Access to psychedelic drugs is liberalizing, yet responses are highly unpredictable. It is therefore imperative that we improve our ability to predict the nature of the acute psychedelic experience to improve safety and optimize potential therapeutic outcomes. This study sought to validate the ‘Imperial Psychedelic Predictor Scale’ (IPPS), a short, widely applicable, prospective measure intended to be predictive of salient dimensions of the psychedelic experience.

Using four independent datasets in which the IPPS was completed prospectively – two online surveys of ‘naturalistic’ use (N = 741, N = 836) and two controlled administration datasets (N = 30, N = 28) – we conducted factor analysis, regression, and correlation analyses to assess the construct, predictive, and convergent validity of the IPPS.

Our approach produced a 9-item scale with good internal consistency (Cronbach's α = 0.8) containing three factors: set, rapport, and intention. The IPPS was significantly predictive of ‘mystical’, ‘challenging’, and ‘emotional breakthrough’ experiences. In a controlled administration dataset (N = 28), multiple regression found set and rapport explaining 40% of variance in mystical experience, and simple regression found set explained 16% of variance in challenging experience. In another (N = 30), rapport was related to emotional breakthrough explaining 9% of variance.

Together, these data suggest that the IPPS is predictive of relevant acute features of the psychedelic experience in a broad range of contexts. We hope that this brief 9-item scale will be widely adopted for improved knowledge of psychedelic preparedness in controlled settings and beyond.

Different aspects of social relationships (e.g., social network size or loneliness) have been associated with dementia risk, while their overlap and potentially underlying pathways remain largely unexplored. This study therefore aimed to (1) discriminate between different facets of social relationships by means of factor analysis, (2) examine their associations with dementia risk, and (3) assess mediation by depressive symptoms.

Thirty-six items from questionnaires on social relationships administered in Wave 2 (2004/2005) of the English Longitudinal Study of Ageing (n = 7536) were used for exploratory and confirmatory factor analysis. Factors were then used as predictors in Cox proportional hazard models with dementia until Wave 9 as outcome, adjusted for demographics and cardiovascular risk factors. Structural equation modeling tested mediation by depressive symptoms through effect decomposition.

Factor analyses identified six social factors. Across a median follow-up time of 11.8 years (IQR = 5.9–13.9 years), 501 people developed dementia. Higher factor scores for frequency and quality of contact with children (HR = 0.88; p = 0.021) and more frequent social activity engagement (HR = 0.84; p < 0.001) were associated with lower dementia risk. Likewise, higher factor scores for loneliness (HR = 1.13; p = 0.011) and negative experiences of social support (HR = 1.10; p = 0.047) were associated with higher dementia risk. Mediation analyses showed a significant partial effect mediation by depressive symptoms for all four factors. Additional analyses provided little evidence for reverse causation.

Frequency and quality of social contacts, social activity engagement, and feelings of loneliness are associated with dementia risk and might be suitable targets for dementia prevention programs, partly by lowering depressive symptoms.

A minority of naturally cycling individuals experience clinically significant affective changes across the menstrual cycle. However, few studies have examined cognitive and behavioral constructs that may maintain or worsen these changes. Several small studies link rumination with premenstrual negative affect, with authors concluding that a tendency to ruminate amplifies and perpetuates hormone-sensitive affective symptoms. Replication in larger samples is needed to confirm the validity of rumination as a treatment target.

190 cycling individuals (M = 30.82 years; 61.1% Caucasian) were recruited for moderate perceived stress, a risk factor for cyclical symptoms. They completed the Rumination Response Scale at baseline, then reported daily affective and physical symptoms across 1–6 cycles. Multilevel growth models tested trait rumination as a predictor of baseline levels, luteal increases, and follicular decreases in symptoms.

The degree of affective cyclicity was normally distributed across a substantial range, supporting feasibility of hypothesis tests and validating the concept of dimensional hormone sensitivity. Contrary to prediction, higher brooding did not predict levels or cyclical changes of any symptom. In a subsample selected for luteal increases in negative affect, brooding predicted higher baseline negative affect but still did not predict affective cyclicity.

An individual's trait-like propensity to engage in rumination may not be a valid treatment target in premenstrual mood disorders. State-like changes in rumination should still be further explored, and well-powered prospective studies should explore other cognitive and behavioral factors to inform development of targeted psychological treatments for patients with cyclical affective symptoms.

Major depressive disorder (MDD) is the leading cause of disability globally, with moderate heritability and well-established socio-environmental risk factors. Genetic studies have been mostly restricted to European settings, with polygenic scores (PGS) demonstrating low portability across diverse global populations.

This study examines genetic architecture, polygenic prediction, and socio-environmental correlates of MDD in a family-based sample of 10 032 individuals from Nepal with array genotyping data. We used genome-based restricted maximum likelihood to estimate heritability, applied S-LDXR to estimate the cross-ancestry genetic correlation between Nepalese and European samples, and modeled PGS trained on a GWAS meta-analysis of European and East Asian ancestry samples.

We estimated the narrow-sense heritability of lifetime MDD in Nepal to be 0.26 (95% CI 0.18–0.34, p = 8.5 × 10−6). Our analysis was underpowered to estimate the cross-ancestry genetic correlation (rg = 0.26, 95% CI −0.29 to 0.81). MDD risk was associated with higher age (beta = 0.071, 95% CI 0.06–0.08), female sex (beta = 0.160, 95% CI 0.15–0.17), and childhood exposure to potentially traumatic events (beta = 0.050, 95% CI 0.03–0.07), while neither the depression PGS (beta = 0.004, 95% CI −0.004 to 0.01) or its interaction with childhood trauma (beta = 0.007, 95% CI −0.01 to 0.03) were strongly associated with MDD.

Estimates of lifetime MDD heritability in this Nepalese sample were similar to previous European ancestry samples, but PGS trained on European data did not predict MDD in this sample. This may be due to differences in ancestry-linked causal variants, differences in depression phenotyping between the training and target data, or setting-specific environmental factors that modulate genetic effects. Additional research among under-represented global populations will ensure equitable translation of genomic findings.

The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

Many autistic people in mental health are suicidal. This study evaluated the effectiveness of dialectical behavior therapy (DBT) v. treatment as usual (TAU) in reducing suicidal ideation and suicide attempts.

At six Dutch mental health centers, 123 outpatients (18–65 years) with DSM-5 diagnosed autism spectrum disorder (ASD) and suicidal behavior were randomly assigned to the DBT intervention group (n = 63) or TAU control group (n = 60). Assessments were conducted at baseline, post-treatment at 6 months and 12-month follow-up. The primary outcomes were severity of suicidal ideation and frequency of suicide attempts. The severity of depression and social anxiety were secondary outcomes.

At end-of-treatment, DBT significantly reduced both suicidal ideation (z = −2.24; p = 0.025; b = −4.41; s.e. = 197.0) and suicide attempts (z = −3.15; p = 0.002; IRR = 0.046; s.e. = 0.045) compared to TAU, but lost statistical significance at the 12-month follow-up. Depression severity significantly decreased with DBT (z = −1.99; p = 0.046: b = −2.74; s.e. = 1.37) remaining so at 12 months (z = −2.46; p = 0.014; b = −3.37; s.e. = 1.37). No effects were observed on social anxiety. Severe adverse events included two suicides in the TAU condition.

DBT is an acceptable, safe, and short-term effective intervention to reduce suicidal ideation and suicide attempts in autistic adults with suicidal behavior.

Physical sequelae of anorexia nervosa (AN) include a marked reduction in whole brain volume and subcortical structures such as the hippocampus. Previous research has indicated aberrant levels of inflammatory markers and growth factors in AN, which in other populations have been shown to influence hippocampal integrity.

Here we investigated the influence of concentrations of two pro-inflammatory cytokines (tumor necrosis factor-alpha [TNF-α] and interleukin-6 [IL-6]) and brain-derived neurotrophic factor (BDNF) on the whole hippocampal volume, as well as the volumes of three regions (the hippocampal body, head, and tail) and 18 subfields bilaterally. Investigations occurred both cross-sectionally between acutely underweight adolescent/young adult females with AN (acAN; n = 82) and people recovered from AN (recAN; n = 20), each independently pairwise age-matched with healthy controls (HC), and longitudinally in acAN after partial renourishment (n = 58). Hippocampal subfield volumes were quantified using FreeSurfer. Concentrations of molecular factors were analyzed in linear models with hippocampal (subfield) volumes as the dependent variable.

Cross-sectionally, there was no evidence for an association between IL-6, TNF-α, or BDNF and between-group differences in hippocampal subfield volumes. Longitudinally, increasing concentrations of BDNF were positively associated with longitudinal increases in bilateral global hippocampal volumes after controlling for age, age2, estimated total intracranial volume, and increases in body mass index (BMI).

These findings suggest that increases in BDNF may contribute to global hippocampal recovery over and above increases in BMI during renourishment. Investigations into treatments targeted toward increasing BDNF in AN may be warranted.

Individuals with major depressive disorder (MDD) can experience reduced motivation and cognitive function, leading to challenges with goal-directed behavior. When selecting goals, people maximize ‘expected value’ by selecting actions that maximize potential reward while minimizing associated costs, including effort ‘costs’ and the opportunity cost of time. In MDD, differential weighing of costs and benefits are theorized mechanisms underlying changes in goal-directed cognition and may contribute to symptom heterogeneity.

We used the Effort Foraging Task to quantify cognitive and physical effort costs, and patch leaving thresholds in low effort conditions (reflecting perceived opportunity cost of time) and investigated their shared versus distinct relationships to clinical features in participants with MDD (N = 52, 43 in-episode) and comparisons (N = 27).

Contrary to our predictions, none of the decision-making measures differed with MDD diagnosis. However, each of the measures was related to symptom severity, over and above effects of ability (i.e. performance). Greater anxiety symptoms were selectively associated with lower cognitive effort cost (i.e. greater willingness to exert effort). Anhedonia and behavioral apathy were associated with increased physical effort costs. Finally, greater overall depression was related to decreased patch leaving thresholds.

Markers of effort-based decision-making may inform understanding of MDD heterogeneity. Increased willingness to exert cognitive effort may contribute to anxiety symptoms such as worry. Decreased leaving threshold associations with symptom severity are consistent with reward rate-based accounts of reduced vigor in MDD. Future research should address subtypes of depression with or without anxiety, which may relate differentially to cognitive effort decisions.

Previous observational epidemiological studies have suggested that coffee consumption during pregnancy may affect fetal neurodevelopment. However, results are inconsistent and may represent correlational rather than causal relationships. The present study investigated whether maternal coffee consumption was observationally associated and causally related to offspring childhood neurodevelopmental difficulties (NDs) in the Norwegian Mother, Father and Child Cohort Study.

The observational relationships between maternal/paternal coffee consumption (before and during pregnancy) and offspring NDs were assessed using linear regression analyses (N = 58694 mother-child duos; N = 22 576 father-child duos). To investigate potential causal relationships, individual-level (N = 46 245 mother-child duos) and two-sample Mendelian randomization (MR) analyses were conducted using genetic variants previously associated with coffee consumption as instrumental variables.

We observed positive associations between maternal coffee consumption and offspring difficulties with social-communication/behavioral flexibility, and inattention/hyperactive-impulsive behavior (multiple testing corrected p < 0.005). Paternal coffee consumption (negative control) was not observationally associated with the outcomes. After adjusting for potential confounders (smoking, alcohol, education and income), the maternal associations attenuated to the null. MR analyses suggested that increased maternal coffee consumption was causally associated with social-communication difficulties (individual-level: beta = 0.128, se = 0.043, p = 0.003; two-sample: beta = 0.348, se = 0.141, p = 0.010). However, individual-level MR analyses that modelled potential pleiotropic pathways found the effect diminished (beta = 0.088, se = 0.049, p = 0.071). Individual-level MR analyses yielded similar estimates (heterogeneity p = 0.619) for the causal effect of coffee consumption on social communication difficulties in maternal coffee consumers (beta = 0.153, se = 0.071, p = 0.032) and non-consumers (beta = 0.107, se = 0.134, p = 0.424).

Together, our results provide little evidence for a causal effect of maternal coffee consumption on offspring NDs.

Although the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk, such as suicidal ideation, prior suicide attempts, and recent psychiatric hospitalization. Approximately 90% of VA patients that go on to die by suicide do not meet these high-risk criteria and therefore do not receive targeted suicide prevention services. In this study, we used national VA data to focus on patients that were not classified as high-risk, but died by suicide.

Our sample included all VA patients who died by suicide in 2017 or 2018. We determined whether patients were classified as high-risk using the VA's machine learning risk prediction algorithm. After excluding these patients, we used principal component analysis to identify moderate-risk and low-risk patients and investigated demographics, service-usage, diagnoses, and social determinants of health differences across high-, moderate-, and low-risk subgroups.

High-risk (n = 452) patients tended to be younger, White, unmarried, homeless, and have more mental health diagnoses compared to moderate- (n = 2149) and low-risk (n = 2209) patients. Moderate- and low-risk patients tended to be older, married, Black, and Native American or Pacific Islander, and have more physical health diagnoses compared to high-risk patients. Low-risk patients had more missing data than higher-risk patients.

Study expands epidemiological understanding about non-high-risk suicide decedents, historically understudied and underserved populations. Findings raise concerns about reliance on machine learning risk prediction models that may be biased by relative underrepresentation of racial/ethnic minorities within health system.

Severe fatigue and cognitive complaints are frequently reported after SARS-CoV-2 infection and may be accompanied by depressive symptoms and/or limitations in physical functioning. The long-term sequelae of COVID-19 may be influenced by biomedical, psychological, and social factors, the interplay of which is largely understudied over time. We aimed to investigate how the interplay of these factors contribute to the persistence of symptoms after COVID-19.

RECoVERED, a prospective cohort study in Amsterdam, the Netherlands, enrolled participants aged⩾16 years after SARS-CoV-2 diagnosis. We used a structural network analysis to assess relationships between biomedical (initial COVID-19 severity, inflammation markers), psychological (illness perceptions, coping, resilience), and social factors (loneliness, negative life events) and persistent symptoms 24 months after initial disease (severe fatigue, difficulty concentrating, depressive symptoms and limitations in physical functioning). Causal discovery, an explorative data-driven approach testing all possible associations and retaining the most likely model, was performed.

Data from 235/303 participants (77.6%) who completed the month 24 study visit were analysed. The structural model revealed associations between the putative factors and outcomes. The outcomes clustered together with severe fatigue as its central point. Loneliness, fear avoidance in response to symptoms, and illness perceptions were directly linked to the outcomes. Biological (inflammatory markers) and clinical (severity of initial illness) variables were connected to the outcomes only via psychological or social variables.

Our findings support a model where biomedical, psychological, and social factors contribute to the development of long-term sequelae of SARS-CoV-2 infection.

Pre-diagnostic stages of psychotic illnesses, including ‘clinical high risk’ (CHR), are marked by sleep disturbances. These sleep disturbances appear to represent a key aspect in the etiology and maintenance of psychotic disorders. We aimed to examine the relationship between self-reported sleep dysfunction and attenuated psychotic symptoms (APS) on a day-to-day basis.

Seventy-six CHR young people completed the Experience Sampling Methodology (ESM) component of the European Union Gene-Environment Interaction Study, collected through PsyMate® devices, prompting sleep and symptom questionnaires 10 times daily for 6 days. Bayesian multilevel mixed linear regression analyses were performed on time-variant ESM data using the brms package in R. We investigated the day-to-day associations between sleep and psychotic experiences bidirectionally on an item level. Sleep items included sleep onset latency, fragmentation, and quality. Psychosis items assessed a range of perceptual, cognitive, and bizarre thought content common in the CHR population.

Two of the seven psychosis variables were unidirectionally predicted by previous night's number of awakenings: every unit increase in number of nightly awakenings predicted a 0.27 and 0.28 unit increase in feeling unreal or paranoid the next day, respectively. No other sleep variables credibly predicted next-day psychotic symptoms or vice-versa.

In this study, the relationship between sleep disturbance and APS appears specific to the item in question. However, some APS, including perceptual disturbances, had low levels of endorsement amongst this sample. Nonetheless, these results provide evidence for a unidirectional relationship between sleep and some APS in this population.

Enlarged pituitary gland volume could be a marker of psychotic disorders. However, previous studies report conflicting results. To better understand the role of the pituitary gland in psychosis, we examined a large transdiagnostic sample of individuals with psychotic disorders.

The study included 751 participants (174 with schizophrenia, 114 with schizoaffective disorder, 167 with psychotic bipolar disorder, and 296 healthy controls) across six sites in the Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium. Structural magnetic resonance images were obtained, and pituitary gland volumes were measured using the MAGeT brain algorithm. Linear mixed models examined between-group differences with controls and among patient subgroups based on diagnosis, as well as how pituitary volumes were associated with symptom severity, cognitive function, antipsychotic dose, and illness duration.

Mean pituitary gland volume did not significantly differ between patients and controls. No significant effect of diagnosis was observed. Larger pituitary gland volume was associated with greater symptom severity (F = 13.61, p = 0.0002), lower cognitive function (F = 4.76, p = 0.03), and higher antipsychotic dose (F = 5.20, p = 0.02). Illness duration was not significantly associated with pituitary gland volume. When all variables were considered, only symptom severity significantly predicted pituitary gland volume (F = 7.54, p = 0.006).

Although pituitary volumes were not increased in psychotic disorders, larger size may be a marker associated with more severe symptoms in the progression of psychosis. This finding helps clarify previous inconsistent reports and highlights the need for further research into pituitary gland-related factors in individuals with psychosis.

Selective serotonin reuptake inhibitors (SSRIs) are first-line pharmacological treatments for depression and anxiety. However, little is known about how pharmacological action is related to cognitive and affective processes. Here, we examine whether specific reinforcement learning processes mediate the treatment effects of SSRIs.

The PANDA trial was a multicentre, double-blind, randomized clinical trial in UK primary care comparing the SSRI sertraline with placebo for depression and anxiety. Participants (N = 655) performed an affective Go/NoGo task three times during the trial and computational models were used to infer reinforcement learning processes.

There was poor task performance: only 54% of the task runs were informative, with more informative task runs in the placebo than in the active group. There was no evidence for the preregistered hypothesis that Pavlovian inhibition was affected by sertraline. Exploratory analyses revealed that in the sertraline group, early increases in Pavlovian inhibition were associated with improvements in depression after 12 weeks. Furthermore, sertraline increased how fast participants learned from losses and faster learning from losses was associated with more severe generalized anxiety symptoms.

The study findings indicate a relationship between aversive reinforcement learning mechanisms and aspects of depression, anxiety, and SSRI treatment, but these relationships did not align with the initial hypotheses. Poor task performance limits the interpretability and likely generalizability of the findings, and highlights the critical importance of developing acceptable and reliable tasks for use in clinical studies.

This article presents research supported by NIHR Program Grants for Applied Research (RP-PG-0610-10048), the NIHR BRC, and UCL, with additional support from IMPRS COMP2PSYCH (JM, QH) and a Wellcome Trust grant (QH).

Increased autocorrelation (AR) of system-specific measures has been suggested as a predictor for critical transitions in complex systems. Increased AR of mood scores has been reported to anticipate depressive episodes in major depressive disorder, while other studies found AR increases to be associated with depressive episodes themselves. Data on AR in patients with bipolar disorders (BD) is limited and inconclusive.

Patients with BD reported their current mood via daily e-diaries for 12 months. Current affective status (euthymic, prodromal, depressed, (hypo)manic) was assessed in 26 bi-weekly expert interviews. Exploratory analyses tested whether self-reported current mood and AR of the same item could differentiate between prodromal phases or affective episodes and euthymia.

A total of 29 depressive and 20 (hypo)manic episodes were observed in 29 participants with BD. Self-reported current mood was significantly decreased during the two weeks prior to a depressive episode (early prodromal, late prodromal), but not changed prior to manic episodes. The AR was neither a significant predictor for the early or late prodromal phase of depression nor for the early prodromal phase of (hypo)mania. Decreased AR was found in the late prodromal phase of (hypo)mania. Increased AR was mainly found during depressive episodes.

AR changes might not be better at predicting depressive episodes than simple self-report measures on current mood in patients with BD. Increased AR was mostly found during depressive episodes. Potentially, changes in AR might anticipate (hypo)manic episodes.

Drug use Disorder (DUD), the risk for which is substantially influenced by both genetic and social factors, is geographically concentrated in high-risk regions. An important step toward understanding this pattern is to examine geographical distributions of the genetic liability to DUD and a key demographic risk factor – social deprivation.

We calculated the mean family genetic risk score (FGRS) for DUD ((FGRSDUD) and social deprivation for each of the 5983 areas Demographic Statistical Areas (DeSO) for all of Sweden and used geospatial techniques to analyze and map these factors.

Using 2018 data, substantial spatial heterogeneity was seen in the distribution of the genetic risk for DUD in Sweden as a whole and in its three major urban centers which was confirmed by hot-spot analyses. Across DeSOs, FGRSDUD and s.d. levels were substantially but imperfectly correlated (r = + 0.63), with more scattering at higher FGRSDUD and s.d. scores. Joint mapping across DeSOs for FGRSDUD and s.d. revealed a diversity of patterns across Sweden. The stability of the distributions of FGRSDUD and s.d. in DeSOs within Sweden over the years 2012–2018 was quite high.

The geographical distribution of the genetic risk to DUD is quite variable in Sweden. DeSO levels of s.d. and FRGSDUD were substantially correlated but also disassociated in a number of regions. The observed patterns were largely consistent with known trends in the human geography of Sweden. This effort lays the groundwork for further studies of the sources of geographic variation in rates of DUD.

Dropout from healthcare interventions can negatively affect patients and healthcare providers through impaired trust in the healthcare system and ineffective use of resources. Research on this topic is still largely missing on refugees and asylum seekers. The current study aimed to characterize predictors for dropout in the Mental Health in Refugees and Asylum Seekers (MEHIRA) study, one of the largest multicentered controlled trials investigating the effectiveness and cost-effectiveness of a nationwide stepped and collaborative care model.

Predictors were multiply imputed and selected for descriptive modelling using backward elimination. The final variable set was entered into logistic regression.

The overall dropout rate was 41,7%. Dropout was higher in participants in group therapy (p = 0.001; OR = 10.7), with larger satisfaction with social relationships (p = 0.017; OR = 1.87), with difficulties in maintaining personal relationships (p = 0.005; OR = 4.27), and with higher depressive symptoms (p = 0.029; OR = 1.05). Participants living in refugee accommodation (p = 0.040; OR = 0.45), with a change in social status (p = 0.008; OR = 0.67) and with conduct (p = 0.020; OR = 0.24) and emotional problems (p = 0.013; OR = 0.31) were significantly less likely to drop out of treatment.

Overall, the outcomes of this study suggest that predictors assessing social relationships, social status, and living conditions should be considered as topics of psychological treatment to increase adherence and as predictors for future research studies (including treatment type).

Daylength and the rates of changes in daylength have been associated with seasonal fluctuations in psychiatric symptoms and in cognition and mood in healthy adults. However, variations in human brain glucose metabolism in concordance with seasonal changes remain under explored.

In this cross-sectional study, we examined seasonal effects on brain glucose metabolism, which we measured using 18F-fluorodeoxyglucose-PET in 97 healthy participants. To maximize the sensitivity of regional effects, we computed relative metabolic measures by normalizing the regional measures to white matter metabolism. Additionally, we explored the role of rest–activity rhythms/sleep–wake activity measured with actigraphy in the seasonal variations of regional brain metabolic activity.

We found that seasonal variations of cerebral glucose metabolism differed across brain regions. Glucose metabolism in prefrontal regions increased with longer daylength and with greater day-to-day increases in daylength. The cuneus and olfactory bulb had the maximum and minimum metabolic values around the summer and winter solstice respectively (positively associated with daylength), whereas the temporal lobe, brainstem, and postcentral cortex showed maximum and minimum metabolic values around the spring and autumn equinoxes, respectively (positively associated with faster daylength gain). Longer daylength was associated with greater amplitude and robustness of diurnal activity rhythms suggesting circadian involvement.

The current findings advance our knowledge of seasonal patterns in a key indicator of brain function relevant for mood and cognition. These data could inform treatment interventions for psychiatric symptoms that peak at specific times of the year.