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MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness. This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

Abstract Background Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms. Design Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms. Results LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores. Conclusions This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.

While alterations in rest-activity rhythms are common in older adults \"at risk\" for dementia, it is unclear how rest-activity rhythms relate to underlying brain integrity. Older adults aged ≥50 years (n = 143, mean age = 67) with subjective and/or objective cognitive impairment underwent magnetic resonance imaging scanning and 14 days of actigraphy. The following nonparametric measures were computed: intra-daily variability (IV), inter-daily stability (IS), relative amplitude (RA), and average activity during the least active 5-h period (L5). A vertex-wise analysis correcting for age, sex, and clinical variables examined the association between nonparametric actigraphy measures and cortical thickness. When controlling for age, sex, and body mass index (BMI), lower IV was associated with greater cortical thickness in the right cuneus (cluster-wise p-values [CWP] < 0.001), left middle frontal gyrus (CWP < 0.001), and lateral orbital frontal cortex (CWP = 0.004). When controlling for age, sex, medical burden (CIRS-G), BMI, and antidepressant use, lower IS was associated with lower cortical thickness in the left (CWP = 0.002) and right superior frontal gyrus (CWP < 0.001), left superior temporal gyrus (CWP = 0.043), and left post-central gyrus (CWP = 0.033). There were no significant associations between RA or L5 and cortical thickness. In older adults \"at risk\" for dementia, variability and stability of rest-activity rhythms were associated with reduced cortical thickness in frontal, temporal, parietal, and occipital regions. Further studies could focus on determining the prognostic utility of such markers longitudinally.

Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age. Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer. CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions. Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.

Cortical thickness reductions associated with chronic methamphetamine use exhibit a non-uniform spatial distribution across brain regions. A potential neurobiological mechanism underlying for this heterogeneous pattern may involve the structural and functional organization of cortical connectivity networks, which could mediate the propagation of neuroanatomical alterations. Here, we aimed to explore how brain network architecture constrains cortical thickness alterations and their clinical relevance. The 3D-T1 images were acquired from 139 patients with methamphetamine use disorder (MUD) and 119 sex- and age-matched healthy controls. We first characterized distributed cortical thinning patterns in patients with MUD, then evaluated the relationships between regional atrophy and (1) multimodal nodal centrality measures (structural, morphological, and functional) and (2) atrophy profiles of structural connected neighbors. Individual network-weighted cortical abnormality maps were used to identify distinct MUD biotypes and related to clinical features through k-means clustering and partial least squares regression. Cortical thinning patterns demonstrated significant associations with nodal centrality across all modalities, as well as cortical thinning of connected neighbors revealing a network-dependent atrophy architecture. Fronto-temporal regions emerged as critical epicenters, showing both high nodal centrality and strong correlations with connected neighbors' thinning severity. We found that the individual differences in network-weighted cortical abnormality corresponded to clinical symptom variability, and distinguished two MUD biotypes associated with drug use. Our findings suggest that cortical thinning in MUD is influenced by the brain connectome architecture, providing a mechanistic framework for understanding individual variability in addiction progression.

Neuroimaging is vital in quantifying brain atrophy due to typical aging and due to neurodegenerative diseases. To collect large samples necessary to model lifespan brain development, research consortiums aggregate images acquired across multiple study sites. Previous studies have demonstrated that this multi‐site study design can lead to site‐related bias, necessitating harmonization of these “site effects.” However, current methodologies are unable to generalize to new sites outside the original harmonized sample, limiting translation to new sites or clinical practice. Here, we propose a method called ComBat‐Predict (CB‐Predict) building upon the ComBat method for site effect adjustment, which extends to data from a new site with smaller sample sizes and unknown site effects. In data from the Alzheimer's Disease Neuroimaging Initiative, our proposed method mitigates bias and yields high accuracy in predicting cortical thickness measures when generalizing the model to new data. Furthermore, we demonstrate that our proposed harmonization method can reduce site‐related variance in centile scores estimated using data from the Lifespan Brain Chart Consortium. Altogether, our results demonstrate that CB‐Predict effectively harmonizes new sites and thereby enables effective translation of neuroimaging models to additional samples. ComBat‐Predict effectively harmonizes data from a new site, improving the applicability of normative models to private data. ComBat‐Predict is available for R in the ComBatFamily package.

Cortical thickness measurements from MRI are increasingly used as biomarkers for neurodegenerative disease progression. However, variations in MRI acquisition parameters, such as inversion time (TI) and repetition time (TR), which are common in clinical settings, can compromise the reliability and sensitivity of these measurements. We fine‐tuned a deep‐learning‐based segmentation tool (DL+DiReCT) to reduce its dependence to image contrast variations by training it on simulated MPRAGE images derived from quantitative relaxation maps. Fine‐tuning markedly reduced contrast sensitivity, with the Pearson correlation coefficient decreasing from −0.644 -0.644 to 0.094 0.094 . Evaluation on a synthetic atrophy dataset demonstrated that our model accurately replicated atrophy trends with minimal underestimation, outperforming FreeSurfer and SynthSeg. When applied to a dataset of relapsing–remitting multiple sclerosis (RRMS) patients, the fine‐tuned model showed a substantial reduction in contrast sensitivity and maintained stable performance after controlling for covariates such as age, sex, field strength, and Expanded Disability Status Scale (EDSS) score. Overall, the proposed approach achieves robust contrast invariance without sacrificing sensitivity to cortical atrophy, offering a practical improvement for longitudinal and multi‐center clinical studies. Key Points Changes in MR acquisition settings, specifically of TI and TR, which are common in the clinical setting, affect WM/GM contrast, which in turn affects cortical thickness measurements. Deep learning models can be fine‐tuned on synthetic MRI simulation across different contrasts for more robust cortical thickness measurements. Left: Our finetuned model reduces dependence on image contrast significantly (top), while conserving sensitivity to synthetic atrophy (bottom). Right: For the finetuning procedure, MRIs with varying grey‐white contrast were synthesized using Deichmann's equations on T1 and PD maps. The segmentation ground truth for all synthetic images was generated by running FreeSurfer on the MRI with highest image contrast. Cortical thickness estimated with various methods was finally compared.

Lower midlife physical activity is associated with higher risk of neurodegenerative disease in late life. However, it remains unknown whether physical exercise and fitness are associated with brain structural integrity during midlife. The purpose of this study was to compare brain structures between middle-aged aerobically trained adults (MA), middle-aged sedentary (MS), and young sedentary (YS) adults. Thirty MA (54±4 years), 30 MS (54±4 years), and 30 YS (32±6 years) participants (50% women) underwent measurements of brain volume, cortical thickness, and white matter (WM) fiber integrity using MRI. MA participants had aerobic training for 24.8±9.6 years and the highest cardiorespiratory fitness level (i.e., peak oxygen uptake: VO2peak) among all groups. Global WM integrity, as assessed with fractional anisotropy (FA) from diffusion tensor imaging, was lower in the MS compared with the YS group. However, global FA in the MA group was significantly higher than that in the MS group (P<0.05) and at a similar level to the YS group. Furthermore, tract-based spatial statistical analysis demonstrated that FA in the anterior, superior, and limbic WM tracts (e.g., the genu of the corpus callosum, superior longitudinal fasciculus, uncinate fasciculus) was higher in the MA compared with MS groups, and positively associated with VO2peak, independently from age and sex. From cortical thickness analysis, MS and MA participants showed thinner prefrontal and parieto-temporal areas than the YS group. On the other hand, the MA group exhibited thicker precentral, postcentral, pericalcarine, and lateral occipital cortices than the MS and YS groups. But, the insula and right superior frontal gyrus showed thinner cortical thickness in the MA compared with the MS groups. Collectively, these findings suggest that midlife aerobic exercise is associated with higher WM integrity and greater primary motor and somatosensory cortical thickness.

Understanding the evolution of negative symptoms in first-episode psychosis (FEP) requires long-term longitudinal study designs that capture the progression of this condition and the associated brain changes. To explore the factors underlying negative symptoms and their association with long-term abnormal brain trajectories. We followed up 357 people with FEP over a 10-year period. Factor analyses were conducted to explore negative symptom dimensionality. Latent growth mixture modelling (LGMM) was used to identify the latent classes. Analysis of variance (ANOVA) was conducted to investigate developmental trajectories of cortical thickness. Finally, the resulting ANOVA maps were correlated with a wide set of regional molecular profiles derived from public databases. Three trajectories (stable, decreasing and increasing) were found in each of the three factors (expressivity, experiential and attention) identified by the factor analyses. Patients with an increasing trajectory in the expressivity factor showed cortical thinning in caudal middle frontal, pars triangularis, rostral middle frontal and superior frontal regions from the third to the tenth year after the onset of the psychotic disorder. The -statistic map of cortical thickness expressivity differences was associated with a receptor density map derived from positron emission tomography data. Stable and decreasing were the most common trajectories. Additionally, cortical thickness abnormalities found at relatively late stages of FEP onset could be exploited as a biomarker of poor symptom outcome in the expressivity dimension. Finally, the brain areas with less density of receptors spatially overlap areas that discriminate the trajectories of the expressivity dimension.

There is a growing body of evidence based on adult neuroimaging that suggests that the brain adapts to bilingual experiences to support language proficiency. The Adolescent Brain Cognitive Development (ABCD) Study is a useful source of data for evaluating this claim during childhood, as it involves data from a large sample of American children. Using the baseline ABCD Study data collected at ages nine and ten, the goal of this study was to identify differences in cortical thickness between bilinguals and monolinguals and to evaluate how variability in English vocabulary and English use within bilinguals might explain these group differences. We identified bilingual participants as children who spoke a non-English language and were exposed to the non-English language at home. We then identified a matched sample of English monolingual participants based on age, sex, pubertal status, parent education, household income, non-verbal IQ, and handedness. Bilinguals had thinner cortex than monolinguals in widespread cortical regions. Within bilinguals, more English use was associated with greater frontal and parietal cortical thickness; greater English vocabulary was associated with greater frontal and temporal cortical thickness. These findings replicate and extend previous research with bilingual children and highlight unexplained cortical thickness differences between bilinguals and monolinguals.