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Left–right asymmetry of the human brain is one of its cardinal features, and also a complex, multivariate trait. Decades of research have suggested that brain asymmetry may be altered in psychiatric disorders. However, findings have been inconsistent and often based on small sample sizes. There are also open questions surrounding which structures are asymmetrical on average in the healthy population, and how variability in brain asymmetry relates to basic biological variables such as age and sex. Over the last 4 years, the ENIGMA‐Laterality Working Group has published six studies of gray matter morphological asymmetry based on total sample sizes from roughly 3,500 to 17,000 individuals, which were between one and two orders of magnitude larger than those published in previous decades. A population‐level mapping of average asymmetry was achieved, including an intriguing fronto‐occipital gradient of cortical thickness asymmetry in healthy brains. ENIGMA's multi‐dataset approach also supported an empirical illustration of reproducibility of hemispheric differences across datasets. Effect sizes were estimated for gray matter asymmetry based on large, international, samples in relation to age, sex, handedness, and brain volume, as well as for three psychiatric disorders: autism spectrum disorder was associated with subtly reduced asymmetry of cortical thickness at regions spread widely over the cortex; pediatric obsessive–compulsive disorder was associated with altered subcortical asymmetry; major depressive disorder was not significantly associated with changes of asymmetry. Ongoing studies are examining brain asymmetry in other disorders. Moreover, a groundwork has been laid for possibly identifying shared genetic contributions to brain asymmetry and disorders. Left–right asymmetry of the human brain is one of its cardinal features, and also a complex, multivariate trait. Over the last four years, the ENIGMA‐Laterality Working Group has published six studies of grey matter morphological asymmetry in health and disease, based on total sample sizes from roughly 3,500 to 17,000 individuals, which were between one and two orders of magnitude larger than those published in previous decades. Here we review the findings from these six studies.

Here we review the motivation for creating the enhancing neuroimaging genetics through meta‐analysis (ENIGMA) Consortium and the genetic analyses undertaken by the consortium so far. We discuss the methodological challenges, findings, and future directions of the genetics working group. A major goal of the working group is tackling the reproducibility crisis affecting “candidate gene” and genome‐wide association analyses in neuroimaging. To address this, we developed harmonized analytic methods, and support their use in coordinated analyses across sites worldwide, which also makes it possible to understand heterogeneity in results across sites. These efforts have resulted in the identification of hundreds of common genomic loci robustly associated with brain structure. We have found both pleiotropic and specific genetic effects associated with brain structures, as well as genetic correlations with psychiatric and neurological diseases. Improvement in the polygenic score prediction of hippocampal volume, as power in the discovery GWAS increases. PRS may be thought of as weighted‐sum scores that summarize the results of the GWAS to a given level of significance, these results show the increased explanatory power of the GWAS for hipocampal volume as sample size increases.

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer’s disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer’s treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer’s Disease Neuroimaging Initiative. We evaluated each model’s balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high‐powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site‐related image variation. However, most statistical approaches may over‐correct for technical, scanning‐related, variation as they cannot distinguish between confounded image‐acquisition based variability and site‐related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition‐based variability. To overcome this limitation, we consider site‐related magnetic resonance (MR) imaging harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep‐learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labels a priori. We trained our model using data from five large‐scale multisite datasets with varied demographics. Results demonstrated that our style‐encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain‐age estimates, and case–control effect sizes before and after the harmonization. We showed that our harmonization removed the site‐related variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising tool for ongoing collaborative studies. Source code is released in USC‐IGC/style_transfer_harmonization (github.com). We develop a novel harmonization approach for T1‐weighted magnetic resonance imaging using a style‐encoding generative adversarial network that can be used to harmonize entire images for a variety of international, multi‐cohort, neuroimaging collaborations. Results demonstrated that this model avoids the need to control for clinical or demographic information. We showed that our harmonization removed the cross‐site variances, while preserving the anatomical information and clinical meaningful patterns.

Sex is a biological variable that contributes to individual variability in brain structure and behavior. Neuroimaging studies of population‐based samples have identified normative differences in brain structure between males and females, many of which are exacerbated in psychiatric and neurological conditions. Still, sex differences in MRI outcomes are understudied, particularly in clinical samples with known sex differences in disease risk, prevalence, and expression of clinical symptoms. Here we review the existing literature on sex differences in adult brain structure in normative samples and in 14 distinct psychiatric and neurological disorders. We discuss commonalities and sources of variance in study designs, analysis procedures, disease subtype effects, and the impact of these factors on MRI interpretation. Lastly, we identify key problems in the neuroimaging literature on sex differences and offer potential recommendations to address current barriers and optimize rigor and reproducibility. In particular, we emphasize the importance of large‐scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta‐Analyses consortium, the UK Biobank, Human Connectome Project, and others to provide unprecedented power to evaluate sex‐specific phenotypes in major brain diseases. Here, we review the human neuroimaging literature examining sex effects on adult brain structure using structural and diffusion MRI. We discuss normative sex differences based on population‐based studies as well as sex differences in 14 major brain diseases. Finally, we identify key barriers to advancing the science on neuroimaging sex effects and offer recommendations to mitigate these challenges, particularly through large‐scale neuroimaging.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large‐scale studies. In response, we used cross‐sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to infer age‐related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta‐analysis and one‐way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes. We used cross‐sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to infer age‐related changes in cortical thickness.

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy‐to‐use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test–retest data from three public data sets to determine within‐version reliability and between‐version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37–0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter‐version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version‐related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/. We use test–retest data from three public data sets to determine within‐version reliability and between‐version compatibility across 42 regional outputs from FreeSurfer version 7.1, 6.0, and 5.3. Overall, we find generally high within‐version reliability across most versions, however, considerable differences are observed when analyzing between‐version compatibility for regional cortical thickness, surface area, and subcortical volumes.

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. This Special Issue of Human Brain Mapping is dedicated to a 10‐year anniversary of the international Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium. It reports updates from abroad range of international neuroimaging projects that pool data from over 45 countries to answer fundamental questions in neuroscience. Here, we summarize the 31 papers in this Special Issue from across the ENIGMA Consortium.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer’s disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau 181 and Aβ 1–42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer’s Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ 1–42 and higher pTau 181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ 1–42 than pTau 181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.