Sleep disturbances and Alzheimer's disease (AD) are interconnected public health challenges. However, the underlying mechanisms of their complex relationships remain elusive. Here, we propose a hypothetical integrative, stream-like model outlining how external and internal exposome factors accelerate brain aging, thereby exacerbating circadian dysregulation, orexin-mediated hyperexcitability, metabolic imbalance, and inflammaging. These changes can lead to increased sleep fragmentation and reduced slow-wave sleep, triggering widespread neuroinflammation, glymphatic dysfunction, and the accumulation and dissemination of beta-amyloid and tau peptides. These processes collectively accelerate synaptic dysfunction, neuronal loss, and cognitive decline. We also highlight recent neuroimaging evidence that elucidates the neural substrates underlying the relationship between poor sleep and AD. Moreover, tackling their shared burden necessitates the active consideration of inter-individual variability in vulnerable populations through artificial intelligence and computational approaches, aligning with the core tenets of precision medicine. We hope this review encourages clinicians to prioritize monitoring and treating sleep disturbances to reduce the incidence, severity, and consequences of dementia in the general population.

Motor performance (MP) is essential for maintaining functional independence, particularly in later life. However, the relationship between MP and sleep quality, depressive symptoms, and their underlying brain substrates remains obscure. We employed four samples of younger/mid-to-older adults (n = 1,954) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS) to assess the replicability of our findings. Using canonical correlation analyses within a machine learning framework, we investigated the associations of sleep quality, depressive symptoms, and grey matter volume (GMV) with MP. In the combined model of the HCP-YA sample, a canonical variate of better sleep, mild, sub-clinical depressive symptoms, and altered GMV of several cortical (including precentral and fusiform gyrus), thalamus, and cerebellar brain regions was associated with a canonical variate of better MP (r = 0.2, SD = 0.05). This pattern was conceptually replicated in the young eNKI-RS sample (r = 0.25, SD = 0.13). In the HCP-A sample, a variate of better sleep quality, fewer depressive symptoms, and increased GMV was associated with a variate of MP (r = 0.18, SD = 0.1), but these findings did not replicate in the mid-to-older eNKI-RS sample (r = 0, SD = 0.12). Across all samples, variates of increased GMV were associated with variates of better MP, suggesting potential neuroanatomical underpinnings. We observed age-related variations in the multivariate associations between sleep quality, depressive symptoms, and GMV with MP.

The physical and social exposome affects human aging, and brain clocks may track its effects. However, most studies neglect multidomain exposures (physical, social and political) across diverse settings globally and their associations with brain aging. In this study, we characterized the associations between 73 country-level physical and social exposomal factors and multimodal brain age in 18,701 participants from 34 countries (healthy individuals and those with Alzheimer's disease, frontotemporal lobar degeneration or mild cognitive impairment). Exposome effects were assessed using generalized additive models and meta-analytic frameworks. Aggregated exposome models explained up to 15.5-fold more variance than individual exposures (delta Akaike information criterion (ΔAIC): 2,034-3,127). Physical exposome was primarily associated with accelerated structural brain aging (limbic, subcortical and cerebellar regions), whereas social exposome was more strongly associated with functional brain aging (frontotemporal and limbic networks). Exposome burden accounted for 3.3-9.1-fold higher risk of accelerated aging, exceeding effects of clinical diagnoses. Findings were out-of-sample validated in cross-sectional and longitudinal designs, remained consistent across clinical subgroups and persisted after adjustment for demographics, age correction bias, cognition, scanner type and data quality. The exposome accelerates brain aging in health and disease, underscoring the need to address physical, social and political inequities.

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.

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.

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.

Research using the multidimensional sleep health (MDSH) framework has increased globally, often relying on self-report measures. The Ru-SATED scale and Sleep Health Index (SHI) are common self-report measures of MDSH, but comparative data on their measurement properties and contextual characteristics remain limited. Seven electronic databases were searched for measurement properties and uses of the two scales over the past twelve years. This review identified 19 psychometric validation studies concerning two original and 17 cross-cultural, and summarized contextual comparison of MDSH measures and frameworks. Measurement properties of both measures were assessed with the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guideline, and contextual comparisons were conducted narratively. Both measures exhibited acceptable psychometric properties across diverse cultural settings, with the SHI findings showing greater consistency than those of the Ru-SATED scale. Aggregating the Ru-SATED and SHI frameworks fully covered the sleep characteristics assessed by five instruments grounded in the World Sleep Society initiative, encompassing regularity, satisfaction, alertness, timing, efficiency, duration, and disorder. Notably, the SHI framework incorporates targeted sleep disorder assessment while the Ru-SATED framework specifically excludes such assessment, highlighting the distinct focus and scope of each tool. Instrument selection depends primarily on research purpose, study sample, and intended use. We recommend characterizing both sleep health and sleep disorders to fully capture the complex relationships between sleep and health outcomes.

Sleep is a non-negotiable necessity for humans and animals and a cornerstone of health. Research is recently undergoing a paradigm shift, moving from a focus on individual factors to macro-level influences on sleep health. The physical, social, and lifestyle exposome increasingly disrupts human and animal sleep, highlighting shared vulnerabilities and potential health risks across species within shared ecosystems. Yet, the interactions among sleep, whole-body health, and exposome factors remain elusive. Here, we propose a novel integrative One Sleep Health framework that recognizes sleep as a fundamental pillar of planetary health and highlights how environmental factors influence it in the modern era. It incorporates the sleep capital concept-the cumulative health, social, and economic benefits of high sleep quality-into a global sleep health agenda. By adopting a transdisciplinary lens encompassing neuroscience, medicine, environmental science, and public health, this framework aims to bridge critical gaps in global sleep research and diplomacy.

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.

Rapid eye movement (REM) sleep behaviour disorder (RBD), particularly its idiopathic/isolated form (iRBD), is a prodromal marker for α-synucleinopathies, including Parkinson's disease, dementia with Lewy bodies and multiple system atrophy. Machine learning (ML) offers opportunities to improve diagnosis and risk stratification in this high-risk group. We conducted a systematic review of PubMed, Embase (Ovid) and Medline (Ovid) from 2014 to September 2025, following PRISMA guidelines. From 335 records identified, 202 remained after duplicate removal and 75 studies on adult humans with clinically diagnosed RBD or iRBD that applied and validated an ML model were included. Fifty-eight studies addressed diagnosis, four studied RBD phenotypes, and thirteen evaluated prediction of phenoconversion to overt α-synucleinopathy. Across diagnostic studies, reported accuracies ranged from ∼63% to ∼99.7%, with median values around 90%, using polysomnography, EEG, neuroimaging, molecular and behavioural markers. Phenoconversion models (often using dopaminergic imaging or multimodal features) achieved AUCs up to ∼0.94, but frequently relied on small, single-centre cohorts with heterogeneous definitions of phenoconversion and limited external validation. A wide variety of ML algorithms was used (n ~ 30), most commonly support vector machines, random forests and logistic regression. Overall, ML approaches show promise for scalable diagnosis and risk stratification in iRBD, but progress is constrained by methodological bias, inconsistent endpoints, data imbalance and a lack of explainable, externally validated models. We outline methodological priorities to make future ML tools clinically interpretable and translatable.

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.

Numerous correlational and group comparison studies have demonstrated robust associations between sleep health (SH) and large-scale brain organization. However, individual differences play a critical role in this relationship, highlighting the need for person-specific analyses. In this study, we aimed to explore whether multiple brain imaging features could predict various SH-related traits at the individual level using machine learning (ML) techniques. We utilized data from 28 088 participants in the UK Biobank, extracting 4677 structural and functional neuroimaging markers. These features were then used to predict a range of self-reported sleep characteristics, including insomnia symptoms, sleep duration, ease of waking in the morning, chronotype, napping behaviour, daytime sleepiness and snoring. For each of these seven traits, we trained both linear and nonlinear ML models to evaluate how well brain imaging data could account for individual differences. Our analyses involved extensive computational resources, equivalent to over 200 000 core-hours (equivalent to 25 years of compute time). Despite this, the predictive performance of brain features was consistently low across all models, with balanced accuracy scores ranging from 0.50 to 0.59. The highest accuracy achieved (0.59) came from a linear model predicting the ease of getting up in the morning. Notably, models using only demographic variables such as age and sex achieved comparable performance, suggesting that these basic characteristics may largely explain the observed variability. These findings indicate that, even when using a large, well-powered sample and advanced ML techniques, multi-modal brain imaging features provide limited predictive value for SH at the individual level. This low predictability underscores the complexity of the relationship between self-reported sleep and brain structure/function. It also suggests that other biological, environmental or psychological factors-possibly not captured by current imaging modalities-may play a more substantial role in shaping sleep-related behaviours.

BACKGROUND:Previous research has suggested an association between insomnia disorder (ID) and alterations in emotion processing. Therefore, it is crucial to investigate neurobiological changes in emotion processing in patients with ID before and after Cognitive Behavioral Therapy for Insomnia (CBT-I) and to compare them with healthy controls (HC) with task-based functional magnetic resonance imaging (fMRI). METHODS:20 patients with ID and 20 HC were included in this study to view five different blocks of pictures with varying emotional arousal, valence, and content (sleep-relatedness) in the fMRI scanner. RESULTS:A significant Group × Session interaction was identified in the amygdala for both the sleep-related negative contrast (F(1,38) = 4.19, p = .048) and the neutral moderate contrast (F(1,38) = 5.39, p = .026). Post-hoc tests revealed that patients with ID had a significantly higher average amygdala reactivity to sleep-related stimuli at T0, whereas no significant difference was observed between the groups at T1. However, the analysis of Intraclass Correlation Coefficients (ICC) in the control group suggests a very low retest reliability across all fMRI measures. CONCLUSIONS:CBT-I may normalise amygdala responses to sleep-related negative stimuli, which may reflect a shift toward improved emotional processing. However, the very low retest reliability of fMRI measures warrants cautious interpretation of these results.

Insomnia affects a substantial proportion of the population and frequently co-occurs with mental illnesses including depression and anxiety. However, the neurobiological correlates of these disorders remain unclear. Here we review magnetic resonance imaging (MRI) studies assessing structural and functional brain associations with depressive and anxiety symptoms in insomnia disorder (ID; n = 38), insomnia symptoms in depressive and anxiety disorders (n = 14), and these symptoms in the general populations (n = 3). The studies on insomnia disorder consistently showed overlapping (salience network: insula and anterior cingulate cortex) and differential MRI correlation patterns between depressive (thalamus, orbitofrontal cortex and its associated functional connectivity) and anxiety (functional connectivity associated with default mode network) symptoms. The insula was also consistently identified as indicating the severity of insomnia symptoms in depressive disorder. In contrast, findings for other regions related to insomnia symptoms in both depressive and anxiety disorders were generally inconsistent across studies, partly due to variations in methods and patient cohorts. In the general population, brain regions in the default mode network provided a functional link between insomnia and depressive symptoms. These findings underscore both the shared and distinct neural correlates among depression, anxiety, and insomnia, providing potential avenues for the clinical management of these conditions.

The link between brain health and risk/protective factors for non-communicable diseases (such as high blood pressure, high body mass index, diet, smoking, physical activity, etc.) is increasingly acknowledged. However, the specific effects that these factors have on brain health are still poorly understood, delaying their implementation in precision brain health. Here, we studied the multivariate relationships between risk factors for non-communicable diseases and brain structure, including cortical thickness (CT) and gray matter volume (GMV). Furthermore, we adopted a systems-level perspective to understand such relationships, by characterizing the cortical patterns (yielded in association to risk factors) with regards to brain morphological and functional features, as well as with neurotransmitter systems. Similarly, we related the pattern of risk/protective factors dimensions with a peripheral marker of inflammation. First, we identified latent dimensions linking a broad set of risk factors for non-communicable diseases to parcel-wise CT and GMV across the whole cortex. Data was obtained from the UK Biobank (n = 7,370, age range = 46-81 years). We used regularized canonical correlation analysis (RCCA) embedded in a machine learning framework. This approach allows us to capture inter-individual variability in a multivariate association and to assess the generalizability of the model. The brain patterns (captured in association with risk/protective factors) were characterized from a multi-level perspective, by performing correlations (spin tests) between them and different brain patterns of structure, function, and neurotransmitter systems. The association between the risk/protective factors pattern and C-reactive protein (CRP, a marker of inflammation) was examined using Spearman correlation. We found two significant and partly replicable latent dimensions. One latent dimension linked cardiometabolic health to brain patterns of CT and GMV and was consistent across sexes. The other latent dimension linked physical robustness (including non-fat mass and strength) to patterns of CT and GMV, with the association to GMV being consistent across sexes and the association to CT appearing only in men. The CT and GMV patterns of both latent dimensions were associated to the binding potentials of several neurotransmitter systems. Finally, the cardiometabolic health dimension was correlated to CRP, while physical robustness was only very weakly associated to it. We observed robust, multi-level and multivariate links between both cardiometabolic health and physical robustness with respect to CT, GMV, and neurotransmitter systems. Interestingly, we found that cardiometabolic health and physical robustness are associated with not only increases in CT or GMV, but also with decreases of CT or GMV in some brain regions. Our results also suggested a role for low-grade chronic inflammation in the association between cardiometabolic health and brain structural health. These findings support the relevance of adopting a holistic perspective in health, by integrating neurocognitive and physical health. Moreover, our findings contribute to the challenge to the classical conceptualization of neuropsychiatric and physical illnesses as categorical entities. In this perspective, future studies should further examine the effects of risk/protective factors on different brain regions in order to deepen our understanding of the clinical significance of such increased and decreased CT and GMV.

BACKGROUND:Late-life depression (LLD) is prevalent in older adults and linked to increased disability, mortality, and suicide risk. Insomnia symptoms are considered common remaining symptoms of LLD following treatment. However, the multivariate relationship between insomnia and depressive symptoms and the impact of psychotherapy on their interrelationship is insufficiently assessed. METHODS:We conducted a secondary analysis of data from 185 patients with LLD, recruited from seven university hospitals in Germany as part of a larger original cohort study. Participants had undergone eight-week psychotherapy interventions (cognitive behavioral therapy or supportive unspecific intervention). Three regularized canonical correlation analyses (rCCA) assessed the multivariate association between insomnia and depressive symptoms at baseline, post-treatment, and six-month follow-up. rCCA was conducted within a machine learning framework with 100 repeated hold-out splits and permutation tests to ensure robust findings. Canonical loadings and cross-loading difference scores were calculated to examine symptom changes before/after psychotherapy (Holm-Bonferroni corrected p-value <0.05). RESULTS:At baseline, a moderate association was observed between insomnia and depressive symptoms (r = 0.24). Interestingly, this association slightly increased after the eight-week treatment period (r = 0.42, pcorrected = 0.064) and remained significantly elevated at the follow-up session (r = 0.48, pcorrected = 0.018). At baseline, anxiety-related depressive symptoms were mainly associated with insomnia, while at post-treatment and follow-up sessions, somatic and negative affective symptoms showed the strongest correlation with insomnia symptoms. While the relative relationship of depressive symptoms with insomnia altered after psychotherapy, the pattern of insomnia symptoms remained stable. CONCLUSIONS:The observed changes in the association between insomnia and depressive symptoms after psychotherapy highlight the necessity to consider targeting insomnia for effective LLD treatment.

Sleep appears to modulate brain-wide neurofluid transport, encompassing the movement and exchange of cerebrospinal and interstitial fluids via perivascular pathways. However, neurofluid transport in common sleep disorders, such as insomnia disorder and obstructive sleep apnea, requires further assessment. In this study, we recruited 159 participants: patients with moderate to severe obstructive sleep apnea (n = 36) or chronic insomnia disorder (n = 62), and healthy controls (n = 61). Participants underwent structural magnetic resonance imaging, polysomnography, the Pittsburgh Sleep Quality Index, and the STOP-Bang questionnaires. Here, neurofluid transport is indirectly assessed using two noninvasive MRI indices (i.e., the perivascular space volume fraction and diffusion tensor imaging along perivascular spaces). Patients with obstructive sleep apnea exhibited a significantly larger perivascular space volume fraction compared with patients with insomnia disorder (p = 0.042) and healthy controls (p = 0.032), whereas no group differences were observed for the diffusion-based index. Partial correlation analyses, adjusted for age, sex, and body mass index, revealed that in obstructive sleep apnea, a larger perivascular space volume fraction was associated with less sleep disturbance (r = -0.35, p = 0.04), and diffusion measures increased with snoring severity (r = 0.38, p = 0.03). In insomnia disorder, a larger perivascular space volume fraction was associated with a higher nocturnal wake index (r = 0.38, p = 0.006) and an elevated risk of blood pressure (r = 0.50, p < 0.001), while inversely relating to subjective sleep quality (r = -0.35, p = 0.01). Our results highlight different patterns of neurofluid transport alterations across obstructive sleep apnea and insomnia disorder.