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

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.

Psilocybin for dementia prevention? The potential role of psilocybin to alter mechanisms associated with major depression and neurodegenerative diseases

Major depression is an established risk factor for subsequent dementia, and depression in late life may also represent a prodromal state of dementia. Considering current challenges in the clinical development of disease modifying therapies for dementia, the focus of research is shifting towards prevention and modification of risk factors to alter the neurodegenerative disease trajectory. Understanding mechanistic commonalities underlying affective symptoms and cognitive decline may reveal biomarkers to aid early identification of those at risk of progressing to dementia during the preclinical phase of disease, thus allowing for timely intervention. Adult hippocampal neurogenesis (AHN) is a phenomenon that describes the birth of new neurons in the dentate gyrus throughout life and it is associated with spatial learning, memory and mood regulation. Microglia are innate immune system macrophages in the central nervous system that carefully regulate AHN via multiple mechanisms. Disruption in AHN is associated with both dementia and major depression and microgliosis is a hallmark of several neurodegenerative diseases. Emerging evidence suggests that psychedelics promote neuroplasticity, including neurogenesis, and may also be immunomodulatory. In this context, psilocybin, a serotonergic agonist with rapid-acting antidepressant properties has the potential to ameliorate intersecting pathophysiological processes relevant for both major depression and neurodegenerative diseases. In this narrative review, we focus on the evidence base for the effects of psilocybin on adult hippocampal neurogenesis and microglial form and function; which may suggest that psilocybin has the potential to modulate multiple mechanisms of action, and may have implications in altering the progression from major depression to dementia in those at risk.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression

Cross-sectional studies have demonstrated strong associations between physical frailty and depression. However, the evidence from prospective studies is limited. Here, we analyze data of 352,277 participants from UK Biobank with 12.25-year follow-up. Compared with non-frail individuals, pre-frail and frail individuals have increased risk for incident depression independent of many putative confounds. Altogether, pre-frail and frail individuals account for 20.58% and 13.16% of depression cases by population attributable fraction analyses. Higher risks are observed in males and individuals younger than 65 years than their counterparts. Mendelian randomization analyses support a potential causal effect of frailty on depression. Associations are also observed between inflammatory markers, brain volumes, and incident depression. Moreover, these regional brain volumes and three inflammatory markers-C-reactive protein, neutrophils, and leukocytes-significantly mediate associations between frailty and depression. Given the scarcity of curative treatment for depression and the high disease burden, identifying potential modifiable risk factors of depression, such as frailty, is needed.

An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr<0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, pcorr<0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders

Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.

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 aging. Yet, 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 quantifying normative neurodevelopmental trajectories.

METHODS

304 MDD participants and 236 non-depressed controls were recruited and scanned from three studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for: a) differences in MDD relative to controls; b) differences in individuals with versus without severe childhood maltreatment; and c) correlations with depressive symptom severity, neurocognitive assessment domains, or escitalopram treatment response.

RESULTS

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

CONCLUSIONS

Consistent with prior 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 on neurocognitive deficits associated with aging-related cognitive function.

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.

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person's brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual's brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.

Depressive symptom trajectories with prolonged rTMS treatment

BACKGROUND

A prolonged repetitive transcranial magnetic stimulation (rTMS) treatment course could be beneficial for some patients experiencing major depressive episodes (MDE). We identified trajectories of rTMS response in depressive patients who received an extended rTMS treatment course and sought to determine which trajectories achieved the greatest benefit with a prolonged treatment course.

METHOD

We applied group-based trajectory modeling to a naturalistic dataset of depressive patients receiving a prolonged course of sequential bilateral rTMS (up to 51 treatment sessions) to the dorsolateral prefrontal cortex. Trajectories of the PHQ-9 with extended treatment courses were characterized, and we explored the association between baseline clinical characteristics and group membership using multinomial logistic regression.

RESULTS

Among the 324 study participants, four trajectories were identified: "linear response, extended course" (N = 73; 22.5 %); "nonresponse" (N = 23; 7.1 %); "slowed response" (N = 159; 49.1 %); "rapid response, standard treatment length" (N = 69; 21.3 %). Only the "linear response, extended course" group showed considerable clinical improvement after receiving additional rTMS treatments. Greater baseline depressive symptoms were associated with linear response and non-response.

CONCLUSION

Our results confirmed the distinctive response trajectories in depressive patients receiving rTMS and further highlighted that prolonged rTMS treatment courses may be beneficial for a subset of patients with higher initial symptom levels and linear early treatment response.

Association of Retinal Neurovascular Impairment with Disease Severity in Patients with Major Depressive Disorder: An Optical Coherence Tomography Angiography Study

Background

Identifying the fundus objective biomarkers for the major depressive disorders (MDD) may help promote mental health. The aim of this study was to evaluate retinal neurovascular changes and further investigate their association with disease severity in MDD.

Methods

This cross-sectional study conducted in the hospital enrolled patients with MDD and healthy controls.The retinal neurovascular parameters for all subjects, including vessel density (VD), thickness of ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL), and optic nerve head (ONH) eg are automatically calculated by the software in optical coherence tomography angiography (OCTA). The severity of MDD including depressive symptoms, anxiety, cognition, and insomnia was assessed by Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Scale (HAMA), Montreal Cognitive Assessment (MoCA), and Insomnia Severity Index (ISI) respectively.

Results

This study included 74 MDD patients (n=74 eyes) and 60 healthy controls (HCs) (n=60 eyes). MDD patients showed significantly decreased VD of superficial and deep capillary plexus, thickness of GCC and RNFL, and volume of ONH (all p<0.05) and increased vertical cup-to-disc ratio and global loss volume (GLV) (all p<0.05) compared to HCs. Positive associations were found between HAMD scores and cup area (r=0.30, p=0.035), cup volume (r=0.31, p=0.029), and disc area (r=0.33, p=0.020) as well as ISI scores and RNFL thickness (r=0.34, p=0.047).

Conclusion

We found the retinal neurovascular impairment and its association with disease severity in MDD patients. OCTA showed promise as a potential complementary assessment tool for MDD.

Voxel-wise multivariate analysis of brain-psychosocial associations in adolescents reveals six latent dimensions of cognition and psychopathology

BACKGROUND

Adolescence heralds the onset of much 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 voxel-wise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges.

METHODS

We obtained voxel-wise grey matter density and psychosocial variables from the baseline (aged 9-10 years) Adolescent Brain and Cognitive Development cohort (n=11288), 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 six latent dimensions, four pertaining specifically to mental health. The mental health dimensions related to overeating, anorexia/internalizing, oppositional symptoms (all p<0.002) and ADHD symptoms (p=0.03). ADHD related to increased and internalizing related to decreased grey matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms related to increased grey matter in a noradrenergic nucleus. Internalizing related to increased and oppositional symptoms to reduced grey matter density in insula, cingulate and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus grey matter in ADHD, and reduced putamen grey matter in oppositional/conduct problems. Voxel-wise grey matter density generated stronger brain-psychosocial correlations than brain parcellations.

CONCLUSIONS

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

Examining the reliability of brain age algorithms under varying degrees of participant motion

Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.

BrainAGE, brain health, and mental disorders: A systematic review

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.

Accelerated brain aging as a biomarker for staging in bipolar disorder: an exploratory study

BACKGROUND

Two established staging models outline the longitudinal progression in bipolar disorder (BD) based on episode recurrence or inter-episodic functioning. However, underlying neurobiological mechanisms and corresponding biomarkers remain unexplored. This study aimed to investigate if global and (sub)cortical brain structures, along with brain-predicted age difference (brain-PAD) reflect illness progression as conceptualized in these staging models, potentially identifying brain-PAD as a biomarker for BD staging.

METHODS

In total, 199 subjects with bipolar-I-disorder and 226 control subjects from the Dutch Bipolar Cohort with a high-quality T1-weighted magnetic resonance imaging scan were analyzed. Global and (sub)cortical brain measures and brain-PAD (the difference between biological and chronological age) were estimated. Associations between individual brain measures and the stages of both staging models were explored.

RESULTS

A higher brain-PAD (higher biological age than chronological age) correlated with an increased likelihood of being in a higher stage of the inter-episodic functioning model, but not in the model based on number of mood episodes. However, after correcting for the confounding factors lithium-use and comorbid anxiety, the association lost significance. Global and (sub)cortical brain measures showed no significant association with the stages.

CONCLUSIONS

These results suggest that brain-PAD may be associated with illness progression as defined by impaired inter-episodic functioning. Nevertheless, the significance of this association changed after considering lithium-use and comorbid anxiety disorders. Further research is required to disentangle the intricate relationship between brain-PAD, illness stages, and lithium intake or anxiety disorders. This study provides a foundation for potentially using brain-PAD as a biomarker for illness progression.

Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort

BACKGROUND

Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging.

METHODS

In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality-cognition, cortical structure information, and the neuroanatomical measure of brain age gap-to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231).

RESULTS

The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%-12%, brain age gap 7%, cognition 0%-16%).

CONCLUSIONS

In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Longitudinal changes in brain-derived neurotrophic factor (BDNF) but not cytokines contribute to hippocampal recovery in anorexia nervosa above increases in body mass index

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Predictors of the Social Participation of Adults with Down Syndrome: An Analysis Based on the Biopsychosocial Model of the International Classification of Functioning, Disability, and Health

OBJECTIVE

The aim of the study is to identify the predictors of social participation in Down syndrome adults from the biopsychosocial model of the International Classification of Functioning, Disability, and Health.

METHODS

An exploratory, analytical, cross-sectional study was conducted with Down syndrome adults. The social participation was assessed using the Life Habits Assessment. The independent variables were determined using the International Classification of Functioning, Disability, and Health biopsychosocial model: body functions were assessed by body mass index, cognition function (Mini-Mental State Examination), and lower limbs muscle strength (Sit-to-Stand Test). Activities were assessed by the 8-Foot Up and Go Test. Environmental factors were assessed by the measure of the quality of the environment, and personal factors were assessed by age, sex, and education level.

RESULTS

The total Life Habits Assessment score indicates that individuals show moderate restriction in social participation, with major restriction in the education, employment, and responsibilities domains. The 8-Foot Up and Go Test was the best social participation predictor variable, followed by Mini-Mental State Examination, and the Sit-to-Stand Test. Contextual factors were not predictors of participation.

CONCLUSIONS

It was concluded that individuals with Down syndrome present the most restrictions to social participation in activities that involve social roles. The predictors influencing social participation are functional mobility, cognition, and lower limb muscle strength.

Differential longitudinal changes of hippocampal subfields in patients with anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a mental disorder characterized by dietary restriction, fear of gaining weight, and distorted body image. Recent studies indicate that the hippocampus, crucial for learning and memory, may be affected in AN, yet subfield-specific effects remain unclear. We investigated hippocampal subfield alterations in acute AN, changes following weight restoration, and their associations with leptin levels.

METHODS

T1-weighted magnetic resonance imaging scans were processed using FreeSurfer. We compared 22 left and right hemispheric hippocampal subfield volumes cross-sectionally and longitudinally in females with acute AN (n = 165 at baseline, n = 110 after partial weight restoration), healthy female controls (HCs; n = 271), and females after long-term recovery from AN (n = 79) using linear models.

RESULTS

We found that most hippocampal subfield volumes were significantly reduced in patients with AN compared with HCs (~-3.9%). Certain areas such as the subiculum exhibited no significant reduction in the acute state of AN, while other areas, such as the hippocampal tail, showed strong decreases (~-9%). Following short-term weight recovery, most subfields increased in volume. Comparisons between participants after long-term weight-recovery and HC yielded no differences. The hippocampal tail volume was positively associated with leptin levels in AN independent of body mass index.

CONCLUSIONS

Our study provides evidence of differential volumetric differences in hippocampal subfields between individuals with AN and HC and almost complete normalization after weight rehabilitation. These alterations are spatially inhomogeneous and more pronounced compared with other major mental disorders (e.g. major depressive disorder and schizophrenia). We provide novel insights linking hypoleptinemia to hippocampal subfield alterations hinting towards clinical relevance of leptin normalization in AN recovery.

Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment

BACKGROUND

The high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume.

METHODS

We enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors.

RESULTS

In contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146-0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800-0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination.

CONCLUSIONS

Capturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.

Common and distinct functional brain network abnormalities in adolescent, early-middle adult, and late adult major depressive disorders

BACKGROUND

The age-related heterogeneity in major depressive disorder (MDD) has received significant attention. However, the neural mechanisms underlying such heterogeneity still need further investigation. This study aimed to explore the common and distinct functional brain abnormalities across different age groups of MDD patients from a large-sample, multicenter analysis.

METHODS

The analyzed sample consisted of a total of 1238 individuals including 617 MDD patients (108 adolescents, 12-17 years old; 411 early-middle adults, 18-54 years old; and 98 late adults, > = 55 years old) and 621 demographically matched healthy controls (60 adolescents, 449 early-middle adults, and 112 late adults). MDD-related abnormalities in brain functional connectivity (FC) patterns were investigated in each age group separately and using the whole pooled sample, respectively.

RESULTS

We found shared FC reductions among the sensorimotor, visual, and auditory networks across all three age groups of MDD patients. Furthermore, adolescent patients uniquely exhibited increased sensorimotor-subcortical FC; early-middle adult patients uniquely exhibited decreased visual-subcortical FC; and late adult patients uniquely exhibited wide FC reductions within the subcortical, default-mode, cingulo-opercular, and attention networks. Analysis of covariance models using the whole pooled sample further revealed: (1) significant main effects of age group on FCs within most brain networks, suggesting that they are decreased with aging; and (2) a significant age group × MDD diagnosis interaction on FC within the default-mode network, which may be reflective of an accelerated aging-related decline in default-mode FCs.

CONCLUSIONS

To summarize, these findings may deepen our understanding of the age-related biological and clinical heterogeneity in MDD.

Predicting Age from White Matter Diffusivity with Residual Learning

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Molecular mechanisms underlying major depressive disorder and post-stroke affective disorders

Two of the most common and incapacitating mental health disorders around the world are major depressive disorder (MDD) and post-stroke depression (PSD). MDD is thought to result from abnormal connectivity between the monoaminergic, glutamatergic, GABAergic, and/or cholinergic pathways. Additional factors include the roles of hormonal, immune, ageing, as well as the influence of cellular, molecular, and epigenetics in the development of mood disorders. This complexity of factors has been anticipated by the Swiss psychiatrists Paul Kielholz and Jules Angst who introduced a multimodal treatment of MDD. Depression is the predominant mood disorder, impacting around one-third of individuals who have experienced a stroke. MDD and PSD share common underlying biological mechanisms related to the disruption of monoaminergic pathways. The major contributor to PSD is the stroke lesion location, which can involve the disruption of the serotoninergic, dopaminergic, glutamatergic, GABAergic, or cholinergic pathways. Additionally, various other disorders such as mania, bipolar disorder, anxiety disorder, and apathy might occur post-stroke, although their prevalence is considerably lower. However, there are differences in the onset of MDD among mood disorders. Some mood disorders develop gradually and can persist for a lifetime, potentially culminating in suicide. In contrast, PSD has a rapid onset because of the severe disruption of neural pathways essential for mood behavior caused by the lesion. However, PSD might also spontaneously resolve several months after a stroke, though it is associated with higher mortality. This review also provides a brief overview of the treatments currently available in medical practice.

Relative brain age is associated with socioeconomic status and anxiety/depression problems in youth

Brain age, a measure of biological aging in the brain, has been linked to psychiatric illness, principally in adult populations. Components of socioeconomic status (SES) associate with differences in brain structure and psychiatric risk across the lifespan. This study aimed to investigate the influence of SES on brain aging in childhood and adolescence, a period of rapid neurodevelopment and peak onset for many psychiatric disorders. We reanalyzed data from the Healthy Brain Network to examine the influence of SES components (occupational prestige, public assistance enrollment, parent education, and household income-to-needs ratio [INR]) on relative brain age (RBA). Analyses included 470 youth (5-17 years; 61.3% men), self-identifying as White (55%), African American (15%), Hispanic (9%), or multiracial (17.2%). Household income was 3.95 ± 2.33 (mean ± SD) times the federal poverty threshold. RBA quantified differences between chronological age and brain age using covariation patterns of morphological features and total volumes. We also examined associations between RBA and psychiatric symptoms (Child Behavior Checklist [CBCL]). Models covaried for sex, scan location, and parent psychiatric diagnoses. In a linear regression, lower RBA is associated with lower parent occupational prestige (p = .01), lower public assistance enrollment (p = .03), and more parent psychiatric diagnoses (p = .01), but not parent education or INR. Lower parent occupational prestige (p = .02) and lower RBA (p = .04) are associated with higher CBCL anxious/depressed scores. Our findings underscore the importance of including SES components in developmental brain research. Delayed brain aging may represent a potential biological pathway from SES to psychiatric risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Frontoamygdalar Effective Connectivity in Youth Depression and Treatment Response

BACKGROUND

Emotion regulation deficits are characteristic of youth depression and are underpinned by altered frontoamygdalar function. However, the causal dynamics of frontoamygdalar pathways in depression and how these dynamics relate to treatment prognosis remain unexplored. This study aimed to assess frontoamygdalar effective connectivity during cognitive reappraisal in youths with depression and to test whether pathway dynamics are predictive of individual response to combined cognitive behavioral therapy plus treatment with fluoxetine or placebo.

METHODS

One hundred seven young people with moderate to severe depression and 94 healthy control participants completed a functional magnetic resonance imaging cognitive reappraisal task. After the task, 87 participants with depression were randomized and received 12 weeks of cognitive behavioral therapy plus either fluoxetine or placebo. Dynamic causal modeling was used to map frontoamygdalar effective connectivity during reappraisal and to assess the predictive capacity of baseline frontoamygdalar effective connectivity on depression diagnosis and posttreatment depression remission.

RESULTS

Young people with depression showed weaker inhibitory modulation of ventrolateral prefrontal cortex to amygdala connectivity during reappraisal (0.29 Hz, posterior probability = 1.00). Leave-one-out cross-validation demonstrated that this effect was sufficiently large to predict individual diagnostic status (r = 0.20, p = .003). Posttreatment depression remission was associated with weaker excitatory ventromedial prefrontal cortex to amygdala connectivity (-0.56 Hz, posterior probability = 1.00) during reappraisal at baseline, though this effect did not predict individual remission status (r = -0.02, p = .561).

CONCLUSIONS

Frontoamygdalar effective connectivity shows promise in identifying youth depression diagnosis, and circuits responsible for negative affect regulation are implicated in responsiveness to first-line depression treatments.

Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes

Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement.

Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages

Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a person's chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower or accelerated biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a comparison of such packages with respect to (1) predictive accuracy, (2) test-retest reliability, and (3) the ability to track age progression over time. We evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 ± 17.5 years). All packages showed significant correlations between predicted brain age and chronological age (r = 0.66-0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94-0.98), as well as predicting age progression over a longer time span. Of the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.

Early longitudinal changes in brain structure and cognitive functioning in remitted patients with recently diagnosed bipolar disorder

BACKGROUND

Patients with bipolar disorder (BD) who are presenting with cognitive impairment and associated structural brain abnormalities have generally a poorer clinical outcome. This study aims to map the early longitudinal trajectories in brain structure and cognition in patients with recently diagnosed BD.

METHODS

Fully or partially remitted patients with a recent diagnosis of BD and matched healthy controls (HC) underwent structural MRI and neuropsychological testing at baseline (BD n = 97; HC n = 66) and again following an average of 16 (range 6-27) months (BD n = 50; HC n = 38). We investigated the differential trajectories in BD vs. HC in cortical gray matter volume and thickness, total cerebral white matter, hippocampal and amygdala volumes, estimated brain age, and cognitive functioning using linear mixed models. Within patients, we further investigated whether brain structural abnormalities detected at baseline were associated with subsequent mood episodes.

RESULTS

Compared to HC, patients showed a decline in total white matter volume over time and they had a larger amygdala volume, both at baseline and at follow-up time. Patients further showed lower cognitive performance at both times of investigation with no significant change over time. There were no differences between patients and HC in cortical gray matter volume or thickness, hippocampal volume, or brain-aging patterns.

CONCLUSIONS

Cognitive impairment and amygdala enlargement may represent stable markers of BD early in the course of illness, whereas subtle white matter decline may result from illness progression.

Telomere Length Associations With Clinical Diagnosis, Age, and Polygenic Risk Scores for Anxiety Disorder, Depression, and Bipolar Disorder

Background

Accelerated biological aging might contribute to the lower life expectancy of individuals with mental disorders. The aim of this study was to characterize telomere length, a biological hallmark of aging, in individuals with mental disorders.

Methods

The UK Biobank is a multicenter community-based observational study that recruited >500,000 middle-aged and older adults. Average leukocyte telomere length (telomere repeat copy number/single-copy gene ratio) was measured using quantitative polymerase chain reaction. Polygenic risk scores (PRSs) were calculated for individuals of European ancestry. We estimated differences in telomere length between individuals with anxiety disorder, depression, or bipolar disorder and people without mental disorders and examined associations with psychotropic medication use, age, and PRSs for these 3 disorders.

Results

The analyses included up to 308,725 participants. Individuals with depression had shorter telomeres than people without mental disorders (β = -0.011, 95% CI, -0.019 to -0.004, Bonferroni-corrected p = .027). Associations between bipolar disorder and telomere length differed by lithium use. There was limited evidence that individuals with an anxiety disorder had shorter telomeres. There was no evidence that associations between age and telomere length differed between individuals with and without these disorders. PRSs for depression, but not anxiety disorder or bipolar disorder, were associated with shorter telomeres (β = -0.006, 95% CI, -0.010 to -0.003, Bonferroni-corrected p = .001).

Conclusions

Differences in telomere length were observed primarily for individuals with depression or bipolar disorder and in individuals with a higher PRS for depression. There was no evidence that the association between age and telomere length differed between individuals with and without an anxiety disorder, depression, or bipolar disorder.

Differential alterations of amygdala nuclei volumes in acutely ill patients with anorexia nervosa and their associations with leptin levels

BACKGROUND

The amygdala is a subcortical limbic structure consisting of histologically and functionally distinct subregions. New automated structural magnetic resonance imaging (MRI) segmentation tools facilitate the in vivo study of individual amygdala nuclei in clinical populations such as patients with anorexia nervosa (AN) who show symptoms indicative of limbic dysregulation. This study is the first to investigate amygdala nuclei volumes in AN, their relationships with leptin, a key indicator of AN-related neuroendocrine alterations, and further clinical measures.

METHODS

T1-weighted MRI scans were subsegmented and multi-stage quality controlled using FreeSurfer. Left/right hemispheric amygdala nuclei volumes were cross-sectionally compared between females with AN (n = 168, 12-29 years) and age-matched healthy females (n = 168) applying general linear models. Associations with plasma leptin, body mass index (BMI), illness duration, and psychiatric symptoms were analyzed via robust linear regression.

RESULTS

Globally, most amygdala nuclei volumes in both hemispheres were reduced in AN v. healthy control participants. Importantly, four specific nuclei (accessory basal, cortical, medial nuclei, corticoamygdaloid transition in the rostral-medial amygdala) showed greater volumetric reduction even relative to reductions of whole amygdala and total subcortical gray matter volumes, whereas basal, lateral, and paralaminar nuclei were less reduced. All rostral-medially clustered nuclei were positively associated with leptin in AN independent of BMI. Amygdala nuclei volumes were not associated with illness duration or psychiatric symptom severity in AN.

CONCLUSIONS

In AN, amygdala nuclei are altered to different degrees. Severe volume loss in rostral-medially clustered nuclei, collectively involved in olfactory/food-related reward processing, may represent a structural correlate of AN-related symptoms. Hypoleptinemia might be linked to rostral-medial amygdala alterations.

Brain development mediates the relationship between self-reported poor parental monitoring and adolescent anxiety

Adolescence is the peak period for the onset of generalized anxiety disorder (GAD). Brain networks of cognitive and affective control in adolescents are not well developed when their exposure to external stimuli suddenly increases.Reasonable parental monitoring is especially important during this period.To examine the role of parental monitoring in the development of functional brain networks of GAD, we conducted a cross-validation-based predictive study based on the functional brain networks of 192 participants. We found that a set of functional brain networks, especially the default mode network and its connectivity with the frontoparietal network, could predict the ages of adolescents, which was replicated in three independent samples.Importantly, the difference between predicted age and chronological age significantly mediated the relationship between parental monitoring and anxiety levels. These findings suggest that inadequate parental monitoring plays a crucial role in the delayed development of specific brain networks associated with GAD in adolescents. Our work highlights the important role of parental monitoring in adolescent development.

Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers

The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain degeneration. However, existing brain age prediction models usually rely on a single marker and can not discover meaningful hidden information in radiographic images. This study focuses on the application of radiomics, an advanced imaging analysis technique, combined with automated machine learning to predict BAG. Our methods achieve a promising result with a mean absolute error of 1.509 using the Alzheimer's Disease Neuroimaging Initiative dataset. Furthermore, we find that the hippocampus and parahippocampal gyrus play a significant role in predicting age with interpretable method called SHapley Additive exPlanations. Additionally, our investigation of age prediction discrepancies between patients with Alzheimer's disease (AD) and those with mild cognitive impairment (MCI) reveals a notable correlation with clinical cognitive assessment scale scores. This suggests that BAG has the potential to serve as a biomarker to support the diagnosis of AD and MCI. Overall, this study presents valuable insights into the application of neuroimaging models in the diagnosis of neurodegenerative diseases.

Major depressive disorder

Major depressive disorder (MDD) is characterized by persistent depressed mood, loss of interest or pleasure in previously enjoyable activities, recurrent thoughts of death, and physical and cognitive symptoms. People with MDD can have reduced quality of life owing to the disorder itself as well as related medical comorbidities, social factors, and impaired functional outcomes. MDD is a complex disorder that cannot be fully explained by any one single established biological or environmental pathway. Instead, MDD seems to be caused by a combination of genetic, environmental, psychological and biological factors. Treatment for MDD commonly involves pharmacological therapy with antidepressant medications, psychotherapy or a combination of both. In people with severe and/or treatment-resistant MDD, other biological therapies, such as electroconvulsive therapy, may also be offered.

Brain age and cognitive functioning in first-episode bipolar disorder

BACKGROUND

There is significant heterogeneity in cognitive function in patients with bipolar I disorder (BDI); however, there is a dearth of research into biological mechanisms that might underlie cognitive heterogeneity, especially at disease onset. To this end, this study investigated the association between accelerated or delayed age-related brain structural changes and cognition in early-stage BDI.

METHODS

First episode patients with BDI (n = 80) underwent cognitive assessment to yield demographically normed composite global and domain-specific scores in verbal memory, non-verbal memory, working memory, processing speed, attention, and executive functioning. Structural magnetic resonance imaging data were also collected from all participants and subjected to machine learning to compute the brain-predicted age difference (brainPAD), calculated by subtracting chronological age from age predicted by neuroimaging data (positive brainPAD values indicating age-related acceleration in brain structural changes and negative values indicating delay). Patients were divided into tertiles based on brainPAD values, and cognitive performance compared amongst tertiles with ANCOVA.

RESULTS

Patients in the lowest (delayed) tertile of brainPAD values (brainPAD range -17.9 to -6.5 years) had significantly lower global cognitive scores (p = 0.025) compared to patients in the age-congruent tertile (brainPAD range -5.3 to 2.4 yrs), and significantly lower verbal memory scores (p = 0.001) compared to the age-congruent and accelerated (brainPAD range 2.8 to 16.1 yrs) tertiles.

CONCLUSION

These results provide evidence linking cognitive dysfunction in the early stage of BDI to apparent delay in typical age-related brain changes. Further studies are required to assess how age-related brain changes and cognitive functioning evolve over time.

Genetic architecture of brain age and its causal relations with brain and mental disorders

The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.

The Nonlinear Relationship Between Body Mass Index (BMI) and Perceived Depression in the Chinese Population

Purpose

Existing studies on the association between BMI and depression report conflicting results with some demonstrating a positive relationship, while others a negative link or insignificant correlation. Very limited research on the nonlinear relationship between BMI and depression has yet to clarify the reliability and robustness of the potential nonlinearity and whether a more complex association exists. This paper aims to systematically investigate the nonlinear relationship between the two factors applying rigorous statistical methods, as well as explore the heterogeneity of their association.

Materials and Methods

A large-scale nationally representative dataset, Chinese General Social Survey, is used to empirically analyze the nonlinear relationship between BMI and perceived depression. Various statistical tests are employed to check the robustness of the nonlinearity.

Results

Results indicate that there is a U-shaped relationship between BMI and perceived depression, with the turning point (25.718) very close to while slightly larger than the upper limit of the range of healthy weight (18.500 ≤ BMI < 25.000) defined by World Health Organization. Both very high and low BMIs are associated with increased risk for depressive disorders. Furthermore, perceived depression is higher at almost all BMI levels among individuals who are older, female, lower educated, unmarried, in rural areas, belonging to ethnic minorities, non-Communist Party of China members, as well as those with lower income and uncovered by social security. In addition, these subgroups have smaller inflection points and their self-rated depression is more sensitive to BMI.

Conclusion

This paper confirms a significant U-shaped trend in the association between BMI and depression. Therefore, it is important to account for the variations in this relationship across different BMI categories when using BMI to predict depression risk. Besides, this study clarifies the management goals for achieving an appropriate BMI from a mental health perspective and identifies vulnerable subgroups at higher risk of depression.

Cocaine Destroys Gray Matter Brain Cells and Accelerates Brain Aging

Introduction: Cocaine use disorder (CUD) is a substance use disorder characterized by a strong desire to obtain, consume, and misuse cocaine. Little is known about how cocaine affects the structure of the brain. In this study, we first investigated the anatomical brain changes in individuals with CUD compared to their matched healthy controls, and then explored whether these anatomical brain abnormalities contribute to considerably accelerated brain aging among this population. Methods: At the first stage, we used anatomical magnetic resonance imaging (MRI) data, voxel-based morphometry (VBM), and deformation-based morphometry techniques to uncover the morphological and macroscopic anatomical brain changes in 74 CUD patients compared to 62 age- and sex-matched healthy controls (HCs) obtained from the SUDMEX CONN dataset, the Mexican MRI dataset of patients with CUD. Then, we computed brain-predicted age difference (i.e., brain-PAD: the brain-predicted age minus the actual age) in CUD and HC groups using a robust brain age estimation framework. Using a multiple regression analysis, we also investigated the regional gray matter (GM) and white matter (WM) changes associated with the brain-PAD. Results: Using a whole-brain VBM analysis, we observed widespread gray matter atrophy in CUD patients located in the temporal lobe, frontal lobe, insula, middle frontal gyrus, superior frontal gyrus, rectal gyrus, and limbic lobe regions compared to the HCs. In contrast, we did not observe any swelling in the GM, changes in the WM, or local brain tissue atrophy or expansion between the CUD and HC groups. Furthermore, we found a significantly higher brain-PAD in CUD patients compared to matched HCs (mean difference = 2.62 years, Cohen's d = 0.54; t-test = 3.16, p = 0.002). The regression analysis showed significant negative changes in GM volume associated with brain-PAD in the CUD group, particularly in the limbic lobe, subcallosal gyrus, cingulate gyrus, and anterior cingulate regions. Discussion: The results of our investigation reveal that chronic cocaine use is linked to significant changes in gray matter, which hasten the process of structural brain aging in individuals who use the drug. These findings offer valuable insights into the impact of cocaine on the composition of the brain.

Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke

BACKGROUND AND OBJECTIVES

Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes.

METHODS

We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage.

RESULTS

We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β = 0.21; 95% CI 0.04-0.38, p = 0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (β = -0.28; 95% CI -0.41 to -0.15, p < 0.001) and across multiple domains of function (β = -0.14; 95% CI -0.22 to -0.06, p < 0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI 3%-58%, p = 0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (odds ratio 1.04, 95% CI 1.01-1.08, p = 0.004).

DISCUSSION

We provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

Spontaneous beta power, motor-related beta power and cortical thickness in major depressive disorder with psychomotor disturbance

INTRODUCTION

The psychomotor disturbance is a common symptom in patients with major depressive disorder (MDD). The neurological mechanisms of psychomotor disturbance are intricate, involving alterations in the structure and function of motor-related regions. However, the relationship among changes in the spontaneous activity, motor-related activity, local cortical thickness, and psychomotor function remains unclear.

METHOD

A total of 140 patients with MDD and 68 healthy controls performed a simple right-hand visuomotor task during magnetoencephalography (MEG) scanning. All patients were divided into two groups according to the presence of psychomotor slowing. Spontaneous beta power, movement-related beta desynchronization (MRBD), absolute beta power during movement and cortical characteristics in the bilateral primary motor cortex were compared using general linear models with the group as a fixed effect and age as a covariate. Finally, the moderated mediation model was tested to examine the relationship between brain metrics with group differences and psychomotor performance.

RESULTS

The patients with psychomotor slowing showed higher spontaneous beta power, movement-related beta desynchronization and absolute beta power during movement than patients without psychomotor slowing. Compared with the other two groups, significant decreases were found in cortical thickness of the left primary motor cortex in patients with psychomotor slowing. Our moderated mediation model showed that the increased spontaneous beta power indirectly affected impaired psychomotor performance by abnormal MRBD, and the indirect effects were moderated by cortical thickness.

CONCLUSION

These results suggest that patients with MDD have aberrant cortical beta activity at rest and during movement, combined with abnormal cortical thickness, contributing to the psychomotor disturbance observed in this patient population.

Biological factors influencing depression in later life: role of aging processes and treatment implications

Late-life depression occurring in older adults is common, recurrent, and malignant. It is characterized by affective symptoms, but also cognitive decline, medical comorbidity, and physical disability. This behavioral and cognitive presentation results from altered function of discrete functional brain networks and circuits. A wide range of factors across the lifespan contributes to fragility and vulnerability of those networks to dysfunction. In many cases, these factors occur earlier in life and contribute to adolescent or earlier adulthood depressive episodes, where the onset was related to adverse childhood events, maladaptive personality traits, reproductive events, or other factors. Other individuals exhibit a later-life onset characterized by medical comorbidity, pro-inflammatory processes, cerebrovascular disease, or developing neurodegenerative processes. These later-life processes may not only lead to vulnerability to the affective symptoms, but also contribute to the comorbid cognitive and physical symptoms. Importantly, repeated depressive episodes themselves may accelerate the aging process by shifting allostatic processes to dysfunctional states and increasing allostatic load through the hypothalamic-pituitary-adrenal axis and inflammatory processes. Over time, this may accelerate the path of biological aging, leading to greater brain atrophy, cognitive decline, and the development of physical decline and frailty. It is unclear whether successful treatment of depression and avoidance of recurrent episodes would shift biological aging processes back towards a more normative trajectory. However, current antidepressant treatments exhibit good efficacy for older adults, including pharmacotherapy, neuromodulation, and psychotherapy, with recent work in these areas providing new guidance on optimal treatment approaches. Moreover, there is a host of nonpharmacological treatment approaches being examined that take advantage of resiliency factors and decrease vulnerability to depression. Thus, while late-life depression is a recurrent yet highly heterogeneous disorder, better phenotypic characterization provides opportunities to better utilize a range of nonspecific and targeted interventions that can promote recovery, resilience, and maintenance of remission.

A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility

Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

BACKGROUND AND AIMS

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.

DESIGN

Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.

SETTING

United Kingdom.

PARTICIPANTS

The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].

MEASUREMENTS

Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.

FINDINGS

The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results.

CONCLUSIONS

There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

BrainAge of patients with severe late-life depression referred for electroconvulsive therapy

BACKGROUND

Severe depression is associated with accelerated brain aging. BrainAge gap, the difference between predicted and observed BrainAge, was investigated in patients with late-life depression (LLD). We aimed to examine BrainAge gap in LLD and its associations with clinical characteristics indexing LLD chronicity, current severity, prior to electroconvulsive therapy (ECT) and ECT outcome.

METHODS

Data was analyzed from the Mood Disorders in Elderly treated with Electroconvulsive Therapy (MODECT) study. A previously established BrainAge algorithm (BrainAge R by James Cole, (https://github.com/james-cole/brainageR)) was applied to pre-ECT T1-weighted structural MRI-scans of 42 patients who underwent ECT.

RESULTS

A BrainAge gap of 1.8 years (SD = 5.5) was observed, Cohen's d = 0.3. No significant associations between BrainAge gap, number of previous episodes, current episode duration, age of onset, depression severity, psychotic symptoms or ECT outcome were observed.

LIMITATIONS

Limited sample size.

CONCLUSIONS

Our initial findings suggest an older BrainAge than chronological age in patients with severe LLD referred for ECT, however with high degree of variability and direction of the gap. No associations were found with clinical measures. Larger samples are needed to better understand brain aging and to evaluate the usability of BrainAge gap as potential biomarker of prognosis an treatment-response in LLD.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02667353.

Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer's disease and/or major depressive disorder

Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both major depressive disorder (MDD) and Alzheimer's disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.

Advances in stress and depression research

PURPOSE OF REVIEW

Stress plays a central role in the onset and course of depression. However, only a subset of people who encounter stressful life events go on to experience a depressive episode. The current review highlights recent advances in understanding when, why, and for whom the stress-depression link occurs, and we identify avenues for future research.

RECENT FINDINGS

In the last 18 months, researchers have taken a more nuanced perspective on the biopsychosocial mechanisms critical to the stress-depression link. For example, examination of specific facets of emotion regulation, including emotion regulation flexibility and interpersonal emotion regulation, has been critical to understanding its role in depression. Similarly, refined investigations of social support allowed researchers to identify distinct - and occasionally opposite - outcomes depending on the context or manner in which the support was provided. Researchers also documented that the stress-depression link was enhanced by dysregulation of several stress-sensitive biological systems, such as the immune system, microbiome, endocrine system, and neuroanatomical substrates.

SUMMARY

Recent studies highlight the importance of adopting a nuanced understanding of mechanisms and moderators that explain the stress-depression link. We also encourage continued engagement in collaborative, open science that uses multiple methods to study the full breadth of human diversity.

Long-term depressive symptoms and midlife brain age

BACKGROUND

Evidence suggests that depression may be a risk factor for dementia in older adults, but the link between depressive symptoms and brain health earlier in life is less understood. Our aim was to investigate the association between long-term depressive symptoms in young to mid-adulthood and a measure of brain age derived from structural MRI.

METHODS

From the Coronary Artery Risk Development in Young Adults study, we identified 649 participants (age 23-36 at baseline) with brain MRI and cognitive testing. Long-term depressive symptoms were measured with the Center for Epidemiological Studies Depression scale (CESD) six times across 25 years and analyzed as time-weighted averages (TWA). Brain age was derived using previously validated high dimensional neuroimaging pattern analysis, quantifying individual differences in age-related atrophy. Elevated depressive symptoms were defined as CES-D ≥16. Linear regression was used to test the association between TWA depressive symptoms, brain aging, and cognition.

RESULTS

Each standard deviation (5-points) increment in TWA depression symptoms over 25 years was associated with one-year greater brain age (β: 1.14, 95 % confidence interval [CI]: 0.57 to 1.71). Participants with elevated TWA depressive symptoms had on average a 3-year greater brain age (β: 2.75, 95 % CI: 0.43 to 5.08). Moreover, elevated depressive symptoms were associated with higher odds of poor cognitive function in midlife (OR: 3.30, 95 % CI: 1.37 to 7.97).

LIMITATIONS

Brain age was assessed at one time, limiting our ability to evaluate the temporality of depressive symptoms and brain aging.

CONCLUSIONS

Elevated depressive symptoms in early adulthood may have implications for brain health as early as in midlife.

Major depression and the biological hallmarks of aging

Major depressive disorder (MDD) is characterized by psychological and physiological manifestations contributing to the disease severity and outcome. In recent years, several lines of evidence have suggested that individuals with MDD have an elevated risk of age-related adverse outcomes across the lifespan. This review provided evidence of a significant overlap between the biological abnormalities in MDD and biological changes commonly observed during the aging process (i.e., hallmarks of biological aging). Based on such evidence, we formulate a mechanistic model showing how abnormalities in the hallmarks of biological aging can be a common denominator and mediate the elevated risk of age-related health outcomes commonly observed in MDD. Finally, we proposed a roadmap for novel studies to investigate the intersection between the biology of aging and MDD, including the use of geroscience-guided interventions, such as senolytics, to delay or improve major depression by targeting biological aging.