functional organization relies on neural, metabolic, and vascular interactions. Molecular neuroimaging offers powerful tools for assessing macroscale brain connectivity by capturing relationships between regional perfusion and glucose metabolism. This review summarizes molecular connectivity studies of cerebral blood flow (CBF) and metabolism, focusing on methodological approaches and key findings. A systematic search across MEDLINE, EMBASE, and Scopus identified studies employing radiotracers to examine brain perfusion or glucose metabolic connectivity. Data extraction focused on tracer type, connectivity methodology, population, and clinical relevance. Overall, 384 studies were included, covering healthy condition, dementia, movement disorders, psychiatric diseases, epilepsy, and disorders of consciousness. Both resting-state and task-based paradigms were identified, with perfusion studies being popular for detecting fast task-induced molecular connectivity changes. Metabolic connectivity, assessed via [18F]FDG-PET at rest, emerged as robust marker of functional integrity and disease progression, especially in neurodegenerative conditions. Multimodal PET/MRI studies revealed partial overlap between metabolic and hemodynamic connectivity. Noteworthy findings include the identification of default mode network through the study of CBF and disease-related covariance patterns in neurodegenerative disorders through the study of glucose metabolism. Integrating macroscale molecular brain organization studies with neurophysiological techniques will deepen the understanding of brain connectivity in health and disease. Additionally, total-body PET/MRI data may in the future elucidate brain-body interactions fostering a more comprehensive connectome framework.

Introduction:Age-related declines in cognitive and motor functions show highly variable trajectories. To better understand the underlying mechanisms, we investigated multivariate associative effects between modifiable vascular risk factors, biological brain aging, cognitive, and motor performance in 40,579 individuals from the population-based UK Biobank and Hamburg City Health Study. Methods:We employed partial least squares correlation analysis (PLS) to model associations between multi-domain cognitive and motor test scores and three distinct MRI-derived markers of biological brain aging: relative brain age (from morphometric brain imaging), white matter hyperintensity load, and peak width of skeletonized mean diffusivity. Furthermore, we conducted mediation analyses to assess if these markers mediate the impact of vascular risk on functional decline. Results:PLS identified a single dominant latent dimension explaining 94.7% of the shared variance between neuroimaging and behavior. This dimension linked higher biological brain aging markers - with relative brain age showing the strongest contribution - to poorer cognitive and motor performance, particularly in executive function and processing speed. Mediation analysis revealed that biological brain aging acts as a partial mediator for the negative effects of blood pressure, glucose, waist-hip ratio, and smoking load on cognitive and motor function. Notably, this mediating effect was not observed for cholesterol levels. These results were consistent across both cohorts. Discussion:Our study illustrates the associative interplay between vascular health, biological brain aging, and cognitive and motor performance, emphasizing the need for preventive strategies to maintain late-life independence in aging populations.

The human brain is organized into interacting functional systems. Their underlying neurobiological mechanisms remain difficult to study in vivo1,2. Here, we adopt a topological framework to quantify the association between neurobiology and brain functional connectivity derived from both resting-state functional magnetic resonance imaging (rsfMRI) and magnetic encephalography (MEG). Across six healthy adult cohorts (n = 19-112), regional variation in rsfMRI connectivity robustly aligns with the distribution of neurotransmitter receptors and transporters. We find that low-frequency functional synchronization measured by rsfMRI is predominantly modulated by decreased regional availability of multiple receptors and transporters. These patterns are present in every single subject, replicate across all cohorts, and are mirrored in MEG, where high-frequency synchronization increases with availability of the same receptors and transporters. Most prominently, we observe noradrenergic modulation of functional connectivity in a sensorimotor-posterior-insular network that is consistently detected across individuals and is linked to autonomic arousal. In pharmacological and clinical samples, the associations are sensitive to manipulation of the respective neurotransmitter systems and are altered in patients with early psychosis, aligning with clinical symptomatology. These findings provide biological insight into typical and atypical functional organization of the human brain using a framework linking underlying neurobiology to the functional connectome (NEOFC).

While modern diagnostic classification systems aim to nosologically structure psychiatric disorders, they poorly align with the genetic, neurobiological, and environmental heterogeneity observed in these disorders. This limitation has complicated the search for clinically useful biomarkers for diagnosis and treatment. Recent work on genetic and environmental contributions to mental health indicates that this heterogeneity stems from differential involvement of diverse biological pathways within and across diagnostic clusters. This complex interplay presents a many-to-many mapping problem in psychiatry, where distinct pathophysiological processes can lead to similar clinical symptoms. Here, we argue that disentangling these biological mechanisms requires development of process-specific biomarkers that could replace non-specific neuroimaging markers widely used in neuropsychiatric research. We further propose a framework for biomarker research that adopts a biologically informed perspective integrating the interactions between genes and the environment to address this problem. Such a multidimensional framework holds promise for developing biology-driven models of psychiatric disorders, enabling treatment strategies tailored to individual pathophysiology.

During late gestation and early postnatal development a combination of intrinsic and extrinsic factors drive the maturation of the human cortex. This process is regionally heterogeneous, with cortical areas developing at different paces and trajectories. Leveraging submillimetre T1-weighted/T2w-weighted (T1w/T2w) magnetic resonance imaging (MRI) from pre- and full-term neonates (n = 599, 26-44 weeks), we sampled intracortical profiles across the cortex and characterized the profiles' shapes according to their central moments. We found that gestational age at birth dominated the effects on early cortical development, with significant, global increases in intracortical homogeneity and a bimodal change in the balance of myelin-sensitive signal between superficial and deeper cortical layers. On the other hand, weeks since birth (i.e., postnatal age) exhibited different effects on myelin, with increasing intracortical heterogeneity and intracortical balance only shifting towards deeper layers in posterior temporal, occipital, medial parietal areas and some prefrontal areas. These effects align with low spatial-frequency geometric eigenmodes of the human cortex, specifically the anterior-posterior and superior-inferior axes. Our findings demonstrate that separating prenatal from postnatal influences, and analyzing intracortical profiles rather than macroscale features, provides finer-grained insights into how human cortical myelin changes during perinatal development and lays the groundwork for investigating the biological underpinnings that govern normative cortical maturation.

The human thalamus is composed of multiple nuclei that differ in structure and function. From early development onwards, these nuclei form reciprocal, nucleus-specific connections with the cerebral cortex, contributing to sensory and cognitive processing. In childhood and adolescence, a key period of neurocognitive development, these connections undergo widespread refinement, yet how developmental trajectories of thalamocortical connections vary across nuclei remains unknown. Here, we leveraged the Human Connectome Project in Development dataset (HCP-D, N = 604, age range 8-21) and segmented 10 thalamic nuclei using a segmentation approach optimized for intrathalamic contrast. Applying probabilistic tractography, we reconstructed nucleus-specific thalamocortical connections and charted their maturational profiles based on changes in fractional anisotropy (FA) using generalized additive models. We found FA to increase in thalamocortical connections, with nucleus-specific variation in temporal profiles and magnitude of age effects. Connections of core-cell-rich, sensory-projecting nuclei, such as the lateral geniculate nucleus, showed earlier maturational plateaus, whereas matrix-cell-rich, association-projecting nuclei, such as ventral anterior nucleus, showed more sustained maturation. This links maturational heterochronicity to thalamic organization of cell distribution and connectivity embedding. In parallel, functional thalamocortical connectivity decreased with age, with FA and functional connectivity age effects coupled in nucleus-connections showing prolonged maturation. Finally, concordant age effects in connectivity and nucleus volumes suggest that intra-nucleus remodeling may support refinement of structural connections while reducing thalamocortical functional synchrony. Together, our work reveals that thalamocortical maturation is anchored in the developmental and organizational heterogeneity of thalamic nuclei, offering a framework for understanding how diverse thalamic nuclei contribute to neurocognitive development.

Objective:Sleep health and depression are interconnected multidimensional constructs, yet their shared determinants remain obscure. Understanding the role of socioeconomic/lifestyle factors in predicting sleep-related depression (SRD) is critical for preventive strategies. This study aimed to identify the key socioeconomic/lifestyle predictors of SRD in the general population and patients with clinical depression. Methods:To characterize SRD, we performed regularized canonical correlation analysis between sleep and depression to identify latent phenotypes of SRD in a general population subsample (GP1; n□=□87,405) from the UK Biobank. Subsequently, machine-learning predictive models were developed in GP1 to predict SRD using socioeconomic/lifestyle factors. The best-performing predictive model was subsequently validated in GP2 at both baseline and follow-up (GP2; n□=□5,187), and in clinical depression (n□=□7,454) to assess its generalizability. Complementary analyses were conducted to assess other latent phenotypes (i.e., depression-related sleep, non-SRD, non-depression-related sleep, overall sleep health, and overall depression). Results:A robust multivariate association was identified between sleep and depression in GP1 (canonical r = 0.42, P FDR < 0.001). Socioeconomic/lifestyle factors moderately predicted SRD (r = 0.25; 95% CI: [0.24, 0.25]; R² = 0.06; 95% CI: [0.06, 0.06]; rMSE = 1.08; 95% CI: [1.08, 1.09]). The top predictors were less frequency of confiding in others, more sedentary television viewing, less vigorous physical activity, and passive smoking exposure. Out-of-sample validation of the predictive model showed similar patterns in GP2 at baseline, at follow-up, and in clinical depression subsamples. Similarly, less frequency of confiding in others and greater sedentary television viewing were the main predictors of other depression-related profiles, whereas more alcohol consumption frequency, less walking frequency, and less time spent outdoors in winter predicted poor sleep-related profiles. Conclusions:Our generalizable predictive model identifies critical modifiable predictors of the association between sleep health and depression that could serve as potential targets for personalized interventions.