Cognitive fatigue is a prevalent and disabling symptom in neurological and post-viral conditions, including multiple sclerosis (MS) and Long COVID. Assessment relies largely on self-report, and no validated objective biomarker exists, limiting reliable diagnosis and treatment monitoring. The aperiodic exponent of the Electroencephalogram (EEG) power spectrum, reflecting the excitation/inhibition (E/I) balance, is a promising candidate biomarker. We examined whether aperiodic exponent values can objectively identify pathological fatigue and assessed their classification accuracy.

We conducted a cross-sectional study, including 119 participants: 36 healthy controls, 33 with Long COVID-related fatigue (LCOF), and 50 with MS (23 fatigued and 27 nonfatigued). Resting-state EEGs were analyzed, and associations with fatigue ratings and group differences were assessed. Logistic mixed-effects regression models evaluated classification accuracy for fatigue status.

Lower frontal aperiodic exponents were associated with higher cognitive fatigue across participants. Fatigued individuals, regardless of diagnosis, showed reduced frontal exponent values compared with nonfatigued groups, while no differences emerged in occipital regions. Logistic regression confirmed that frontal exponent values significantly predicted fatigue status, improving classification accuracy beyond age and depression, with good sensitivity and specificity.

The frontal aperiodic exponent is a regionally specific biomarker of cognitive fatigue across MS and LCOF. Mechanistic interpretation suggests an altered prefrontal E/I balance, which could inform the development of targeted interventions to alleviate cognitive fatigue. It offers a clinically accessible tool to complement self-report, support trial stratification, and enable objective treatment monitoring. Importantly, its presence across distinct disorders highlights its value as a transdiagnostic marker of fatigue.

Substance use is sustained partly through implicit associations toward drugs – i.e. automatic positive attitudes and motivational responses toward drug-related cues. Such implicit associations may be inferred by behavioral measures that capture the relative ease, speed, or priming of those associations. However, implicit opioid associations in patients with opioid use disorder (OUD) remain underexplored, and it is unknown whether mindfulness-based interventions such as Mindfulness-Oriented Recovery Enhancement (MORE) can modify implicit associations to support recovery.

We conducted secondary analyses of data from a clinical trial of adults with OUD (N = 154), randomized to either methadone treatment as usual (TAU) or TAU plus MORE. Participants completed an opioid implicit association test (IAT) at baseline. Days of opioid use were tracked over 16 weeks. Data were analyzed using logistic and zero-inflated negative binomial (ZINB) regressions to examine the impact of baseline IAT scores on future opioid use and MORE’s moderating effect.

In the TAU group, each 1-unit increase in IAT D score was associated with a 216% increase in the odds of opioid use (OR = 3.16, p = 0.049). However, in the MORE group, IAT scores were not significantly associated with future opioid use (OR = 0.58, p = 0.57). ZINB analysis revealed that each 1-unit increase in IAT D score predicted 0.96 fewer days of use in MORE relative to TAU (B = –1.25; SE = 0.58; p = 0.030).

Implicit attitudes toward opioids predicted higher opioid use among individuals receiving methadone. However, MORE attenuated this relationship and may counteract automatic cognitive biases that sustain opioid use.

Hypochondriasis, or health anxiety disorder, is associated with increased mortality, mainly from potentially preventable causes. Substance misuse is a well-known contributor to premature death, yet its relationship with hypochondriasis remains unclear. We assessed the risk of broadly defined substance-related problems in individuals diagnosed with hypochondriasis.

This Swedish register-based matched cohort study included 4,129 individuals diagnosed with hypochondriasis in specialist services between 1997 and 2020 and 41,290 demographically matched unexposed individuals. Stratified Cox proportional hazards models were fitted to estimate hazard ratios (HRs) for the association between hypochondriasis and substance-related problems – defined as alcohol and drug use disorders, dispensed medications for alcohol dependence and opioid use disorders, and alcohol- and drug-related accidental poisonings, deaths, and suspected criminal offenses. Models were adjusted for sociodemographic variables, parental substance-related problems, and personal psychiatric history.

Substance-related problems were identified in 504 (12.2%) individuals with hypochondriasis and 1,924 (4.7%) matched unexposed individuals. After adjustment for sociodemographic and parental covariates, hypochondriasis was significantly associated with an increased risk of substance-related problems (HR, 2.55; 95% confidence interval [CI], 2.30–2.84). Similar results were observed in individuals without preexisting substance-related problems (HR, 2.85; 95% CI, 2.48–3.27). Further adjustment for psychiatric comorbidity, particularly anxiety and depression, reduced the risk estimates, but the associations remained statistically significant. In an additional analysis including primary care diagnoses of hypochondriasis (presumably reflecting less complex cases), the risk of substance-related problems remained elevated (HR, 1.61; 95% CI, 1.39–1.86).

Improved recognition of, and clinical awareness of substance misuse may help reduce long-term adverse outcomes in individuals with hypochondriasis.

Depressive symptoms are closely associated with cognitive decline and risk of incident dementia, and plasma biomarkers may play a significant role in this relationship. We aimed to investigate the influence of plasma biomarkers and explore the underlying mechanisms.

This study included 1,658 dementia-free community residents recruited in 2009–2011 from the Shanghai Aging Study. At baseline, we assayed plasma phosphorylated tau 217 (p-tau217) and neurofilament light chain (NfL), and assessed depressive symptoms using the Center for Epidemiologic Studies Depression scale. Cox regression models were performed to estimate the risks of incident dementia and Alzheimer’s disease (AD) during the 5-year follow-up. Parallel and serial mediation models were applied to investigate whether plasma p-tau217 and NfL mediated the relationship between depressive symptoms and cognitive decline.

Older adults with depressive symptoms had higher risks of dementia and AD, especially among those with higher concentrations of baseline plasma p-tau217/NfL. Sex-specific analysis revealed that depressive symptoms combined with high plasma NfL increased AD risk in men (hazard ratio, HR [95% confidence interval, CI] = 5.89 [2.01, 17.27], p = 0.001), whereas women with depressive symptoms and high plasma p-tau217 showed higher AD risk (HR [95%CI] = 6.07 [2.82, 13.09], p < 0.001). Parallel mediation analysis revealed that plasma p-tau217/NfL mediated the relationship between depressive symptoms and cognitive decline, respectively. Additionally, serial mediation analysis found p-tau217 precedes NfL within the mediating pathway (β = 0.403, bootstrap 95% CI: 0.347, 0.452).

Plasma p-tau217 and NfL could individually or jointly mediate the relationship between depressive symptoms and cognitive decline.

Menopause is a natural physiological process, but its effects on the brain remain poorly understood. In England, approximately 15% of women use hormone-replacement therapy (HRT) to manage menopausal symptoms. However, the psychological benefits of HRT are not well established. This study aims to investigate the impact of menopause and HRT on mental health, cognitive function, and brain structure.

We analyzed data from nearly 125,000 participants in the UK Biobank to assess associations between menopause, HRT use, and outcomes related to mental health, cognition, and brain morphology. Specifically, we focused on gray matter volumes in the medial temporal lobe (MTL) and anterior cingulate cortex (ACC).

Menopause was associated with increased levels of anxiety, depression, and sleep difficulties. Women using HRT reported greater mental health challenges than post-menopausal women not using HRT. Post-hoc analyses revealed that women prescribed HRT had higher levels of pre-existing mental health symptoms. In terms of brain structure, MTL and ACC volumes were smaller in post-menopausal women compared to pre-menopausal women, with the lowest volumes observed in the HRT group.

Our findings suggest that menopause is linked to adverse mental health outcomes and reductions in gray matter volume in key brain regions. The use of HRT does not appear to mitigate these effects and may be associated with more pronounced mental health challenges, potentially due to underlying baseline differences. These results have important implications for understanding the neurobiological effects of HRT and highlighting the unmet need for addressing mental health problems during menopause.

Parental prenatal mood and anxiety disorders (PMADs) are linked to child neurodevelopmental disorders (NDDs), but evaluations of the magnitude and mechanisms of this association are limited. This study estimates the strength of the association and whether it is impacted by genetic and environmental factors.

A systematic search of PubMed, CENTRAL, PsycINFO, OVID, and Google Scholar was performed for articles published from January 1988 to September 2025. Of 2,420 articles screened, 74 met the inclusion criteria. Meta-analyses were conducted on 21 studies, and 53 were included in the narrative synthesis. We conducted random-effects meta-analyses, along with tests for heterogeneity (I2) and publication bias (Egger’s test). The review followed PRISMA and MOOSE guidelines.

Maternal PMADs were associated with a significantly increased risk of attention-deficit/hyperactivity disorder (ADHD; odds ratio [OR] 1.91, 95% confidence interval [CI] 1.45–2.52) and autism spectrum disorder (ASD; OR 1.75, 95% CI 1.43–2.14) in children. Paternal PMADs were also associated with the risk of NDDs, with combined odds for ASD and ADHD (OR = 1.23, 95% CI 1.14–1.33). Several studies suggested that the link between parental PMADs and offspring NDDs might be impacted by both genetic and environmental factors, including the impact of ongoing parental depression on child behavior.

Parental PMADs are associated with increased risk of NDDs in children. These findings likely reflect a combination of inherited liability and environmental processes; clarifying mechanisms will require genetically informed designs. Regardless of mechanism, offering optional, family-centered developmental support may help promote child well-being in families where a parent is experiencing PMADs.

Depression is often comorbid with alcohol use problems, and sex differences may further complicate this interplay.

We conducted a longitudinal study using a large European adolescent cohort assessed at ages 14 (baseline, BL), 16 (follow-up 1, FU1), 19 (follow-up 2, FU2), and 23 (follow-up 3, FU3). Depression and alcohol use were measured using standardized behavioral scales. Cross-lagged analysis, improved Mendelian randomization (MR) analysis, and mediation analysis were conducted to infer the causal interplay.

2110 adolescents were included at baseline (49% male). Depression and alcohol consumption demonstrated a significant positive correlation (rBL = 0.094, pBL = 1.58E-05, 95% CI = [0.052, 0.137]), which gradually diminished over time and eventually became significantly negative. Depression and alcohol use problems remained strongly correlated across three timepoints (r > 0.074, p < 6.76E-03). Cross-lagged analysis suggested that depression predicted future alcohol use problems: βBL-FU1 = 0.058, p = 0.021, 95% CI = [0.009, 0.108]; βFU2-FU3 = 0.142, p = 8.34E-07, 95% CI = [0.113, 0.263]. MR analyses confirmed this causal interplay (rmean = 0.043, longitudinal ppermuation < 0.001). Interestingly, MR analyses also indicated that alcohol consumption might alleviate depression (rmean = −0.022, longitudinal ppermutation = 0.043), particularly in females at FU3, of which the anxiety status and the personality trait neuroticism largely mediated the effect. These findings were validated in an independent matched sample (N = 562) from Human Connectome Project.

Depression may predict future alcohol use problems, whereas moderate alcohol consumption might alleviate depressive symptoms, especially in females.

Depression in adolescents involves complex interactions among depression, anxiety, sleep disturbances, and suicidal symptoms. Network theory offers insights into dynamic symptom relationships during recovery.

Of 797 adolescents initially enrolled, 649 with complete baseline data were included in the network analyses; 458 and 277 participants were retained at the 1-month and 3-month follow-ups, respectively. Cross-sectional Gaussian Graphical Models and Cross-Lagged Panel Network (CLPN) analyses examined relationships among nine symptom domains: depression, somatic/subjective anxiety, sleep quantity/quality, daytime insomnia, passive/active sleepiness, and suicidal ideation/tendency. Network centrality and bootstrap validation assessed parameter stability.

Cross-sectional networks showed structural invariance across timepoints (p>0.05). Subjective anxiety demonstrated highest centrality at T0-T1, while somatic symptoms dominated at T2. Depression maintained high closeness centrality throughout. Although betweenness centrality also suggested a central role for depression, its lower stability (CS < 0.5) necessitates a more cautious interpretation of this specific metric. CLPN revealed more predictive relationships during T0→T1 (76.5% significant edges) than T1→T2 (24.7%). Active sleepiness strongly predicted subsequent somatic anxiety (B=0.683) and depression (B=0.647). Suicide ideation-tendency showed stable bidirectional connections. Network stability was excellent (CS>0.5) except betweenness centrality.

Central symptoms evolved during recovery, with subjective anxiety initially dominant but somatic symptoms becoming central over time. The early post-treatment period showed heightened symptom network activity, with sleep disturbances identified as robust predictors of subsequent affective deterioration. Findings support dynamic, network-informed interventions targeting evolving symptom centrality and predictive pathways, particularly addressing sleep-related symptoms and suicide risk during critical recovery phases.

Large biobanks offer unprecedented data for psychiatric genomic research, but concerns exist about representativeness and generalizability. This study examined depression prevalence and polygenic risk score (PRS) associations in the All of Us data to assess potential impacts of nonrepresentative sampling.

Depression prevalence and correlates were analyzed in two subsamples: those with self-reported personal medical history (PMH) data (N = 185,232 overall; N = 114,739 with genetic data) and those with electronic health record (EHR) data (N = 287,015 overall; N = 206,175 with genetic data). PRS weights were estimated across ancestry groups. Associations of PRS with depression were examined by state and ancestry.

Depression prevalence varied across states in both PMH (16.7–35.9%) and EHR (0.2–45.8%) data. Concordance between PMH and EHR diagnoses was low (kappa: 0.29, 95% CI: 0.30–0.30). Overall, one standard deviation increase in depression PRS was associated with lifetime depression based on PMH (odds ratio [OR] = 1.05, 95% confidence interval [CI]: 1.04–1.07) and EHR (OR = 1.05, 95% CI: 1.04–1.07). Results were generally consistent by ancestry, with the strongest signal for European ancestry (PMH: OR = 1.10, 95% CI: 1.08–1.12; EHR: OR = 1.07, 95% CI: 1.05–1.10). Associations between PRS and lifetime depression were largely consistent and significant associations varied minimally (ORs = 1.06–1.45) by state of residence in both subsamples.

Recorded depression prevalence by state in All of Us demonstrates a wide range, likely reflecting recruitment differences, EHR data completeness, and true geographic variation; yet PRS associations remained relatively stable. As studies like All of Us expand, accounting for sample composition and measurement approaches will be crucial for generating actionable findings.

Neurodevelopmental disorders have been associated with hearing problems (HP) later in life, but there is limited information regarding their shared biology.

We leveraged large-scale genome-wide datasets to estimate genetic correlation (global and local), polygenic overlap, and locus-specific pleiotropy among HP, autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Tourette syndrome (TS). Then, we investigated shared molecular functions, biological processes, and cellular components, and performed a drug-repurposing analysis to identify compounds that may target the pathogenic processes linking neurodevelopmental disorders to HP.

We observed high genetic correlation of HP with ASD (rg = 0.22) and TS (rg = 0.22). With respect to HP-ADHD polygenic overlap, 34% of the causal variants were shared between these conditions, with only 74% of them showing concordant effect directions. We also identified nine chromosomal regions with evidence of ADHD-HP local genetic correlations with pleiotropic effects on other outcomes, such as smoking initiation, brain-imaging phenotypes, and bilirubin levels. With respect to HP-ASD, we observed an inverse local genetic correlation within CD33 chromosomal region. Pleiotropy among HP, ASD, and ADHD was also identified in two variants (rs325485 and rs2207286) included within 95% credible sets related to neuropsychiatric conditions, altered hearing function, and other traits such as risk taking and insomnia. Drug-repurposing analyses identified anisomycin for HP-ASD shared biological mechanisms and five compounds related to HP-ADHD pleiotropy.

Our findings provide evidence that the comorbidity between neurodevelopmental disorders and HP is at least partially due to shared pathogenic processes acting through intrinsic and extrinsic factors.

Individuals with subthreshold depression (StD), a potentially preclinical stage of major depression, may habitually employ maladaptive expression suppression strategies in emotion regulation. However, the effect of emotional suppression (EES) and underlying neural mechanisms remain unclear.

Data came from two samples (Sample 1: 55 StD, 60 healthy controls (HC); Sample 2: 23 StD, 20 HC). Both samples completed expression suppression tasks. Using drift diffusion modeling, we decomposed performance on the emotional assessment process into separate processing components, particularly the speed of information update (drift rate), to examine how depression and emotional suppression affect decision-making. To further reveal the potential mechanism, we conducted fMRI scanning in Sample 2 and characterized latent neurocircuit driving emotion suppression and drift rate using dynamic causal modeling (DCM).

The EES negatively correlated with drift rate. StD showed reduced efficacy of EES and faster drift rates of negative preference. Greater activation was observed in the dorsolateral prefrontal cortex (dlPFC) and amygdala in StD during suppression. DCM analysis revealed that inefficient EES might be explained by the stronger connection from the right dlPFC to the right amygdala, while the faster drift rate might be attributed to a stronger connection from the left amygdala to the right dlPFC.

Our study uncovered novel latent behavioral and neurocircuit mechanisms of early risk for depression. Ineffective emotional suppression in StD is associated with faster accumulation of negative evidence. The underlying neural mechanism may involve aberrant regulation between the dlPFC and amygdala in negative contexts.

Persistent affective disturbance is a core, disabling feature of major depressive disorder (MDD), thought to stem from a dysfunctional interaction between emotional bias and cognitive control. However, the underlying neural dynamics are debated, with studies reporting both hyper- and hypoactivation. This study utilized high-temporal-resolution electroencephalogram (EEG) to resolve this discrepancy by examining distinct stages of emotional information processing.

We recruited 175 medication-free patients with MDD (Hamilton Depression Rating Scale-17 ≥ 14) and 101 healthy controls (HCs) who completed an emotional Stroop task while an EEG was recorded. We analyzed event-related potentials reflecting conflict monitoring (N250), inhibition (N450), and resolution (LSP) using a 2 (group) × 2 (valence) × 2 (congruency) analysis of variance.

Results revealed a stage-specific neural cascade. Compared to HCs, the MDD group showed: (1) hypoactivation during initial conflict monitoring (attenuated N250 amplitude); (2) compensatory hyperactivation during conflict inhibition (a significant N450 interaction revealed generalized conflict activity in MDD, unlike the context-specific response in HCs); and (3) subsequent hypoactivation during conflict resolution (reduced LSP amplitude for negative stimuli). Crucially, altered N450 correlated with depression severity, and the entire neural cascade predicted behavioral performance.

The apparent contradiction in the literature reflects a multistage process. MDD is characterized by an inefficient neural cascade: an initial deficit in conflict monitoring is followed by compensatory overactivation during inhibition, which ultimately proves insufficient, leading to impaired late-stage resolution. This temporally specific model advances our understanding of the pathophysiology of depression and identifies potential stage-specific targets for intervention.

Extensive evidence links air pollution exposure to cognitive decline; however, it remains unclear whether cognitive reserve and brain reserve modify this association. We examined the moderating roles of cognitive reserve contributors and brain reserve in the association between air pollution and cognitive function in dementia-free adults.

Cross-sectional data were obtained from 650 participants who underwent 3T brain magnetic resonance imaging and completed the Montreal Cognitive Assessment (MoCA). Cognitive reserve contributors were assessed based on education, occupation, and social engagement. Brain reserve was quantified using the ventricle-to-brain ratio derived from brain scans. Five-year average concentrations of particulate matter with diameters ≤10 and ≤2.5 μm and nitrogen dioxide were estimated based on residential addresses. Partial least squares structural equation modeling was applied to construct latent variables representing the air pollution mixture and composite cognitive reserve (contributors). Analyses examined whether cognitive reserve contributors and brain reserve modified associations of air pollution with MoCA scores and suspected mild cognitive impairment.

In individuals with an average level of cognitive reserve, a 1–standard deviation increase in air pollution mixture was associated with a 0.24-point decrease in MoCA scores (95% confidence interval [CI]: −0.31 to −0.16). This association was attenuated in individuals with higher cognitive reserve (β = −0.12; 95% CI: −0.25 to 0.02) and intensified in those with lower cognitive reserve (β = −0.36; 95% CI: −0.37 to −0.35). The moderating effect of brain reserve was not significant.

Higher cognitive reserve may mitigate the effects of air pollution on cognitive function.

Educational attainment (EA), which comprises cognitive (CogEA) and noncognitive (NonCogEA) components, is positively genetically correlated with alcohol and cannabis use but negatively correlated with alcohol and cannabis use disorders (AUD and CUD). These paradoxical associations suggest that shared genetic influences with EA may differ by level of substance involvement.

To test this, we examined the shared genetic architecture of EA, CogEA, and NonCogEA with alcohol consumption (AC), AUD, lifetime cannabis use (CanUse), and CUD. We used bivariate causal mixture models, local genetic correlation analyses, and conditional/conjunctional false discovery rate analyses to identify global, regional, and variant-level overlap for EA and substance-related trait pairs.

EA shared 57.57% of causal variants with AC and 62.42% with AUD, while sharing 48.07% of causal variants with CanUse and 84.18% with CUD. Among shared variants for AC, 48.12% had concordant effects with CogEA and 52.86% with NonCogEA. For AUD, 38.40% and 41.02% of causal variants had concordant effects with CogEA and NonCogEA, respectively. CanUse had higher concordance with CogEA (71.42%) and NonCogEA (65.56%) than CUD (37.97% and 42.23%, respectively). Functional enrichment in brain tissues varied across substance use and EA pairs.

EA is associated with greater alcohol and cannabis use and lower risk for AUD and CUD, a pattern that reflects both concordant and discordant variant effects. CogEA and NonCogEA show partially distinct patterns, particularly for cannabis-related traits, highlighting the importance of disaggregating EA to clarify the genetic architecture underlying its paradoxical associations with substance-related traits.

Depression is a common comorbidity in neuropsychiatric disorders, affecting a significant proportion of patients with neurodegenerative diseases. Traditional antidepressants show limited efficacy, particularly in cases involving comorbid depressive symptoms, highlighting the need for alternative treatments.

Here we provide the first data on possible benefits of add-on therapy with transcranial pulse stimulation (TPS). Based on the largest patient sample in the emerging field of focused ultrasound (FUS) neuromodulation to date, a retrospective analysis was conducted on 88 patients with various neuropsychiatric diagnoses to evaluate the impact of TPS on depressive symptoms, measured by the Beck Depression Inventory (BDI-II).

The study revealed significant improvements in BDI-II scores posttreatment (N = 88), with the most substantial effects observed in more severely impacted patients: individuals with minimal to severe depression (BDI-II ≥9; N = 32) experienced an average reduction of 5.22 points (29.46%), while those with mild to severe depression (BDI-II ≥14; N = 15) showed an even greater mean improvement of 10.40 points (40.51%). These results surpassed established thresholds for clinical relevance and substantially exceeded placebo effect sizes observed in relevant brain stimulation studies. Moreover, depression score improvement was independent of diagnostic group (dementia, movement disorders, or other), improvement of the primary diagnosis, antidepressant medication, and baseline cognitive status, highlighting the potential of TPS as an effective therapeutic add-on intervention for patients receiving state-of-the-art treatments.

The study’s findings indicate that TPS enhances depression outcomes in neuropsychiatric patients, particularly in those with more severe depressive symptoms.

Neurodevelopmental models regard impulsivity as a central risk factor for adolescent substance use. However, the practical utility of impulsivity in predicting substance use is complicated by variability among measures that encompass multiple methods and theoretical domains. Prior research has been constrained by cross-sectional designs, small sample sizes, and/or the use of a narrow subset of impulsivity measures.

Leveraging the ABCD dataset (n = 11,868), we identified and replicated correlations among impulsivity measures and assessed their prospective longitudinal and concurrent predictive utility regarding adolescent substance use outcomes before 15 years old. We then used simulation to inform how associations between impulsivity and substance use vary across sampling strategies (population vs. high-risk cohorts) and sample sizes.

Correlations between questionnaire and behavioral measures of impulsivity were small, and questionnaires significantly outperformed behavioral measures in predicting substance use initiation, largely due to the contribution of the CBCL externalizing scale. Predictions of substance use based on impulsivity were statistically detectable but small according to clinical standards (AUCs 0.6–0.76), exhibiting sensitivity to sample size and base rate of substance use, and thus, poor absolute predictive performance. Large samples (n > 1,000) were needed to achieve adequate power for impulsivity measures to predict substance use initiation.

These results support a significant but small contribution of impulsivity in predicting the onset of early adolescent substance use, indicating that these factors alone are insufficient for clinically deployable prediction. In community samples, large sample sizes are needed for reproducible impulsivity prediction of adolescent substance use.