Schizophrenia Polygenic Risk During Typical Development Reflects Multiscale Cortical Organization

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

ObjectivesDental caries is one of the most prevalent oral diseases and causes of tooth loss. Cross-sectional studies observed epidemiological associations between dental caries and brain degeneration disorders, while it is unknown whether dental caries causally affect the cerebral structures. This study tested whether genetically proxied DMFS (the sum of Decayed, Missing, and Filled tooth Surfaces) causally impacts the brain cortical structure using Mendelian randomization (MR).MethodsThe summary-level GWAS meta-analysis data from the GLIDE consortium were used for DMFS, including 26,792 participants. ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) consortium GWAS summary data of 51,665 patients were used for brain structure. This study estimated the causal effects of DMFS on the surface area (SA) and thickness (TH) of the global cortex and functional cortical regions accessed by magnetic resonance imaging (MRI). Inverse-variance weighted (IVW) was used as the primary estimate, the MR pleiotropy residual sum and outlier (MR-PRESSO), the MR-Egger intercept test, and leave-one-out analyses were used to examine the potential horizontal pleiotropy.ResultsGenetically proxied DMFS decreases the TH of the banks of the superior temporal sulcus (BANSSTS) with or without global weighted (weighted, β = − 0.0277 mm, 95% CI: − 0.0470 mm to − 0.0085 mm, P = 0.0047; unweighted, β = − 0.0311 mm, 95% CI: − 0.0609 mm to − 0.0012 mm, P = 0.0412). The causal associations were robust in various sensitivity analyses.ConclusionsDental caries causally decrease the cerebral cortical thickness of the BANKSSTS, a cerebral cortical region crucial for language-related functions, and is the most affected brain region in Alzheimer’s disease. This investigation provides the first evidence that dental caries causally affects brain structure, proving the existence of teeth-brain axes. This study also suggested that clinicians should highlight the causal effects of dental caries on brain disorders during the diagnosis and treatments, the cortical thickness of BANKSSTS is a promising diagnostic measurement for dental caries-related brain degeneration.

The effect of polygenic risk scores for major depressive disorder, bipolar disorder and schizophrenia on morphological brain measures: A systematic review of the evidence

Externalizing and internalizing disorders are common in youth but are often studied separately, preventing researchers from identifying shared (i.e., transdiagnostic) alterations in brain structure. Using data from the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium, we conducted a mega-analysis to identify shared and distinct cortical and subcortical brain alterations across internalizing (anxiety disorders and depression) and externalizing (attention-deficit/hyperactivity disorder [ADHD] and conduct disorder [CD]) disorders in youth. 3D T1-weighted magnetic resonance imaging data from youths (age range 4-21 years) with anxiety disorders (n = 1044), depression (n = 504), ADHD (n = 1317), and CD (n = 1172) along with healthy control participants (n = 4743) were analyzed. We assessed group differences in regional cortical thickness, surface area (SA), and subcortical volume using linear models, adjusted for site, age, and sex, as well as total intracranial volume in the SA and subcortical volume models. We observed transdiagnostic associations, with both internalizing and externalizing disorders characterized by lower SA in the insula, entorhinal cortex, and middle temporal gyrus and lower amygdala volume (Cohen's ds = -0.07 to -0.24) as well as total SA and intracranial volume (ds = -0.11 to -0.25). Externalizing-specific reductions in SA were observed in frontoparietal regions (ds = -0.08 to -0.13), but no internalizing-specific associations were identified. Disorder-specific alterations were identified for ADHD, CD, and anxiety disorders but not depression. Both common and disorder-specific alterations were identified, with regions involved in salience attribution and emotion processing implicated across internalizing and externalizing disorders. These novel findings can guide future research targeting common biological processes across youth psychiatric disorders as well as features unique to individual disorders.

BackgroundGlobal scale brain research collaborations such as the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium are beginning to collect data in large quantity and to conduct meta-analyses using uniformed protocols. It becomes strategically important that the results can be communicated among brain scientists effectively. Traditional graphs and charts failed to convey the complex shapes of brain structures which are essential to the understanding of the result statistics from the analyses. These problems could be addressed using interactive visualization strategies that can link those statistics with brain structures in order to provide a better interface to understand brain research results.ResultsWe present ENIGMA-Viewer, an interactive web-based visualization tool for brain scientists to compare statistics such as effect sizes from meta-analysis results on standardized ROIs (regions-of-interest) across multiple studies. The tool incorporates visualization design principles such as focus+context and visual data fusion to enable users to better understand the statistics on brain structures. To demonstrate the usability of the tool, three examples using recent research data are discussed via case studies.ConclusionsENIGMA-Viewer supports presentations and communications of brain research results through effective visualization designs. By linking visualizations of both statistics and structures, users can gain more insights into the presented data that are otherwise difficult to obtain. ENIGMA-Viewer is an open-source tool, the source code and sample data are publicly accessible through the NITRC website (http://www.nitrc.org/projects/enigmaviewer_20). The tool can also be directly accessed online (http://enigma-viewer.org).

Response to Dr Fried & Dr Kievit, and Dr Malhi et al.

This Special Issue of Human Brain Mapping is dedicated to a 10‐year anniversary of the Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large‐scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task‐based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well‐powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large‐scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke.

We present ENIGMA-Viewer, an interactive visualization tool for scientists to compare effective sizes and association results on standardized regions of interest from different studies, as performed in large scale consortia studies such as those in the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium. We report how visualization methods are designed for interactive comparison of brain diffusion tensor imaging (DTI) and cortical thickness statistical test results. ENIGMA-viewer is an open-source software tool, publicly accessible through the NITRC website or online at http://enigma-viewer.org.

Brain network architecture is anticipated to influence future grey matter loss in individuals at Clinical High Risk (CHR) for psychosis. However, existing studies on grey matter structural network properties in CHR are scarce and constrained by small sample sizes. Here, we examined network topology differences comparing a) CHR versus healthy controls (HC); b) CHR who transitioned to psychosis (CHR-T) versus those who did not (CHR-NT); and c) different subsyndromes. We included structural scans from 1842 CHR individuals and 1417 HC individuals from 31 sites within the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. At the global level, CHR individuals exhibited lower structural covariance (q < 0.001; Cohen's d = 0.164) and less optimal structural network configuration than HC (lower global efficiency and clustering coefficient, d = 0.100,0.087, qs <= 0.027). Though no global difference between CHR-T and CHR-NT, network distinctiveness of the frontal and temporal surface area networks was higher in CHR-T than CHR-NT (d = 0.223,0.237) and HC (d = 0.208,0.219) (qs < 0.001). Network distinctiveness of the frontal cortical thickness network was lower in CHR-T (d = 0.218, q < 0.001) than CHR-NT and HC (d = 0.165, q < 0.001). Importantly, higher network distinctiveness was associated with worse positive symptoms in CHR-NT (frontal surface area, q = 0.008, R2 = 0.013) and at trend with worse negative symptoms in CHR-T (frontal thickness, q = 0.063, R2 = 0.049). Further, the brief intermittent psychotic syndrome subgroup showed more severe network alterations. Together, brain structural networks inform symptoms and the risk of transition to psychosis in CHR individuals.

Studies investigating social anxiety disorder (SAD) have reported inconsistent alterations in white matter (WM) microstructure. The ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Anxiety Working Group investigated differences in the microstructure of 25 WM tracts between individuals with SAD and healthy control (HC) participants in a mega-analysis. We analyzed data from 487 individuals with SAD and 1604 HC participants (ages 8-65 years) from 12 cohorts worldwide. Analyses and quality control were performed using standardized ENIGMA diffusion tensor imaging protocols. We primarily examined fractional anisotropy (FA) as the main parameter of WM microstructure. Linear mixed-effects analyses were conducted to compare individuals with SAD with HC participants in the total sample. Next, adult (age >21) and adolescent (age ≤21) samples were analyzed separately. In sensitivity analyses, additional effects of sex, medication, symptom severity, and comorbid psychiatric disorders were investigated. In the total sample, individuals with SAD showed lower FA in several tracts, including the corpus callosum and fornix, compared with HC participants. Widespread sex × diagnosis interactions were observed, mostly driven by lower FA in females with SAD. Adults with SAD showed lower FA in multiple tracts, while age × diagnosis interactions were observed in adolescents. Using a mega-analytic approach, several differences in WM microstructure were found between individuals with SAD and HC participants, both in the full sample and in age group-specific sensitivity analyses. Some neurobiological changes in WM tracts in individuals with SAD may vary with age and sex, whereas others may relate to broader transdiagnostic neurobiological features underlying psychopathology. Further research should investigate these issues in more detail.

T1-weighted (T1-w) anatomical magnetic resonance imaging (MRI) enable us to identify injuries (e.g., extent, location, type of lesion, size etc.) within the brains of individuals with moderate-to-severe traumatic brain injury (ms-TBI). Lesion segmentation is a key step prior to running advanced neuroimaging analyses (such as connectomics, tractography); however, to date, no automated lesion segmentation tools have been developed for T1-w images of patients with ms-TBI. To find a solution to this, we established the Automated Identification of Moderate-to-Severe TBI (AIMS-TBI) challenge for MICCAI 2024. For this challenge, the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Brain Injury working group collated 1100 T1-w scans from individuals with ms-TBI across 12 sites. Data from 764 individuals underwent manual lesion segmentation using a team of 12 manual raters. For these analyses, 388 images were used in the training dataset, 101 for validation, and 275 for the final test set. During the validation phase, 12 submissions were received, and five teams submitted the final test set. To identify the algorithm that generated the most accurate lesion segmentation from the teams, four metrics were computed, including Dice score, absolute volume difference, absolute lesion count difference, and the lesion-wise F1 score. Scores were calculated for each image, and then averaged, with teams ranked according to best cumulative performance across all metrics.

Polygenic Scores Derived From Neuroimaging Endophenotypes Predict Outcomes To Psychotherapy And Medication Treatments For Major Depressive Disorder

The underlying pathologies of psychiatric disorders, which cause substantial personal and social losses, remain unknown, and their elucidation is an urgent issue. To clarify the core pathological mechanisms underlying psychiatric disorders, in addition to laboratory‐based research that incorporates the latest findings, it is necessary to conduct large‐sample‐size research and verify reproducibility. For this purpose, it is critical to conduct multicenter collaborative research across various fields, such as psychiatry, neuroscience, molecular biology, genomics, neuroimaging, cognitive science, neurophysiology, psychology, and pharmacology. Moreover, collaborative research plays an important role in the development of young researchers. In this respect, the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) consortium and Cognitive Genetics Collaborative Research Organization (COCORO) have played important roles. In this review, we first overview the importance of multicenter collaborative research and our target psychiatric disorders. Then, we introduce research findings on the pathophysiology of psychiatric disorders from neurocognitive, neurophysiological, neuroimaging, genetic, and basic neuroscience perspectives, focusing mainly on the findings obtained by COCORO. It is our hope that multicenter collaborative research will contribute to the elucidation of the pathological basis of psychiatric disorders.

A - 21 Sex Differences in Cortical Thickness and Cognition in Individuals with Autosomal Dominant Alzheimer's Disease.

INCREASED CORTICAL THICKNESS IN LATE-LIFE DEPRESSION AFTER ANTI-DEPRESSANT TREATMENT WITH LEVOMILNACIPRAN