Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Gene age gap estimate (GAGE) for major depressive disorder: A penalized biological age model using gene expression

Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Residuals or "gaps" between the predicted biological age and chronological age have been used for statistical inference, such as testing whether an increased age gap is associated with a given disease state. Recently, a gene expression-based model of biological age showed a higher age gap for individuals with MDD compared to healthy controls (HC). In the current study, we propose an approach that simplifies gene selection using a least absolute shrinkage and selection operator (LASSO) penalty to construct an expression-based Gene Age Gap Estimate (GAGE) model. We train a LASSO gene age model on an RNA-Seq study of 78 unmedicated individuals with MDD and 79 HC, resulting in a model with 21 genes. The L-GAGE shows higher biological aging in MDD participants than HC, but the elevation is not statistically significant. However, when we dichotomize chronological age, the interaction between MDD status and age has a significant association with L-GAGE. This effect remains statistically significant even after adjusting for chronological age and sex. Using the 21 age genes, we find a statistically significant elevated biological age in MDD in an independent microarray gene expression dataset. We find functional enrichment of infectious disease and SARS-COV pathways using a broader feature selection of age related genes.

Does restrictive anorexia nervosa impact brain aging? A machine learning approach to estimate age based on brain structure

Anorexia nervosa (AN), a severe eating disorder marked by extreme weight loss and malnutrition, leads to significant alterations in brain structure. This study used machine learning (ML) to estimate brain age from structural MRI scans and investigated brain-predicted age difference (brain-PAD) as a potential biomarker in AN. Structural MRI scans were collected from female participants aged 10-40 years across two institutions (Boston, USA, and Jena, Germany), including acute AN (acAN; n=113), weight-restored AN (wrAN; n=35), and age-matched healthy controls (HC; n=90). The ML model was trained on 3487 healthy female participants (ages 5-45 years) from ten datasets, using 377 neuroanatomical features extracted from T1-weighted MRI scans. The model achieved strong performance with a mean absolute error (MAE) of 1.93 years and a correlation of r = 0.88 in HCs. In acAN patients, brain age was overestimated by an average of +2.25 years, suggesting advanced brain aging. In contrast, wrAN participants showed significantly lower brain-PAD than acAN (+0.26 years, p=0.0026) and did not differ from HC (p=0.98), suggesting normalization of brain age estimates following weight restoration. A significant group-by-age interaction effect on predicted brain age (p<0.001) indicated that brain age deviations were most pronounced in younger acAN participants. Brain-PAD in acAN was significantly negatively associated with BMI (r = -0.291, pfdr = 0.005), but not in wrAN or HC groups. Importantly, no significant associations were found between brain-PAD and clinical symptom severity. These findings suggest that acute AN is linked to advanced brain aging during the acute stage, and that may partially normalize following weight recovery.

Brain Age Gap Associations with Body Composition and Metabolic Indices in an Asian Cohort: An MRI-Based Study

BACKGROUND

Global aging raises concerns about cognitive health, metabolic disorders, and sarcopenia. Prevention of reversible decline and diseases in middle-aged individuals is essential for promoting healthy aging. We hypothesize that changes in body composition, specifically muscle mass and visceral fat, and metabolic indices are associated with accelerated brain aging. To explore these relationships, we employed a brain age model to investigate the links between the brain age gap (BAG), body composition, and metabolic markers.

METHODS

Using T1-weighted anatomical brain MRIs, we developed a machine learning model to predict brain age from gray matter features, trained on 2,675 healthy individuals aged 18-92 years. This model was then applied to a separate cohort of 458 Taiwanese adults (57.8 years ± 11.6; 280 men) to assess associations between BAG, body composition quantified by MRI, and metabolic markers.

RESULTS

Our model demonstrated reliable generalizability for predicting individual age in the clinical dataset (MAE = 6.11 years, r = 0.900). Key findings included significant correlations between larger BAG and reduced total abdominal muscle area (r = -0.146, p = 0.018), lower BMI-adjusted skeletal muscle indices, (r = -0.134, p = 0.030), increased systemic inflammation, as indicated by high-sensitivity C-reactive protein levels (r = 0.121, p = 0.048), and elevated fasting glucose levels (r = 0.149, p = 0.020).

CONCLUSIONS

Our findings confirm that muscle mass and metabolic health decline are associated with accelerated brain aging. Interventions to improve muscle health and metabolic control may mitigate adverse effects of brain aging, supporting healthier aging trajectories.

Sex-Differential Markers of Psychiatric Risk and Treatment Response Based on Premature Aging of Functional Brain Network Dynamics and Peripheral Physiology

BACKGROUND

Aging is a multilevel process of gradual decline that predicts morbidity and mortality. Independent investigations have implicated senescence of brain and peripheral physiology in psychiatric risk, but it is unclear whether these effects stem from unique or shared mechanisms.

METHODS

To address this question, we analyzed clinical, blood chemistry, and resting-state functional neuroimaging data in a healthy aging cohort (n = 427; ages 36-100 years) and 2 disorder-specific samples including patients with early psychosis (100 patients, 16-35 years) and major depressive disorder (MDD) (104 patients, 20-76 years).

RESULTS

We identified sex-dependent coupling between blood chemistry markers of metabolic senescence (i.e., homeostatic dysregulation), functional brain network aging, and psychiatric risk. In females, premature aging of frontoparietal and somatomotor networks was linked to greater homeostatic dysregulation. It also predicted the severity and treatment resistance of mood symptoms (depression/anxiety [all 3 samples], anhedonia [MDD]) and social withdrawal/behavioral inhibition (avoidant personality disorder [healthy aging], negative symptoms [early psychosis]). In males, premature aging of the default mode, cingulo-opercular, and visual networks was linked to reduced homeostatic dysregulation and predicted the severity and treatment resistance of symptoms relevant to hostility/aggression (antisocial personality disorder [healthy aging], mania/positive symptoms [early psychosis]), impaired thought processes (early psychosis, MDD), and somatic problems (healthy aging, MDD).

CONCLUSIONS

Our findings identify sexually dimorphic relationships between brain dynamics, peripheral physiology, and risk for psychiatric illness, suggesting that the specificity of putative risk biomarkers and precision therapeutics may be improved by considering sex and other relevant personal characteristics.

Longitudinal brain age in first-episode mania youth treated with lithium or quetiapine

It is unclear if lithium and quetiapine have neuroprotective effects, especially in early stages of bipolar and schizoaffective disorders. Here, an age-related multivariate brain structural measure (i.e., brain-PAD) at baseline and changes in response to treatment after a first-episode mania (FEM) were examined. FEM participants were randomized to lithium (n=21) or quetiapine (n=18) monotherapy. T1-weighted scans were acquired at baseline, 3-months (FEM participants only) and 12-months. Brain age predictions for healthy controls (n=29) and young people with bipolar or schizoaffective disorder (15-25 years) were derived using a deep learning model trained on one of the largest datasets (N=53,542) to date. Notably, a higher brain-PAD value (predicted brain age - age) signifies an older-appearing brain. Baseline brain-PAD was higher in young people with FEM compared to controls (+1.70 year, p=0.04; Cohen's d=0.53 [SE=0.25], CI 95% [0.04 to 1.01]). However, no significant effects of time or treatment group, nor an interaction between the two, were observed throughout the course of the study. Baseline brain-PAD did not predict any change in symptomatic, quality of life or functional outcome scores over 12 months. In young individuals with FEM, baseline findings show their brains appeared older than controls. However, brain-PAD remained stable over time across treatment groups and neither baseline values nor treatment predicted 12-month outcomes. A longer follow-up with a larger sample is warranted to determine if treatment effects emerge later in bipolar and schizoaffective disorders. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry - ACTRN12607000639426.

Brain Age Disparities in Psychosis Across DSM Diagnoses and B-SNIP Biotypes

BACKGROUND AND HYPOTHESIS

The brain age gap (BAG) quantifies the difference between predicted brain age and chronological age. Prior research implicates higher BAG in psychotic disorders, suggesting accelerated brain aging. We hypothesized distinct brain aging profiles among biological subtypes of psychosis and intermediate BAG in their relatives.

STUDY DESIGN

Brain age gap values were quantified in 348 healthy controls (HCs), 950 psychosis probands classified by both DSM diagnoses of psychotic bipolar disorder, type I (BP, n = 247), schizoaffective disorder (SAD, n = 313), and schizophrenia (SZ, n = 390), and Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) Biotypes (301 Biotype 1, 304 Biotype 2, and 345 Biotype 3), and 491 of their non-psychotic first-degree relatives. We calculated brain age values from structural T1-weighted images using the pre-trained, open-source brain age package, brainageR. In probands, we assessed associations between BAG and clinical characteristics, comorbid disorders, medications, and polygenic risk scores for SZ (PRS-SZ).

STUDY RESULTS

All DSM diagnosis and Biotype groups had higher BAG than HC. While no significant differences were observed between BP, SAD, or SZ, Biotypes 1 and 2 had significantly higher BAG compared to Biotype 3. Relatives exhibited intermediate BAG values between HC and probands, with the highest BAG in relatives of those with SAD. Brain age gap was not linked to comorbid disorders or PRS-SZ, but was associated with symptom severity, cognition, functioning, and psychotropic medication use.

CONCLUSIONS

Bipolar-Schizophrenia Network for Intermediate Phenotypes Biotypes better captured age-related brain structural differences in psychosis than DSM diagnoses. Associations between BAG and medication underscore the potential influence of pharmacotherapy on brain aging in psychosis.

Investigating the link between genetic predictive factors of brain functional networks and two specific sleep disorders: Sleep apnoea and snoring

BACKGROUND

Sleep disorders are a widespread public health issue globally. Investigating the causal relationship between resting-state brain functional abnormalities and sleep disorders can provide scientific evidence for precision medicine interventions.

METHODS

We screened single nucleotide polymorphisms (SNPs) associated with rs-fMRI phenotype as instrumental variables Using bidirectional two-sample Mendelian randomization (MR), mediation MR, and multivariate MR based on Bayesian methods, the study tested the causal relationship between genetically predicted rs-fMRI and nine common sleep disorders.

RESULTS

The main inverse variance weighted (IVW) analysis identified four resting state functional magnetic resonance imaging (rs-fMRI) phenotypes that are causally associated with the risk of sleep disorders. For example, increased amplitude in nodes of the parietal, precuneus, occipital, temporal, and cerebellum regions, as well as the default mode network (DMN), central executive network (CEN) and attention network (AN) was associated with an increased risk of sleep apnoea. Enhanced neural activity in the calcarine or lingual and cerebellum regions and increased functional connectivity with the visual and subcortical-cerebellum networks was associated with a reduced risk of snoring. The mediation MR analysis shows that, body mass index (BMI) plays a significant mediating role in the risk of sleep apnoea by modulating the amplitude of nodes in the parietal, temporal, and cerebellum regions, as well as the connectivity changes in the DMN, CEN, and AN.

CONCLUSIONS

This study identified three rs-fMRI phenotypes linked to increased sleep apnoea risk and one associated with decreased snoring risk, providing an important target for the treatment of sleep disorders at the level of brain functional networks.

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation

BACKGROUND

Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders.

HYPOTHESIS

Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD).

STUDY DESIGN

From a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored.

STUDY RESULTS

The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01).

CONCLUSIONS

ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.

Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age

Background: A neuroimaging-derived biomarker termed the brain age is considered to capture the degree and diversity in the aging process of the brain, serving as a robust indicator of overall brain health. The impact of different levels of physical activity (PA) intensities on brain age is still not fully understood. This study aimed to investigate the associations between accelerometer-measured PA and brain age. Methods: A total of 16,972 eligible participants with both valid T 1-weighted neuroimaging and accelerometer data from the UK Biobank was included. Brain age was estimated using an ensemble learning approach called Light Gradient-Boosting Machine (LightGBM). Over 1,400 image-derived phenotypes (IDPs) were initially chosen to undergo data-driven feature selection for brain age prediction. A measure of accelerated brain aging, the brain age gap (BAG) can be derived by subtracting the chronological age from the estimated brain age. A positive BAG indicates accelerated brain aging. PA was measured over a 7-day period using wrist-worn accelerometers, and time spent on light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), and moderate- to vigorous-intensity PA (MVPA) was extracted. The generalized additive model was applied to examine the nonlinear association between PA and BAG after adjusting for potential confounders. Results: The brain age estimated by LightGBM achieved an appreciable performance (r = 0.81, mean absolute error [MAE] = 3.65), which was further improved by age bias correction (r = 0.90, MAE = 3.03). We found that LPA (F = 2.47, P = 0.04), MPA (F = 6.49, P < 1 × 10-300), VPA (F = 4.92, P = 2.58 × 10-5), and MVPA (F = 6.45, P < 1 × 10-300) exhibited an approximate U-shaped relationship with BAG, demonstrating that both insufficient and excessive PA levels adversely impact brain aging. Furthermore, mediation analysis suggested that BAG partially mediated the associations between PA and cognitive functions as well as brain-related disorders. Conclusions: Our study revealed a U-shaped association between accelerometer-measured PA and BAG, highlighting that advanced brain health may be attainable through engaging in moderate amounts of objectively measured PA irrespectively of intensities.

Mechanism of N6-Methyladenosine Modification in the Pathogenesis of Depression

N6-methyladenosine (m6A) is one of the most common post-transcriptional RNA modifications, which plays a critical role in various bioprocesses such as immunological processes, stress response, cell self-renewal, and proliferation. The abnormal expression of m6A-related proteins may occur in the central nervous system, affecting neurogenesis, synapse formation, brain development, learning and memory, etc. Accumulating evidence is emerging that dysregulation of m6A contributes to the initiation and progression of psychiatric disorders including depression. Until now, the specific pathogenesis of depression has not been comprehensively clarified, and further investigations are warranted. Stress, inflammation, neurogenesis, and synaptic plasticity have been implicated as possible pathophysiological mechanisms underlying depression, in which m6A is extensively involved. Considering the extensive connections between depression and neurofunction and the critical role of m6A in regulating neurological function, it has been increasingly proposed that m6A may have an important role in the pathogenesis of depression; however, the results and the specific molecular mechanisms of how m6A methylation is involved in major depressive disorder (MDD) were varied and not fully understood. In this review, we describe the underlying molecular mechanisms between m6A and depression from several aspects including inflammation, stress, neuroplasticity including neurogenesis, and brain structure, which contain the interactions of m6A with cytokines, the HPA axis, BDNF, and other biological molecules or mechanisms in detail. Finally, we summarized the perspectives for the improved understanding of the pathogenesis of depression and the development of more effective treatment approaches for this disorder.

Multimodal Brain Age Indicators of Internalizing Problems in Early Adolescence: A Longitudinal Investigation

BACKGROUND

Adolescence is a time of increased risk for the onset of internalizing problems, particularly in females. However, how individual differences in brain maturation are related to the increased vulnerability for internalizing problems in adolescence remains poorly understood due to a scarcity of longitudinal studies.

METHODS

Using ABCD (Adolescent Brain Cognitive Development) Study data, we examined longitudinal associations between multimodal brain age and youth internalizing problems. Brain age models were trained, validated, and tested independently on T1-weighted imaging (n = 9523), diffusion tensor imaging (n = 8834), and resting-state functional magnetic resonance imaging (n = 8233) data at baseline (meanage = 9.9 years) and 2-year follow-up (meanage = 11.9 years). Self-reported internalizing problems were measured at 3-year follow-up (meanage = 12.9 years) using the Brief Problem Monitor.

RESULTS

Latent change score models demonstrated that although brain age gap (BAG) at baseline was not related to later internalizing problems, an increase in BAG between time points was positively associated with internalizing problems at 3-year follow-up in females but not males. This association between an increasing BAG and higher internalizing problems was observed in the T1-weighted imaging (β = 0.067, SE = 0.050, false discovery rate [FDR]-corrected p = .020) and resting-state functional magnetic resonance imaging (β = 0.090, SE = 0.025, pFDR = .007) models but not diffusion tensor imaging (β = -0.002, SE = 0.053, pFDR = .932) and remained significant when accounting for earlier internalizing problems.

CONCLUSIONS

A greater increase in BAG in early adolescence may reflect the heightened vulnerability shown by female youth to internalizing problems. Longitudinal research is necessary to understand whether this increasing BAG signifies accelerated brain development and its relationship to the trajectory of internalizing problems throughout adolescence.

Brain predicted age in chronic pelvic pain: a study by the Multidisciplinary Approach to the Study of Chronic Pelvic Pain Research Network

ABSTRACT

The effect of chronic pain on brain-predicted age is unclear. We performed secondary analyses of a large cross-sectional and 3-year longitudinal data set from the Multidisciplinary Approach to the Study of Chronic Pelvic Pain Research Network to test the hypothesis that chronic pelvic pain accelerates brain aging and brain aging rate. Brain-predicted ages of 492 chronic pelvic pain patients and 72 controls were determined from T1-weighted MRI scans and used to calculate the brain-predicted age gap estimation (brainAGE; brain-predicted - chronological age). Separate regression models determined whether the presence of chronic pelvic pain could explain brainAGE and brain aging rate when accounting for covariates. We performed secondary analyses to understand whether brainAGE was associated with factors that subtype chronic pelvic pain patients (inflammation, widespread pain, and psychological comorbidities). We found a significant association between chronic pelvic pain and brainAGE that differed by sex. Women with chronic pelvic pain had higher brainAGE than female controls, whereas men with chronic pelvic pain exhibited lower brainAGE than male controls on average-however, the effect was not statistically significant in men or women when considered independently. Secondary analyses demonstrated preliminary evidence of an association between inflammatory load and brainAGE. Further studies of brainAGE and inflammatory load are warranted.

Functional vs Structural Cortical Deficit Pattern Biomarkers for Major Depressive Disorder

Importance

Major depressive disorder (MDD) is a severe mental illness characterized more by functional rather than structural brain abnormalities. The pattern of regional homogeneity (ReHo) deficits in MDD may relate to underlying regional hypoperfusion. Capturing this functional deficit pattern provides a brain pattern-based biomarker for MDD that is linked to the underlying pathophysiology.

Objective

To examine whether cortical ReHo patterns provide a replicable biomarker for MDD that is more sensitive than reduced cortical thickness and evaluate whether the ReHo MDD deficit pattern reflects regional cerebral blood flow (RCBF) deficit patterns in MDD and whether a regional vulnerability index (RVI) thus constructed may provide a concise brain pattern-based biomarker for MDD.

Design, Settings, and Participants

The UK Biobank (UKBB) participants had ReHo and structural measurements. Participants from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) Consortium were included for measuring the MDD structural cortical deficit pattern. The UKBB ReHo and ENIGMA cortical thickness effect sizes for MDD were used to test the deficit patterns in the Amish Connectome Project (ACP) with ReHo, structural, and RCBF data. Finally, the Ament Clinic Inc (ACI) sample had RCBF data measured using single-photon emission computed tomography. Data were analyzed from August 2021 to September 2024.

Exposures

ReHo and structural measurements.

Results

Included in this analysis were 4 datasets: (1) UKBB (N = 4810 participants; 2220 with recurrent MDD and 2590 controls; mean [SD] age, 63.0 [7.5] years; 1121 female [50%]), (2) ENIGMA (N = 10 115 participants; 2148 with MDD and 7957 healthy controls; mean [SD] age, 39.9 [10.0] years; 5927 female [59%]), (3) ACP (N = 204 participants; 68 with a lifetime diagnosis of MDD and 136 controls; mean [SD] age, 41.0 [14.5] years; 104 female [51%]), and (4) ACI (N = 372 participants; 296 with recurrent MDD and 76 controls; mean [SD] age, 45.3 [17.2] years; 189 female [51%]). MDD participants had lower cortical ReHo in the cingulum, superior temporal lobe, frontal lobe, and several other areas, with no significant differences in cortical thickness. The regional pattern of ReHo MDD effect sizes was significantly correlated with that of RCBF obtained from 2 independent datasets (Pearson r = 0.52 and Pearson r = 0.46; P < 10-4). ReHo and RCBF functional RVIs showed numerically stronger effect sizes (Cohen d = 0.33-0.90) compared with structural RVIs (Cohen d = 0.09-0.20). Elevated ReHo-based RVI-MDD values in individuals with MDD were associated with higher depression symptom severity across cohorts.

Conclusions and Relevance

Results of this case-control study suggest that the ReHo MDD deficit pattern reflected cortical hypoperfusion and was regionally specific in MDD. ReHo-based RVI may serve as a sensitive functional biomarker for MDD.

Sociodemographic Factors, Health-Risk Behaviors, and Chronic Conditions Are Associated with a High Prevalence of Depressive Symptoms: Findings from the Indonesian Family Life Survey-5

Depression is a significant public health challenge. However, limited research exists regarding the risk of sociodemographic factors, health-risk behavior, and chronic conditions in relation to the development of depression in Indonesia. This study assesses the prevalence of depressive symptoms in adolescents and adults, and identifies its potential associations with sociodemographic factors, health-risk behaviors, and chronic conditions. A national cross-sectional population-based survey was performed, using the Indonesian Family Life Survey (IFLS-5), to assess depressive symptoms in respondents aged 15 years and older. Depression was evaluated using the Center for Epidemiologic Studies-Depression (CES-D) scale, and potential associations with sociodemographic factors, health-risk behaviors, and chronic conditions were examined using logistic regression analysis. The study revealed a high prevalence of depressive symptoms, with the highest incidence observed in the age group of 25-34 years. Factors such as unmarried status, younger age, good physical activity, and having chronic conditions showed associations with depression. These findings have implications for developing public mental health strategies to reduce the prevalence of depression in Indonesia.

Resting-state functional connectivity correlates of brain structural aging in schizophrenia

A large body of research has shown that schizophrenia patients demonstrate increased brain structural aging. Although this process may be coupled with aberrant changes in intrinsic functional architecture of the brain, they remain understudied. We hypothesized that there are brain regions whose whole-brain functional connectivity at rest is differently associated with brain structural aging in schizophrenia patients compared to healthy controls. Eighty-four male schizophrenia patients and eighty-six male healthy controls underwent structural MRI and resting-state fMRI. The brain-predicted age difference (b-PAD) was a measure of brain structural aging. Resting-state fMRI was applied to obtain global correlation (GCOR) maps comprising voxelwise values of the strength and sign of functional connectivity of a given voxel with the rest of the brain. Schizophrenia patients had higher b-PAD compared to controls (mean between-group difference + 2.9 years). Greater b-PAD in schizophrenia patients, compared to controls, was associated with lower whole-brain functional connectivity of a region in frontal orbital cortex, inferior frontal gyrus, Heschl's Gyrus, plana temporale and polare, insula, and opercular cortices of the right hemisphere (rFTI). According to post hoc seed-based correlation analysis, decrease of functional connectivity with the posterior cingulate gyrus, left superior temporal cortices, as well as right angular gyrus/superior lateral occipital cortex has mainly driven the results. Lower functional connectivity of the rFTI was related to worse verbal working memory and language production. Our findings demonstrate that well-established frontotemporal functional abnormalities in schizophrenia are related to increased brain structural aging.

Lifetime depression, sleep disruption and brain structure in the UK Biobank cohort

Whether depression and poor sleep interact or have statistically independent associations with brain structure and its change over time is not known. Within a subset of UK Biobank participants with neuroimaging and subjective and/or objective sleep data (n = 28,351), we examined associations between lifetime depression and sleep disruption, and their interaction with structural neuroimaging measures, both cross-sectionally and longitudinally. Sleep variables were: self-reported insomnia and difficulty getting up; actigraphy-derived short sleep (<7 h); sustained inactivity bouts during daytime (SIBD); and sleep efficiency. Imaging measures were white matter microstructure, subcortical volumes, cortical thickness and surface area of 24 cortical regions of interest. Individuals with lifetime depression (self-reported, mental health questionnaire or health records) were contrasted with healthy controls. Interactions between depression and difficulty getting up for i) right nucleus accumbens volume and ii) mean diffusivity of forceps minor, reflected a larger negative association of poor sleep in the presence vs. absence of depression. Depression was associated with widespread reductions in white matter integrity. Depression, higher SIBD and difficulty getting up were individually associated with smaller cortical volumes and surface area, particularly in the frontal and parietal lobes. Many regions showed age-related decline, but this was not exacerbated by either depression or sleep disturbance. Overall, we identified widespread cross-sectional associations of both lifetime depression and sleep measures with brain structure. Findings were more consistent with additive rather than synergistic effects - although in some regions we observed greater magnitude of deleterious associations from poor sleep phenotypes in the presence of depression.

Increased Brain Age Among Psychiatrically Healthy Adults Exposed to Childhood Trauma

BACKGROUND

Adults with childhood trauma exposure may exhibit brain changes typically associated with aging and neurodegeneration (e.g., reduced tissue volume or integrity) to a greater degree than their unexposed counterparts, suggesting accelerated brain aging. Machine learning methods that predict a person's age based on their magnetic resonance imaging (MRI) brain scan may be useful for investigating aberrant brain aging following childhood trauma exposure. Emerging evidence indicates altered brain aging in adolescents with childhood trauma exposure; however, this association has not been examined in healthy adults.

METHODS

We investigated the associations between childhood trauma exposure, including abuse and neglect, and brain-predicted age in psychiatrically healthy adults. "Brain age" predictions were generated from T1-weighted structural MRI scans using a pre-trained machine learning pipeline, namely brainageR. The differences between brain-predicted age and chronological age were calculated and associations with childhood trauma questionnaire scores were investigated using linear regression.

RESULTS

The final sample (n = 153; mean age 46 ± 16 years, 70% female) included 69 adults with childhood trauma exposure and 84 unexposed adults. Childhood sexual abuse was associated with an average increased brain age of 3.2 years, adjusting for chronological age and age-squared, sex, and scanner site; however, this finding did not survive correction for multiple comparisons.

CONCLUSIONS

To our knowledge, this study represents the first published investigation of brain age in adults with childhood trauma using a machine-learning-based prediction model. Our findings suggest a link between childhood trauma exposure, specifically sexual abuse, and accelerated brain aging in adulthood, but this association should be replicated in future work. Accentuated brain aging in adulthood may increase the risk of age-related cognitive and neurodegenerative decline and associated disorders later in life.

ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the field of meditation and contemplative neuroscience.

Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis

Introduction

Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, brain age research in psychiatry has primarily examined mood and psychotic disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group.

Methods

3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1,134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effect models examined group differences in brain-PAD and moderation by age.

Results

No significant group difference in brain-PAD manifested (β diagnosis (SE)=0.37 years (0.43), p=0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (β diagnosis-by-age=-0.08 (0.03), pFDR=0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post-hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; β diagnosis=1.20 (0.60), p<0.05, mixed-effects d (95% confidence interval)=0.14 (0.00-0.28)), but not older participants (36-75 years; β diagnosis=0.07 (0.65), p=0.91).

Conclusions

Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post-hoc analyses showed a subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

Accelerated brain aging in patients with major depressive disorder and its neurogenetic basis: evidence from neurotransmitters and gene expression profiles

BACKGROUND

Numerous studies have explored the relationship between brain aging and major depressive disorder (MDD) and attempted to explain the phenomenon of faster brain aging in patients with MDD from multiple perspectives. However, a major challenge in this field is elucidating the ontological basis of these changes. Here, we aimed to explore the relationship between brain structural changes in MDD-related brain aging and neurotransmitter expression levels and transcriptomics.

METHODS

Imaging data from 670 Japanese participants (MDD: health controls = 233:437) and the support vector regression model were utilized to predict and compare brain age between MDD patients and healthy controls. A map of differences in cortical thickness was generated, furthermore, spatial correlation analysis with neurotransmitters and correlation analysis with gene expression were performed.

RESULTS

The degree of brain aging was found to be significantly higher in patients with MDD. Moreover, significant cortical thinning was observed in the left ventral area, and premotor eye field in patients with MDD. A significant correlation was observed between MDD-related cortical thinning and neurotransmitter receptors/transporters, including dopaminergic, serotonergic, and glutamatergic systems. Enriched Gene Ontology terms, including protein binding, plasma membrane, and protein processing, contribute to MDD-related cortical thinning.

CONCLUSIONS

The findings of this study provide further evidence that patients with MDD experience more severe brain aging, deepening our understanding of the underlying neural mechanisms and genetic basis of the brain changes involved. Additionally, these findings hold promise for the development of interventions aimed at preventing further deterioration in MDD-related brain aging, thus offering potential therapeutic avenues.

Mapping Structural Neuroimaging Trajectories in Bipolar Disorder: Neurobiological and Clinical Implications

Neuroimaging is a powerful noninvasive method for studying brain alterations in bipolar disorder (BD). To date, most neuroimaging studies of BD have included smaller cross-sectional samples reporting case versus control comparisons, revealing small to moderate effect sizes. In this narrative review, we discuss the current state of structural neuroimaging studies using magnetic resonance imaging, which inform our understanding of altered brain trajectories in BD across the lifespan. Alternative methodologies such as those that model patient deviations from age-specific norms are discussed, which may help derive new markers of BD pathophysiology. We discuss evidence from neuroimaging genetics and transcriptomics studies, which attempt to bridge the gap between macroscale brain variations and underlying microscale neurodevelopmental mechanisms. We conclude with a look toward the future and how ambitious investments in longitudinal, deeply phenotyped, population-based cohorts can improve modeling of complex clinical factors and provide more clinically actionable brain markers for BD.

Investigating dynamic brain functional redundancy as a mechanism of cognitive reserve

Introduction

Individuals with higher cognitive reserve (CR) are thought to be more resilient to the effects of age-related brain changes on cognitive performance. A potential mechanism of CR is redundancy in brain network functional connectivity (BFR), which refers to the amount of time the brain spends in a redundant state, indicating the presence of multiple independent pathways between brain regions. These can serve as back-up information processing routes, providing resiliency in the presence of stress or disease. In this study we aimed to investigate whether BFR modulates the association between age-related brain changes and cognitive performance across a broad range of cognitive domains.

Methods

An open-access neuroimaging and behavioral dataset (n = 301 healthy participants, 18-89 years) was analyzed. Cortical gray matter (GM) volume, cortical thickness and brain age, extracted from structural T1 images, served as our measures of life-course related brain changes (BC). Cognitive scores were extracted from principal component analysis performed on 13 cognitive tests across multiple cognitive domains. Multivariate linear regression tested the modulating effect of BFR on the relationship between age-related brain changes and cognitive performance.

Results

PCA revealed three cognitive test components related to episodic, semantic and executive functioning. Increased BFR predicted reduced performance in episodic functioning when considering cortical thickness and GM volume as measures of BC. BFR significantly modulated the relationship between cortical thickness and episodic functioning. We found neither a predictive nor modulating effect of BFR on semantic or executive performance, nor a significant effect when defining BC via brain age.

Discussion

Our results suggest that BFR could serve as a metric of CR when considering certain cognitive domains, specifically episodic functioning, and defined dimensions of BC. These findings potentially indicate the presence of multiple underlying mechanisms of CR.

The St. Göran Project: A Multipronged Strategy for Longitudinal Studies for Bipolar Disorders

INTRODUCTION

The St. Göran Bipolar Project (SBP) is a longitudinal outpatient study investigation aimed at identifying predictive factors associated with long-term outcomes in individuals with bipolar disorder. These outcomes include cognitive function, relapse rate, treatment responses, and functional outcomes. The study employs a multifaceted approach, integrating brain imaging, biochemical analyses of cerebrospinal fluid and blood, and genetics. This paper provides an overview of the research methods used in the SBP, along with a summary of the main findings to date.

METHODS

SBP is a collaborative effort between academia and healthcare, enrolling study participants from bipolar outpatient clinics in Stockholm (SBP-S) and Gothenburg (SBP-G), Sweden. Healthy controls were recruited through Statistics Sweden. Data and samples were collected using structured interviews, self-rated questionnaires, blood and cerebrospinal fluid samples, magnetic resonance imaging, and neuropsychological tests. Follow-up visits are conducted 7 and 14 years after baseline.

CONCLUSION

The SBP has generated numerous original findings and has contributed to advancing knowledge on cognitive function, personality, cerebrospinal and blood biomarkers, neuroimaging, and genetics. Further, as data collection nears completion, new research questions can be addressed. The study's strengths include detailed, multimodal information from each study visit and a long follow-up period. The naturalistic setting ensures that findings are relevant to real-world scenarios. However, variability in data completeness can introduce selection bias. Additionally, the control population, while randomly selected, may not be fully representative due to the voluntary nature of participation. Future projects will focus on longitudinal analyses and novel methods to exploit the study's multifaceted approach.

INTRODUCTION

The St. Göran Bipolar Project (SBP) is a longitudinal outpatient study investigation aimed at identifying predictive factors associated with long-term outcomes in individuals with bipolar disorder. These outcomes include cognitive function, relapse rate, treatment responses, and functional outcomes. The study employs a multifaceted approach, integrating brain imaging, biochemical analyses of cerebrospinal fluid and blood, and genetics. This paper provides an overview of the research methods used in the SBP, along with a summary of the main findings to date.

METHODS

SBP is a collaborative effort between academia and healthcare, enrolling study participants from bipolar outpatient clinics in Stockholm (SBP-S) and Gothenburg (SBP-G), Sweden. Healthy controls were recruited through Statistics Sweden. Data and samples were collected using structured interviews, self-rated questionnaires, blood and cerebrospinal fluid samples, magnetic resonance imaging, and neuropsychological tests. Follow-up visits are conducted 7 and 14 years after baseline.

CONCLUSION

The SBP has generated numerous original findings and has contributed to advancing knowledge on cognitive function, personality, cerebrospinal and blood biomarkers, neuroimaging, and genetics. Further, as data collection nears completion, new research questions can be addressed. The study's strengths include detailed, multimodal information from each study visit and a long follow-up period. The naturalistic setting ensures that findings are relevant to real-world scenarios. However, variability in data completeness can introduce selection bias. Additionally, the control population, while randomly selected, may not be fully representative due to the voluntary nature of participation. Future projects will focus on longitudinal analyses and novel methods to exploit the study's multifaceted approach.

Brain Age Is Not a Significant Predictor of Relapse Risk in Late-Life Depression

BACKGROUND

Late-life depression (LLD) has been associated cross-sectionally with lower brain structural volumes and accelerated brain aging compared with healthy control participants (HCs). There are few longitudinal studies on the neurobiological predictors of recurrence in LLD. We tested a machine learning brain age model and its prospective association with LLD recurrence risk.

METHODS

We recruited individuals with LLD (n = 102) and HCs (n = 43) into a multisite, 2-year longitudinal study. Individuals with LLD were enrolled within 4 months of remission. Remitted participants with LLD underwent baseline neuroimaging and longitudinal clinical follow-up. Over 2 years, 43 participants with LLD relapsed and 59 stayed in remission. We used a previously developed machine learning brain age algorithm to compute brain age at baseline, and we evaluated brain age group differences (HC vs. LLD and HC vs. remitted LLD vs. relapsed LLD). We conducted a Cox proportional hazards model to evaluate whether baseline brain age predicted time to relapse.

RESULTS

We found that brain age did not significantly differ between the HC and LLD groups or between the HC, remitted LLD, and relapsed LLD groups. Brain age did not significantly predict time to relapse.

CONCLUSIONS

In contrast to our hypothesis, we found that brain age did not differ between control participants without depression and individuals with remitted LLD, and brain age was not associated with subsequent recurrence. This is in contrast to existing literature which has identified baseline brain age differences in late life but consistent with work that has shown no differences between people who do and do not relapse on gross structural measures.

Plasma proteomics identify biomarkers and undulating changes of brain aging

Proteomics enables the characterization of brain aging biomarkers and discernment of changes during brain aging. We leveraged multimodal brain imaging data from 10,949 healthy adults to estimate brain age gap (BAG), an indicator of brain aging. Proteome-wide association analysis across 4,696 participants of 2,922 proteins identified 13 significantly associated with BAG, implicating stress, regeneration and inflammation. Brevican (BCAN) (β = -0.838, P = 2.63 × 10-10) and growth differentiation factor 15 (β = 0.825, P = 3.48 × 10-11) showed the most significant, and multiple, associations with dementia, stroke and movement functions. Dysregulation of BCAN affected multiple cortical and subcortical structures. Mendelian randomization supported the causal association between BCAN and BAG. We revealed undulating changes in the plasma proteome across brain aging, and profiled brain age-related change peaks at 57, 70 and 78 years, implicating distinct biological pathways during brain aging. Our findings revealed the plasma proteomic landscape of brain aging and pinpointed biomarkers for brain disorders.

Accelerated biological brain aging in major depressive disorder

Major depressive disorder (MDD) patients commonly encounter multiple types of functional disabilities, such as social, physical, and role functioning. MDD is related to an accreted risk of brain atrophy, aging-associated brain diseases, and mortality. Based on recently available studies, there are correlations between notable biological brain aging and MDD in adulthood. Despite several clinical and epidemiological studies that associate MDD with aging phenotypes, the underlying mechanisms in the brain remain unknown. The key areas in the study of biological brain aging in MDD are structural brain aging, impairment in functional connectivity, and the impact on cognitive function and age-related disorders. Various measurements have been used to determine the severity of brain aging, such as the brain age gap estimate (BrainAGE) or brain-predicted age difference (BrainPAD). This review summarized the current results of brain imaging data on the similarities between the manifestation of brain structural changes and the age-associated processes in MDD. This review also provided recent evidence of BrainPAD or BrainAGE scores in MDD, brain structural abnormalities, and functional connectivity, which are commonly observed between MDD and age-associated processes. It serves as a basis of current reference for future research on the potential areas of investigation for diagnostic, preventive, and potentially therapeutic purposes for brain aging in MDD.

Differing Effects of Alcohol Use on Epigenetic and Brain Age in Adult Children of Parents with Alcohol Use Disorder

BACKGROUND

It is known that being the adult child of a parent with an alcohol use disorder (ACoA) can confer a wide variety of increased health and psychological risks, including higher rates of anxiety, depression, and post-traumatic stress disorder symptoms. Additionally, ACoAs are at greater risk of developing alcohol/substance use disorders (AUDs/SUDs) than individuals from families without a history of AUDs.

METHODS

ACoA individuals with risky hazardous alcohol use (n = 14) and those not engaged in hazardous use (n = 14) were compared to a group of healthy controls. We examined structural brain differences and applied machine learning algorithms to predict biological brain and DNA methylation ages to investigate differences and determine any accelerated aging between these groups.

RESULTS

Hazardous and non-hazardous ACoA groups had lower predicted brain ages than the healthy control group (n = 100), which may result from neuro-developmental differences between ACoA groups and controls. Within specific brain regions, we observed decreased cortical volume within bilateral pars orbitalis and frontal poles, and the left middle temporal gyrus and entorhinal cortex within the hazardous alcohol ACoA group. When looking at the epigenetic aging data, the hazardous ACoA participants had increased predicted epigenetic age difference scores compared to the control group (n = 34) and the non-hazardous ACoA participant groups.

CONCLUSIONS

The results demonstrate a decreased brain age in the ACoAs compared to control, concurrent with increased epigenetic age specifically in the hazardous ACoA group, laying the foundation for future research to identify individuals with an increased susceptibility to developing hazardous alcohol use. Together, these results provide a better understanding of the associations between epigenetic factors, brain structure, and alcohol use disorders.

Evaluation of Brain Age as a Specific Marker of Brain Health

Brain age is a powerful marker of general brain health. Furthermore, brain age models are trained on large datasets, thus giving them a potential advantage in predicting specific outcomes - much like the success of finetuning large language models for specific applications. However, it is also well-accepted in machine learning that models trained to directly predict specific outcomes (i.e., direct models) often perform better than those trained on surrogate outcomes. Therefore, despite their much larger training data, it is unclear whether brain age models outperform direct models in predicting specific brain health outcomes. Here, we compare large-scale brain age models and direct models for predicting specific health outcomes in the context of Alzheimer's Disease (AD) dementia. Using anatomical T1 scans from three continents (N = 1,848), we find that direct models outperform brain age models without finetuning. Finetuned brain age models yielded similar performance as direct models, but importantly, did not outperform direct models although the brain age models were pretrained on 1000 times more data than the direct models: N = 53,542 vs N = 50. Overall, our results do not discount brain age as a useful marker of general brain health. However, in this era of large-scale brain age models, our results suggest that small-scale, targeted approaches for extracting specific brain health markers still hold significant value.

Gene Age Gap Estimate (GAGE) for major depressive disorder: a penalized biological age model using gene expression

Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Residuals or "gaps" between the predicted biological age and chronological age have been used for statistical inference, such as testing whether an increased age gap is associated with a given disease state. Recently, a gene expression-based model of biological age showed a higher age gap for individuals with MDD compared to healthy controls (HC). In the current study, we propose an approach that simplifies gene selection using a least absolute shrinkage and selection operator (LASSO) penalty to construct an expression-based Gene Age Gap Estimate (GAGE) model. We train a LASSO gene age model on an RNA-Seq study of 78 unmedicated individuals with MDD and 79 HC, resulting in a model with 21 genes. The L-GAGE shows higher biological aging in MDD participants than HC, but the elevation is not statistically significant. However, when we dichotomize chronological age, the interaction between MDD status and age has a significant association with L-GAGE. This effect remains statistically significant even after adjusting for chronological age and sex. Using the 21 age genes, we find a statistically significant elevated biological age in MDD in an independent microarray gene expression dataset. We find functional enrichment of infectious disease and SARS-COV pathways using a broader feature selection of age related genes.

Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study

BACKGROUND AND AIMS

The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls.

DESIGN

This was a cross-sectional study.

SETTING

This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites.

PARTICIPANTS

This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls.

MEASUREMENTS

This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls.

FINDINGS

Alcohol use disorder (β = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models.

CONCLUSIONS

People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.

Severe motor impairment is associated with lower contralesional brain age in chronic stroke

Background

Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain-predicted age difference (brain-PAD) has emerged as a sensitive biomarker. Our previous study showed higher global brain-PAD associated with poorer motor function post-stroke. However, the relationship between local stroke lesion load, regional brain age, and motor impairment remains unclear.

Methods

We studied 501 individuals with chronic unilateral stroke (>180 days post-stroke) from the ENIGMA Stroke Recovery Working Group dataset (34 cohorts). Structural T1-weighted MRI scans were used to estimate regional brain-PAD in 18 predefined functional subregions via a graph convolutional network algorithm. Lesion load for each region was calculated based on lesion overlap. Linear mixed-effects models assessed associations between lesion size, local lesion load, and regional brain-PAD. Machine learning classifiers predicted motor outcomes using lesion loads and regional brain-PADs. Structural equation modeling examined directional relationships among corticospinal tract lesion load (CST-LL), ipsilesional brain-PAD, motor outcomes, and contralesional brain-PAD.

Findings

Larger total lesion size was positively associated with higher ipsilesional regional brain-PADs (older brain age) across most regions (p < 0.05), and with lower contralesional brain-PAD, notably in the ventral attention-language network (p < 0.05). Higher local lesion loads showed similar patterns. Specifically, lesion load in the salience network significantly influenced regional brain-PADs across both hemispheres. Machine learning models identified CST-LL, salience network lesion load, and regional brain-PAD in the contralesional frontoparietal network as the top three predictors of motor outcomes. Structural equation modeling revealed that larger stroke damage was associated with poorer motor outcomes (β = -0.355, p < 0.001), which were further linked to younger contralesional brain age (β = 0.204, p < 0.001), suggesting that severe motor impairment is linked to compensatory decreases in contralesional brain age.

Interpretation

Our findings reveal that larger stroke lesions are associated with accelerated aging in the ipsilesional hemisphere and paradoxically decelerated brain aging in the contralesional hemisphere, suggesting compensatory neural mechanisms. Assessing regional brain age may serve as a biomarker for neuroplasticity and inform targeted interventions to enhance motor recovery after stroke.

Fundings

Micheal J Fox Foundation, National Institutes of Health, Canadian Institutes of Health Research, National Health and Medical Research Council, Australian Brain Foundation, Wicking Trust, Collie Trust, and Sidney and Fiona Myer Family Foundation, National Heart Foundation, Hospital Israelita Albert Einstein, Australian Research Council Future Fellowship, Wellcome Trust, National Institute for Health Research Imperial Biomedical Research Centre, European Research Council, Deutsche Forschungsgemeinschaft, REACT Pilot, National Resource Center, Research Council of Norway, South-Eastern Norway Regional Health Authority, Norwegian Extra Foundation for Health and Rehabilitation, Sunnaas Rehabilitation Hospital HT, University of Oslo, and VA Rehabilitation Research and Development.

Exploring associations between psychotic experiences and structural brain age: a population-based study in late adolescence

Neuroimaging studies show advanced structural "brain age" in schizophrenia and related psychotic disorders, potentially reflecting aberrant brain ageing or maturation. The extent to which altered brain age is associated with subthreshold psychotic experiences (PE) in youth remains unclear. We investigated the association between PE and brain-predicted age difference (brain-PAD) in late adolescence using a population-based sample of 117 participants with PE and 115 without PE (aged 19-21 years) from the Avon Longitudinal Study of Parents and Children. Brain-PAD was estimated using a publicly available machine learning model previously trained on a combination of region-wise T1-weighted grey-matter measures. We found little evidence for an association between PEs and brain-PAD after adjusting for age and sex (Cohen's d = -0.21 [95% CI -0.47, 0.05], p = 0.11). While there was some evidence for lower brain-PAD in those with PEs relative to those without PEs after additionally adjusting for parental social class (Cohen's d = -0.31 [95% CI -0.58, -0.03], p = 0.031) or birth weight (Cohen's d = -0.29 [95% CI -0.55, -0.03], p = 0.038), adjusting for maternal education or childhood IQ did not alter the primary results. These findings do not support the notion of advanced brain age in older adolescents with PEs. However, they weakly suggest there might be a younger-looking brain in those individuals, indicative of subtle delays in structural brain maturation. Future studies with larger samples covering a wider age range and multimodal measures could further investigate brain age as a marker of psychotic experiences in youth.

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features

To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimer's disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to (1) predict brain age using 5-HT2AR binding outcomes, (2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and (3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean = 38, std = 18) and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE = 6.63 years, std = 0.74 years) and GM volume (mean MAE = 6.95 years, std = 0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE = 5.54 years, std = 0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.

A perspective on brain-age estimation and its clinical promise

Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.

Independent and joint associations of cardiometabolic multimorbidity and depression on cognitive function: findings from multi-regional cohorts and generalisation from community to clinic

Background

Cardiometabolic multimorbidity (CMM) and depression are often co-occurring in older adults and associated with neurodegenerative outcomes. The present study aimed to estimate the independent and joint associations of CMM and depression on cognitive function in multi-regional cohorts, and to validate the generalizability of the findings in additional settings, including clinical.

Methods

Data harmonization was performed across 14 longitudinal cohort studies within the Cohort Studies of Memory in an International Consortium (COSMIC) group, spanning North America, South America, Europe, Africa, Asia, and Australia. Three external validation studies with distinct settings were employed for generalization. Participants were eligible for inclusion if they had data for CMM and were free of dementia at baseline. Baseline CMM was defined as: 1) CMM 5, ≥2 among hypertension, hyperlipidemia, diabetes, stroke, and heart disease and 2) CMM 3 (aligned with previous studies), ≥2 among diabetes, stroke, and heart disease. Baseline depression was primarily characterized by binary classification of depressive symptom measurements, employing the Geriatric Depression Scale and the Center for Epidemiological Studies-Depression scale. Global cognition was standardized as z-scores through harmonizing multiple cognitive measures. Longitudinal cognition was calculated as changes in global cognitive z-scores. A pooled individual participant data (IPD) analysis was utilized to estimate the independent and joint associations of CMM and depression on cognitive outcomes in COSMIC studies, both cross-sectionally and longitudinally. Repeated analyses were performed in three external validation studies.

Findings

Of the 32,931 older adults in the 14 COSMIC cohorts, we included 30,382 participants with complete data on baseline CMM, depression, and cognitive assessments for cross-sectional analyses. Among them, 22,599 who had at least 1 follow-up cognitive assessment were included in the longitudinal analyses. The three external studies for validation had 1964 participants from 3 multi-ethnic Asian older adult cohorts in different settings (community-based, memory clinic, and post-stroke study). In COSMIC studies, each of CMM and depression was independently associated with cross-sectional and longitudinal cognitive function, without significant interactions between them (Ps > 0.05). Participants with both CMM and depression had lower cross-sectional cognitive performance (e.g. β = -0.207, 95% CI = (-0.255, -0.159) for CMM5 (+)/depression (+)) and a faster rate of cognitive decline (e.g. β = -0.040, 95% CI = (-0.047, -0.034) for CMM5 (+)/depression (+)), compared with those without either condition. These associations remained consistent after additional adjustment for APOE genotype and were robust in two-step random-effects IPD analyses. The findings regarding the joint association of CMM and depression on cognitive function were reproduced in the three external validation studies.

Interpretation

Our findings highlighted the importance of investigating age-related co-morbidities in a multi-dimensional perspective. Targeting both cardiometabolic and psychological conditions to prevent cognitive decline could enhance effectiveness.

Funding

Natural Science Foundation of China and National Institute on Aging/National Institutes of Health.

Relationship between the triglyceride-glucose index and depression in individuals with chronic kidney disease: A cross-sectional study from National Health and Nutrition Examination Survey 2005-2020

Accumulating evidence indicates that individuals with chronic kidney disease (CKD) are at an increased risk of experiencing depressive disorders, which may accelerate its progression. However, the relationship between the triglyceride-glucose (TyG) index and depression in CKD individuals remains unclear. Therefore, this cross-sectional study aimed to assess whether such a relationship exists. To this end, the CKD cohort of the National Health and Nutrition Examination Survey from 2005 to 2020 was analyzed using multivariable logistic regression analyses and a generalized additive approach. A recursive algorithm was employed to pinpoint the turning point, constructing a dual-segment linear regression model. The study included 10,563 participants. After controlling for all variables, the odds ratios and 95% confidence intervals indicated a 1.24 (range, 1.09-1.42) relationship between the TyG index and depression in the CKD cohort. The findings underscored an asymmetrical association, with a pivotal value at a TyG index 9.29. Above this threshold, the adjusted odds ratio (95% confidence interval) was 1.10 (range, 0.93-1.31). This relationship was significant among the obese subgroups. The study results highlight the complex relationship between the TyG index and depression among American adults with CKD.

Investigating the Epigenetic Landscape of Major Depressive Disorder: A Genome-Wide Meta-Analysis of DNA Methylation Data, Including New Insights into Stochastic Epigenetic Mutations and Epivariations

Background/Objectives: Major depressive disorder (MDD) is a mental health condition that can severely impact patients' social lives, leading to withdrawal and difficulty in maintaining relationships. Environmental factors such as trauma and stress can worsen MDD by interacting with genetic predispositions. Epigenetics, which examines changes in gene expression influenced by the environment, may help identify patterns linked to depression. This study aimed to explore the epigenetic mechanisms behind MDD by analysing six public datasets (n = 1125 MDD cases, 398 controls in blood; n = 95 MDD cases, 96 controls in brain tissues) from the Gene Expression Omnibus. Methods: As an innovative approach, two meta-analyses of DNA methylation patterns were conducted alongside an investigation of stochastic epigenetic mutations (SEMs), epigenetic age acceleration, and rare epivariations. Results: While no significant global methylation differences were observed between MDD cases and controls, hypomethylation near the SHF gene (brain-specific probe cg25801113) was consistently found in MDD cases. SEMs revealed a gene-level burden in MDD, though epigenetic age acceleration was not central to the disorder. Additionally, 51 rare epivariations were identified in blood tissue and 1 in brain tissue linked to MDD. Conclusions: The study emphasises the potential role of rare epivariations in MDD's epigenetic regulation but calls for further research with larger, more diverse cohorts to confirm these findings.

Longitudinal development of resting-state functional connectivity in adolescents with and without internalizing disorders

Longitudinal studies using resting-state functional magnetic resonance imaging (rs-fMRI) focused on adolescent internalizing psychopathology are scarce and have mostly investigated standardized treatment effects on functional connectivity (FC) of the full amygdala. The role of amygdala subregions and large resting-state networks had yet to be elucidated, and treatment is in practice often personalized. Here, longitudinal FC development of amygdala subregions and whole-brain networks are investigated in a clinically representative sample. Treatment-naïve adolescents with clinical depression and comorbid anxiety who started care-as-usual (n = 23; INT) and healthy controls (n = 24; HC) participated in rs-fMRI scans and questionnaires at baseline (before treatment) and after three months. Changes between and within groups over time in FC of the laterobasal amygdala (LBA), centromedial amygdala (CMA) and whole-brain networks derived from independent component analysis (ICA) were investigated. Groups differed significantly in FC development of the right LBA to the postcentral gyrus and the left LBA to the frontal pole. Within INT, FC to the frontal pole and postcentral gyrus changed over time while changes in FC of the right LBA were also linked to symptom change. No significant interactions were observed when considering FC from CMA bilateral seeds or within ICA-derived networks. Results in this cohort suggest divergent longitudinal development of FC from bilateral LBA subregions in adolescents with internalizing disorders compared to healthy peers, possibly reflecting nonspecific treatment effects. Moreover, associations were found with symptom change. These results highlight the importance of differentiation of amygdala subregions in neuroimaging research in adolescents.

Brain reserve in midlife is associated with executive function changes across 12 years

We examined how brain reserve in midlife, measured by brain-predicted age difference scores (Brain-PADs), predicted executive function concurrently and longitudinally into early old age, and whether these associations were moderated by young adult cognitive reserve or APOE genotype. 508 men in the Vietnam Era Twin Study of Aging (VETSA) completed neuroimaging assessments at mean age 56 and six executive function tasks at mean ages 56, 62, and 68 years. Results indicated that greater brain reserve at age 56 was associated with better concurrent executive function (r=.10, p=.040) and less decline in executive function over 12 years (r=.34, p=.001). These associations were not moderated by cognitive reserve or APOE genotype. Twin analysis suggested associations with executive function slopes were driven by genetic influences. Our findings suggest that greater brain reserve allowed for better cognitive maintenance from middle- to old age, driven by a genetic association. The results are consistent with differential preservation of executive function based on brain reserve that is independent of young adult cognitive reserve or APOE genotype.

Voxelwise Multivariate Analysis of Brain-Psychosocial Associations in Adolescents Reveals 6 Latent Dimensions of Cognition and Psychopathology

BACKGROUND

Adolescence heralds the onset of considerable psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxelwise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges.

METHODS

We obtained voxelwise gray matter density and psychosocial variables from the baseline (ages 9-10 years) Adolescent Brain Cognitive Development (ABCD) Study cohort (N = 11,288) and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting.

RESULTS

We found 6 latent dimensions, 4 of which pertain specifically to mental health. The mental health dimensions were related to overeating, anorexia/internalizing, oppositional symptoms (all ps < .002) and attention-deficit/hyperactivity disorder symptoms (p = .03). Attention-deficit/hyperactivity disorder was related to increased and internalizing symptoms related to decreased gray matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms were related to increased gray matter in a noradrenergic nucleus. Internalizing symptoms were related to increased and oppositional symptoms to reduced gray matter density in the insular, cingulate, and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus gray matter in attention-deficit/hyperactivity disorder and reduced putamen gray matter in oppositional/conduct problems. Voxelwise gray matter density generated stronger brain-psychosocial correlations than brain parcellations.

CONCLUSIONS

Voxelwise brain data strengthen latent dimensions of brain-psychosocial covariation, and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing symptoms are associated with opposite gray matter changes in similar cortical and subcortical areas.

Integrative molecular and structural neuroimaging analyses of the interaction between depression and age of onset: A multimodal magnetic resonance imaging study

Depression is a neurodevelopmental disorder that exhibits progressive gray matter volume (GMV) atrophy. Research indicates that brain development is influential in depression-induced GMV alterations. However, the interaction between depression and age of onset is not well understood by the underlying molecular and neuropathological mechanisms. Thus, 152 first-episode depression individuals and matched 130 healthy controls (HCs) were recruited to undergo T1-weighted high-resolution magnetic resonance imaging for this study. By two-way ANOVA, age and diagnosis were used as factors when analyzing the interaction of GMV in the participants. Then, spatial correlations between neurotransmitter maps and factor-related volume maps are established. Results illustrate a pronounced antagonistic interaction between depression and age of onset in the right insula, superior temporal gyrus, anterior cingulate gyrus, and orbitofrontal gyrus. Depression-caused reductions in GMV are mainly distributed in thalamic-limbic-cortical regions, regardless of age. For the main effect of age, adults exhibit brain atrophy in frontal, cerebellum, parietal, and temporal lobe structures. Cross-modal correlations showed that GMV changes in the interactive regions were linked with the serotonergic system and dopaminergic systems. Summarily, our results reveal the interaction between depression and age of onset in neurobiological mechanisms, which provide hints for future treatment of different ages of depression.

An estimate of the longitudinal pace of aging from a single brain scan predicts dementia conversion, morbidity, and mortality

To understand how aging affects functional decline and increases disease risk, it is necessary to develop accurate and reliable measures of how fast a person is aging. Epigenetic clocks measure aging but require DNA methylation data, which many studies lack. Using data from the Dunedin Study, we introduce an accurate and reliable measure for the rate of longitudinal aging derived from cross-sectional brain MRI: the Dunedin Pace of Aging Calculated from NeuroImaging or DunedinPACNI. Exporting this measure to the Alzheimer's Disease Neuroimaging Initiative and UK Biobank neuroimaging datasets revealed that faster DunedinPACNI predicted participants' cognitive impairment, accelerated brain atrophy, and conversion to diagnosed dementia. Underscoring close links between longitudinal aging of the body and brain, faster DunedinPACNI also predicted physical frailty, poor health, future chronic diseases, and mortality in older adults. Furthermore, DunedinPACNI followed the expected socioeconomic health gradient. When compared to brain age gap, an existing MRI aging biomarker, DunedinPACNI was similarly or more strongly related to clinical outcomes. DunedinPACNI is a "next generation" MRI measure that will be made publicly available to the research community to help accelerate aging research and evaluate the effectiveness of dementia prevention and anti-aging strategies.

Atypical Brain Aging and Its Association With Working Memory Performance in Major Depressive Disorder

BACKGROUND

Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to those seen in aging. However, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool that quantifies normative neurodevelopmental trajectories.

METHODS

A total of 304 participants with MDD and 236 control participants without depression were recruited and scanned from 3 studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for 1) differences between participants with MDD and control participants; 2) differences between individuals with versus without severe childhood maltreatment; and 3) correlations with depressive symptom severity, neurocognitive assessment domains, and escitalopram treatment response.

RESULTS

Brain centiles were significantly lower in the MDD group than in the control group. Brain centile was also significantly correlated with working memory in the control group but not the MDD group. No significant associations were observed between depression severity or antidepressant treatment response and brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles.

CONCLUSIONS

Consistent with previous work on machine learning models that predict brain age, brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications for neurocognitive deficits associated with aging-related cognitive function.

The neurostructural consequences of glaucoma and their overlap with disorders exhibiting emotional dysregulations: A voxel-based meta-analysis and tripartite system model

BACKGROUND

Glaucoma, a progressive neurodegenerative disorder leading to irreversible blindness, is associated with heightened rates of generalized anxiety and depression. This study aims to comprehensively investigate brain morphological changes in glaucoma patients, extending beyond visual processing areas, and explores overlaps with morphological alterations observed in anxiety and depression.

METHODS

A comparative meta-analysis was conducted, using case-control studies of brain structural integrity in glaucoma patients. We aimed to identify regions with gray matter volume (GMV) changes, examine their role within distinct large-scale networks, and assess overlap with alterations in generalized anxiety disorder (GAD) and major depressive disorder (MDD).

RESULTS

Glaucoma patients exhibited significant GMV reductions in visual processing regions (lingual gyrus, thalamus). Notably, volumetric reductions extended beyond visual systems, encompassing the left putamen and insula. Behavioral and functional network decoding revealed distinct large-scale networks, implicating visual, motivational, and affective domains. The insular region, linked to pain and affective processes, displayed reductions overlapping with alterations observed in GAD.

LIMITATIONS

While the study identified significant morphological alterations, the number of studies from both the glaucoma and GAD cohorts remains limited due to the lack of independent studies meeting our inclusion criteria.

CONCLUSION

The study proposes a tripartite brain model for glaucoma, with visual processing changes related to the lingual gyrus and additional alterations in the putamen and insular regions tied to emotional or motivational functions. These neuroanatomical changes extend beyond the visual system, implying broader implications for brain structure and potential pathological developments, providing insights into the overall neurological consequences of glaucoma.

Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort

In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC

Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.

Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial

INTRODUCTION

One major challenge in developing personalised repetitive transcranial magnetic stimulation (rTMS) is that the treatment responses exhibited high inter-individual variations. Brain morphometry might contribute to these variations. This study sought to determine whether individual's brain morphometry could predict the rTMS responders and remitters.

METHODS

This was a secondary analysis of data from a randomised clinical trial that included fifty-five patients over the age of 60 with both comorbid depression and neurocognitive disorder. Based on magnetic resonance imaging scans, estimated brain age was calculated with morphometric features using a support vector machine. Brain-predicted age difference (brain-PAD) was computed as the difference between brain age and chronological age.

RESULTS

The rTMS responders and remitters had younger brain age. Every additional year of brain-PAD decreased the odds of relieving depressive symptoms by ∼25.7% in responders (Odd ratio [OR] = 0.743, p = .045) and by ∼39.5% in remitters (OR = 0.605, p = .022) in active rTMS group. Using brain-PAD score as a feature, responder-nonresponder classification accuracies of 85% (3rd week) and 84% (12th week), respectively were achieved.

CONCLUSION

In elderly patients, younger brain age appears to be associated with better treatment responses to active rTMS. Pre-treatment brain age models informed by morphometry might be used as an indicator to stratify suitable patients for rTMS treatment.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: ChiCTR-IOR-16008191.

Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning

Purpose

This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data.

Methods

We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features. A multi-flow learning-based framework attentively merges these two features, employing sleep structural information to direct and augment the EEG features. A post-prediction model is designed to integrate the age-related features throughout the night. Furthermore, we propose a DecadeCE loss function to address the problem of an uneven age distribution.

Results

We utilized 18,767 polysomnograms (PSGs) from 13,616 subjects to develop and evaluate the proposed model. The model achieves a mean absolute error (MAE) of 4.19 and a correlation of 0.97 on the mixed-cohort test set, and an MAE of 6.18 years and a correlation of 0.78 on an independent test set. Our brain age estimation work reduced the error by more than 1 year compared to other studies that also used EEG, achieving the level of neuroimaging. The estimated brain age index demonstrated longitudinal sensitivity and exhibited a significant increase of 1.27 years in individuals with psychiatric or neurological disorders relative to healthy individuals.

Conclusion

The multi-flow deep learning model proposed in this study, based on overnight EEG, represents a more accurate approach for estimating brain age. The utilization of overnight sleep EEG for the prediction of brain age is both cost-effective and adept at capturing dynamic changes. These findings demonstrate the potential of EEG in predicting brain age, presenting a noninvasive and accessible method for assessing brain aging.

Examining the associations between microglia genetic capacity, early life exposures and white matter development at the level of the individual

There are inter-individual differences in susceptibility to the influence of early life experiences for which the underlying neurobiological mechanisms are poorly understood. Microglia play a role in environmental surveillance and may influence individual susceptibility to environmental factors. As an index of neurodevelopment, we estimated individual slopes of mean white matter fractional anisotropy (WM-FA) across three time-points (age 4.5, 6.0, and 7.5 years) for 351 participants. Individual variation in microglia reactivity was derived from an expression-based polygenic score(ePGS) comprised of Single Nucleotide Polymorphisms (SNPs) functionally related to the expression of microglia-enriched genes.A higher ePGS denotes an increased genetic capacity for the expression of microglia-related genes, and thus may confer a greater capacity to respond to the early environment and to influence brain development. We hypothesized that this ePGS would associate with the WM-FA index of neurodevelopment and moderate the influence of early environmental factors.Our findings show sex dependency, where a significant association between WM-FA and microglia ePGS was only obtained for females.We then examined associations with perinatal factors known to decrease (optimal birth outcomes and familial conditions) or increase (systemic inflammation) the risk for later mental health problems.In females, individuals with high microglia ePGS showed a negative association between systemic inflammation and WM-FA and a positive association between more advantageous environmental conditions and WM-FA. The microglia ePGS in females thus accounted for variations in the influence of the quality of the early environment on WM-FA.Finally, WM-FA slopes mediated the association of microglia ePGS with interpersonal problems and social hostility in females. Our findings suggest the genetic capacity for microglia function as a potential factor underlying differential susceptibility to early life exposuresthrough influences on neurodevelopment.