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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., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5-90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

The neural mechanisms underlying the therapeutic effects of lamotrigine in bipolar depression are still unexplored. This preliminary study compares the effects of a 12-week treatment with lamotrigine on brain volumes in adults with bipolar disorder (BD).12 BD type II patients (age: 49.33 ± 9.95 years, 3 males, 9 females) and 12 age and gender-matched healthy controls (HC) (HC; age: 41 ± 8.60 years, 3 males, 9 females). BD patients were initially administered 25 mg/day of lamotrigine, which was progressively escalated to 200 mg/d. BD participants underwent brain imaging prior to and following lamotrigine treatment. A 50% reduction in depressive scores indicated "remission". Bayesian general linear models controlled for age, gender and intracranial volume were used to examine changes in relevant brain region following treatment. A posterior probability > 0.90 indicated evidence that there was an effect of diagnosis or remission on brain volumes. Probability distributions of interaction effects between remission and time indicated that BD responders displayed decreased amygdala, cerebellum and nucleus accumbens volumes following lamotrigine treatment. No serious adverse side effects were reported. The antidepressant effects of lamotrigine may be linked to volumetric changes in brain regions involved in mood and emotional regulation. These findings are preliminary and replication in a larger sample is warranted., (Copyright © 2018. Published by Elsevier B.V.)

Previous studies have found increased levels of choline-containing compounds (ie, glycerophosphocholine plus phosphocholine (GPC+PC)) in bipolar disorder using in vivo proton magnetic resonance spectroscopy ( 1 H MRS), especially in bipolar I disorder (BD-I). Increased levels of GPC+PC suggest alterations in the membrane phospholipids metabolism in bipolar disorder. Rapid cycling (RC) bipolar disorder is considered as a severe course of bipolar disorder, but it is unclear whether rapid cycling bipolar disorder is linked to highly altered membrane phospholipid metabolism. The purpose of this study was to investigate whether the regional extent of elevated GPC+PC were greater in BD-I patients with rapid cycling compared to BD-I patients without rapid cycling and healthy controls. Using a multi-voxel 1 H MRS approach at 3 Tesla with high spatial resolution and absolute quantification, GPC+PC levels from the anterior cingulate cortex (ACC), caudate and putamen of 16 RC BD-I, 34 non-RC BD-I and 44 healthy controls were assessed. We found significantly elevated GPC+PC levels in ACC, putamen and caudate of RC BD-I patients compared to healthy controls (P<0.005) and in ACC compared to non-RC BD-I patients (P<0.05). These results suggest greater alteration of membrane phospholipid metabolisms in rapid cycling BD-I compared to non-rapid-cycling BD-I.

Cortical gyrification of the brain represents the folding characteristic of the cerebral cortex. How the brain cortical gyrification changes from childhood to old age in healthy human subjects is still unclear. Additionally, studies have shown regional gyrification alterations in patients with major psychiatric disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). However, whether the lifespan trajectory of gyrification over the brain is altered in patients diagnosed with major psychiatric disorders is still unknown. In this study, we investigated the trajectories of gyrification in three independent cohorts based on structural brain images of 881 subjects from age 4 to 83. We discovered that the trajectory of gyrification during normal development and aging was not linear and could be modeled with a logarithmic function. We also found that the gyrification trajectories of patients with MDD, BD and SCZ were deviated from the healthy one during adulthood, indicating altered aging in the brain of these patients.

Previous studies have reported abnormalities of white-matter diffusivity in pediatric bipolar disorder. However, it has not been established whether these abnormalities are able to distinguish individual subjects with pediatric bipolar disorder from healthy controls with a high specificity and sensitivity. Diffusion-weighted imaging scans were acquired from 16 youths diagnosed with DSM-IV bipolar disorder and 16 demographically matched healthy controls. Regional white matter tissue microstructural measurements such as fractional anisotropy, axial diffusivity and radial diffusivity were computed using an atlas-based approach. These measurements were used to 'train' a support vector machine (SVM) algorithm to predict new or 'unseen' subjects' diagnostic labels. The SVM algorithm predicted individual subjects with specificity=87.5%, sensitivity=68.75%, accuracy=78.12%, positive predictive value=84.62%, negative predictive value=73.68%, area under receiver operating characteristic curve (AUROC)=0.7812 and chi-square p-value=0.0012. A pattern of reduced regional white matter fractional anisotropy was observed in pediatric bipolar disorder patients. These results suggest that atlas-based diffusion weighted imaging measurements can distinguish individual pediatric bipolar disorder patients from healthy controls. Notably, from a clinical perspective these findings will contribute to the pathophysiological understanding of pediatric bipolar disorder., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Background: Major psychiatric disorders are increasingly being conceptualized as 'neurodevelopmental', because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of neuroanatomical measurements may reliably identify individual subjects deviating from a normative neurodevelopmental trajectory. However, while recent studies have shown great promise in developing accurate brain maturation indices using neuroimaging data and multivariate machine learning techniques, this approach has not been validated using a large sample of longitudinal data from children and adolescents., Methods: T1-weighted scans from 303 healthy subjects aged 4.88 to 18.35years were acquired from the National Institute of Health (NIH) pediatric repository (http://www.pediatricmri.nih.gov). Out of the 303 subjects, 115 subjects were re-scanned after 2years. The least absolute shrinkage and selection operator algorithm (LASSO) was 'trained' to integrate neuroanatomical changes across chronological age and predict each individual's brain maturity. The resulting brain maturation index was developed using first-visit scans only, and was validated using second-visit scans., Results: We report a high correlation between the first-visit chronological age and brain maturation index (r=0.82, mean absolute error or MAE=1.69years), and a high correlation between the second-visit chronological age and brain maturation index (r=0.83, MAE=1.71years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27years., Conclusions: The brain maturation index developed in this study accurately predicted individual subjects' brain maturation longitudinally. Due to its strong clinical potentials in identifying individuals with an abnormal brain maturation trajectory, the brain maturation index may allow timely clinical interventions for individuals at risk for psychiatric disorders., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Diagnosis of pediatric neuropsychiatric disorders such as unipolar depression is largely based on clinical judgment - without objective biomarkers to guide diagnostic process and subsequent therapeutic interventions. Neuroimaging studies have previously reported average group-level neuroanatomical differences between patients with pediatric unipolar depression and healthy controls. In the present study, we investigated the utility of multiple neuromorphometric indices in distinguishing pediatric unipolar depression patients from healthy controls at an individual subject level., Methods: We acquired structural T1-weighted scans from 25 pediatric unipolar depression patients and 26 demographically matched healthy controls. Multiple neuromorphometric indices such as cortical thickness, volume, and cortical folding patterns were obtained. A support vector machine pattern classification model was 'trained' to distinguish individual subjects with pediatric unipolar depression from healthy controls based on multiple neuromorphometric indices and model predictive validity (sensitivity and specificity) calculated., Results: The model correctly identified 40 out of 51 subjects translating to 78.4% accuracy, 76.0% sensitivity and 80.8% specificity, chi-square p-value = 0.000049. Volumetric and cortical folding abnormalities in the right thalamus and right temporal pole respectively were most central in distinguishing individual patients with pediatric unipolar depression from healthy controls., Conclusions: These findings provide evidence that a support vector machine pattern classification model using multiple neuromorphometric indices may qualify as diagnostic marker for pediatric unipolar depression. In addition, our results identified the most relevant neuromorphometric features in distinguishing PUD patients from healthy controls., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism has been proposed as a possible candidate for involvement in the pathophysiology of bipolar disorder (BD). To determine whether an association exists between the BDNF Val66Met genotype and morphometric abnormalities of the brain regions involved in memory and learning in BD and healthy subjects. Forty-two BD patients and 42 healthy subjects were studied. Interactions between BDNF Val66Met genotype and diagnosis in gray (GM) volumes were analyzed using an optimized voxel-based morphometry technique. Declarative memory function was assessed with the California Verbal Learning Test II. Left and right anterior cingulate GM volumes showed a significant interaction between genotype and diagnosis such that anterior cingulate GM volumes were significantly smaller in the Val/Met BD patients compared with the Val/Val BD patients (left P=0.01, right P=0.01). Within-group comparisons revealed that the Val/Met carriers showed smaller GM volumes of the dorsolateral prefrontal cortex compared with the Val/Val subjects within the BD patient (P=0.01) and healthy groups (left P=0.03, right P=0.03). The Val/Met healthy subjects had smaller GM volumes of the left hippocampus compared with the Val/Val healthy subjects (P<0.01). There was a significant main effect of diagnosis on memory function (P=0.04), but no interaction between diagnosis and genotype was found (P=0.48). The findings support an association between the BDNF Val66Met genotype and differential gray matter content in brain structures, and suggest that the variation in this gene may play a more prominent role in brain structure differences in subjects affected with BD.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

In proton magnetic resonance spectroscopy (¹H MRS) studies, aberrant levels of choline-containing compounds that include glycerophosphocholine plus phosphocholine (GPC+PC), can signify alterations in the metabolism of cellular membrane phospholipids (MPLs) from a healthy baseline. In a recent ¹H MRS study, we reported increased GPC+PC in cortical and subcortical areas of adult patients with bipolar disorder I (BP-I). Post-traumatic stress disorder (PTSD) can worsen the severity of BP-I, but it is unclear whether the effect of a PTSD comorbidity in BP-I is associated with altered MPL metabolism. The purpose of this study was to re-investigate the ¹H MRS data to determine whether the regional extent of elevated GPC+PC was greater in BP-I patients with PTSD (BP-I/wPTSD) compared to BP-I without comorbid PTSD (BP-I/woPTSD) patients and healthy controls. GPC+PC levels from four brain areas [the anterior cingulate cortex (ACC), anterior-dorsal ACC, caudate, and putamen] were measured in 14 BP-I/wPTSD, 36 BP-I/woPTSD, and 44 healthy controls using a multi-voxel 1H MRS approach on a 3 Tesla system with high spatial resolution and absolute quantification. Results show a significant increase in GPC+PC levels from the caudate and putamen of BP-I/wPTSD patients compared to healthy controls (P<0.05) and in the putamen compared to BP-I/woPTSD patients (P<0.05). These findings are consistent with evidence of elevated degradation of MPLs in the neuropil that is more pronounced in BP-I patients with comorbid PTSD. [ABSTRACT FROM AUTHOR]