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Objective: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross‐sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE‐HS) correlate with clinical features. Methods: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1‐weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA‐Epilepsy consortium, comprising 804 people with MTLE‐HS and 1625 healthy controls from 25 centers. Features with a moderate case–control effect size (Cohen d ≥ .5) were used to train an event‐based model (EBM), which estimates a sequence of disease‐specific biomarker changes from cross‐sectional data and assigns a biomarker‐based fine‐grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. Results: In MTLE‐HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10−16), age at onset (ρ = −.18, p = 9.82 × 10−7), and ASM resistance (area under the curve = .59, p = .043, Mann–Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE‐HS with mild or nondetectable abnormality on T1W MRI. Significance: From cross‐sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE‐HS subjects in other cohorts and help establish connections between imaging‐based progression staging and clinical features.

The amygdala is widely implicated in both anxiety and autism spectrum disorder. However, no studies have investigated the relationship between co-occurring anxiety and longitudinal amygdala development in autism. Here, the authors characterize amygdala development across childhood in autistic children with and without traditional DSM forms of anxiety and anxieties distinctly related to autism.Longitudinal magnetic resonance imaging scans were acquired at up to four time points for 71 autistic and 55 typically developing (TD) children (∼2.5-12 years, 411 time points). Traditional DSM anxiety and anxieties distinctly related to autism were assessed at study time 4 (∼8-12 years) using a diagnostic interview tailored to autism: the Anxiety Disorders Interview Schedule-IV with the Autism Spectrum Addendum. Mixed-effects models were used to test group differences at study time 1 (3.18 years) and time 4 (11.36 years) and developmental differences (age-by-group interactions) in right and left amygdala volume between autistic children with and without DSM or autism-distinct anxieties and TD children.Autistic children with DSM anxiety had significantly larger right amygdala volumes than TD children at both study time 1 (5.10% increase) and time 4 (6.11% increase). Autistic children with autism-distinct anxieties had significantly slower right amygdala growth than TD, autism-no anxiety, and autism-DSM anxiety groups and smaller right amygdala volumes at time 4 than the autism-no anxiety (-8.13% decrease) and autism-DSM anxiety (-12.05% decrease) groups.Disparate amygdala volumes and developmental trajectories between DSM and autism-distinct forms of anxiety suggest different biological underpinnings for these common, co-occurring conditions in autism.

In neuroimaging research, the utilization of multi-centric analyses is crucial for obtaining sufficient sample sizes and representative clinical populations. Data harmonization techniques are typically part of the pipeline in multi-centric studies to address systematic biases and ensure the comparability of the data. However, most multi-centric studies require centralized data, which may result in exposing individual patient information. This poses a significant challenge in data governance, leading to the implementation of regulations such as the GDPR and the CCPA, which attempt to address these concerns but also hinder data access for researchers. Federated learning offers a privacy-preserving alternative approach in machine learning, enabling models to be collaboratively trained on decentralized data without the need for data centralization or sharing.In this paper, we present Fed-ComBat, a federated framework for batch effect harmonization on decentralized data. Fed-ComBat extends existing centralized linear methods, such as ComBat and distributed as d-ComBat, and nonlinear approaches like ComBat-GAM in accounting for potentially nonlinear and multivariate covariate effects. By doing so, Fed-ComBat enables the preservation of nonlinear covariate effects without requiring centralization of data and without prior knowledge of which variables should be considered nonlinear or their interactions, differentiating it from ComBat-GAM. We assessed Fed-ComBat and existing approaches on simulated data and multiple cohorts comprising healthy controls (CN) and subjects with various disorders such as Parkinson’s disease (PD), Alzheimer’s disease (AD), and autism spectrum disorder (ASD).Results indicate that Fed-ComBat outperforms centralized ComBat in the presence of nonlinear effects and is comparable to centralized methods such as ComBat-GAM. Using synthetic data, Fed-ComBat is able to better reconstruct the target unbiased function by 35% (RMSE = 0.5952) with respect to d-ComBat (RMSE = 0.9162) and 12% with respect to our proposal to federate ComBat-GAM, d-ComBat-GAM (RMSE= 0.6751) and exhibits comparable results on MRI-derived phenotypes to centralized methods as ComBat-GAM without the need of prior knowledge on potential nonlinearities.

The role cardiac function plays in the predilection for, and progression of, Alzheimer’s disease (AD), is complex and unclear. While a number of low-frequency genetic variants of large effect-size have been shown to underlie both cardiovascular disease and dementia, recent studies have highlighted the importance of common genetic variants of small-effect size, which, in aggregate, are embodied by a polygenic risk score (PRS). In this study we aim to investigate the effect of polygenic risk for coronary artery disease (CAD) on brain atrophy in AD using whole brain volume (WBV) and put our findings in context with the polygenic risk for AD and presumed small vessel disease as quantified by white matter hyperintensities (WMH). We used 730 subjects from the ADNI database to investigate PRS effects (beyond APOE) on whole brain volumes, total and regional WMH and amyloid beta across diagnostic groups. In a subset of these subjects (N=602) we utilise longitudinal changes in whole brain volume over a maximum of 24 months using the boundary shift integral approach. Linear regression and linear mixed effects models were used to investigate the effect of WMH at baseline as well as AD-PRS and CAD-PRS on whole brain atrophy and whole brain atrophy acceleration, respectively. All genetic associations were examined under oligogenic (p=1e-5) and the more variant-inclusive polygenic (p=0.5) scenarios. Our results suggest no evidence for a link between PRS score and markers of AD pathology at baseline (when stratified by diagnostic group). However, both AD-PRS and CAD-PRS were associated with longitudinal decline in WBV (AD PRS t=3.3, PFDR=0.007 over 24 months in healthy controls) and surprisingly, under certain conditions WBV atrophy is statistically more correlated with cardiac PRS than AD PRS (CAD PRS t=2.1, PFDR=0.04 over 24 months in the MCI group). Further, in our regional analysis of WMH, AD PRS beyond APOE is predictive of white matter volume in the occipital lobe in AD subjects in the polygenic regime. Finally, the rate of change of brain volume (or atrophy acceleration) may be sensitive to AD polygenic risk beyond APOE in healthy individuals (t=2, p=0.04). For subjects with mild cognitive impairment (MCI), beyond APOE, a more inclusive polygenic risk score including more variants, shows CAD PRS to be more predictive of WBV atrophy, than an oligogenic approach including fewer larger effect size variants.

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of biomarkers. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials., Comment: Presents final results of the TADPOLE competition. 60 pages, 7 tables, 14 figures

Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ($$\hbox {MAE}=5.23$$ MAE = 5.23 ), Association ($$\hbox {MAE}=5.24$$ MAE = 5.24 ), and Projection ($$\hbox {MAE}=5.28$$ MAE = 5.28 ) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value $$< 5\hbox {E}{-}8$$ < 5 E - 8 ) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes.

Despite the increasing effort being devoted to the investigation of the link between imaging endophenotypes (IDPs) and genetic determinants (GDs) in Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), many issues remain open and deserve investigation. Among these, the role of functional connectivity (FC) is still blurred. This paper aims at shading some light on the topic relying on the ADNI repository (177 patients, out of which 82 MCI and 95 controls). The within/between-network connectivities were derived from individual FC matrices and used as IDPs. Conversely, the GDs consisted of two Polygenic Risk Scores (PRS) that have recently been proven to play a role in AD. A Partial Least Squares (PLS) model equipped with LASSO regularization was finally fitted to the data for associating IDPs and GDs. In the first component, all FC coefficients had the same sign, and were correlated with PRS2. Connectivities involving the dorsal attention (DAN) and frontoparietal control (CON) networks reached the highest weights, while within/between-network FC for the limbic (LIM) were less represented. Overall, the within-network FC values were less pronounced compared to the between-network ones. In the second component, most of the FC features had zero weights. Visual (VIS) and somatomotory (SMN) showed a correlated trend, while being anti-correlated with LIM, CON and default mode network as well as with PRS1. Our findings suggest that the two PRSs correlated with a possible pattern of aberrant within/between-network FC changes occurring in RSNs devoted to higher cognitive functions and more vulnerable in this pathology.

The brain’s functional connectome is dynamic, constantly reconfiguring in an individual-specific manner. However, which characteristics of such reconfigurations are subject to genetic effects, and to what extent, is largely unknown. Here, we identified heritable dynamic features, quantified their heritability, and determined their association with cognitive phenotypes. In resting-state fMRI, we obtained multivariate features, each describing a temporal or spatial characteristic of connectome dynamics jointly over a set of connectome states. We found strong evidence for heritability of temporal features, particularly fractional occupancy (FO) and transition probability (TP), describing the trajectory of state transitions. Genetic effects explained a substantial proportion of phenotypic variance of these features (h2=.39, 95% CI= [.24,.54] for FO; h2=.43, 95% CI=[.29,.57] for TP). Moreover, these temporal phenotypes were associated with cognitive performance. Contrarily, we found no robust evidence for heritability of spatial features of the dynamic states (i.e., states’ Modularity and connectivity pattern). Genetic effects may therefore primarily contribute to how the connectome transitions across states, rather than the precise spatial instantiation of the states in individuals. In sum, genetic effects impact the duration spent in each connectivity configuration and the frequency of shifting between configurations, and such temporal features may act as endophenotypes for cognitive abilities.

The epilepsies are now conceptualized as network disruptions: focal epilepsies are considered to have network alterations in the hemisphere of seizure onset, whilst generalized epilepsies are considered to have bi-hemispheric network changes. Increasingly, many epilepsies are also considered to be neurodevelopmental disorders, with early changes in the brain underpinning seizure biology. The development of the structure of the face is influenced by complex molecular interactions between surface ectoderm and underlying developing forebrain and neural crest cells. This influence is likely to continue postnatally, given the evidence of facial growth changes over time in humans until at least 18 years of age. In this case–control study, we hypothesized that people with lateralized focal epilepsies (i.e. unilateral network changes) have an increased degree of facial asymmetry, compared with people with generalized epilepsies or controls without epilepsy. We applied three-dimensional stereophotogrammetry and dense surface models to evaluate facial asymmetry in people with epilepsy, aiming to generate new tools to explore pathophysiological mechanisms in epilepsy. We analysed neuroimaging data to explore the correlation between face and brain asymmetry. We consecutively recruited 859 people with epilepsy attending the epilepsy clinics at a tertiary referral centre. We used dense surface modelling of the full face and signature analyses of three-dimensional facial photographs to analyse facial differences between 378 cases and 205 healthy controls. Neuroimaging around the time of the facial photograph was available for 234 cases. We computed the brain asymmetry index between contralateral regions. Cases with focal symptomatic epilepsy associated with unilateral lesions showed greater facial asymmetry compared to controls (P = 0.0001, two-sample t-test). This finding was confirmed by linear regression analysis after controlling for age and gender. We also found a significant correlation between duration of illness and the brain asymmetry index of total average cortical thickness (r = −0.19, P = 0.0075) but not for total average surface area (r = 0.06, P = 0.3968). There was no significant correlation between facial asymmetry and asymmetry of regional cortical thickness or surface area. We propose that the greater facial asymmetry in cases with focal epilepsy caused by unilateral abnormality might be explained by early unilateral network disruption, and that this is independent of underlying brain asymmetry. Three-dimensional stereophotogrammetry and dense surface modelling are a novel powerful phenotyping tool in epilepsy that may permit greater understanding of pathophysiology in epilepsy, and generate further insights into the development of cerebral networks underlying epilepsy, and the genetics of facial and neural development.

Structural and functional brain connectomics are considered a basis for an individual's behavior and cognition. Therefore, deviations from typical connectivity patterns may indicate disease processes, and can potentially serve as disease biomarkers. To date, the direct clinical application of brain connectivity measures for diagnostics or treatment is limited. Nonetheless, the extant literature on fundamental and clinical research applications reveals important advances in our understanding of typical and atypical brain structure and function. In this chapter we discuss the current status of the field regarding: (1) the impact of the connectome on cognitive processes and behavior, (2) the connectome across the lifespan, and (3) clinical research applications of connectomics. In addition, we highlight some limitations of connectomics for research and clinical translation.

KLOTHO∗VS heterozygosity (KL∗VSHET+) was recently shown to be associated with reduced risk of Alzheimer’s disease (AD) in APOE∗4 carriers. Additional studies suggest that KL∗VSHET+ protects against amyloid burden in cognitively normal older subjects, but sample sizes were too small to draw definitive conclusions. We performed a well-powered meta-analysis across 5 independent studies, comprising 3581 pre-clinical participants ages 60–80, to investigate whether KL∗VSHET+ reduces the risk of having an amyloid-positive positron emission tomography scan. Analyses were stratified by APOE∗4 status. KL∗VSHET+ reduced the risk of amyloid positivity in APOE∗4 carriers (odds ratio = 0.67 [0.52–0.88]; p = 3.5 × 10−3), but not in APOE∗4 non-carriers (odds ratio = 0.94 [0.73–1.21]; p = 0.63). The combination of APOE∗4 and KL∗VS genotypes should help enrich AD clinical trials for pre-symptomatic subjects at increased risk of developing amyloid aggregation and AD. KL-related pathways may help elucidate protective mechanisms against amyloid accumulation and merit exploration for novel AD drug targets. Future investigation of the biological mechanisms by which KL interacts with APOE∗4 and AD are warranted.

Aims\ud The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis.\ud \ud Methods\ud Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy.\ud \ud Results\ud We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia.\ud \ud Conclusions\ud These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels. In this study, we evaluate the utility of a pre-screening classification network prior to the segmentation network. Experimental results demonstrate that such a classifier, minimising frame classification errors, was able to directly impact the number of false positive and false negative frames. Importantly, the segmentation accuracy on the classifier-selected frames, that would be segmented, remains comparable to or better than those from standalone segmentation networks. Interestingly, the efficacy of the pre-screening classifier was affected by the sampling methods for training labels from multiple observers, a seemingly independent problem. We show experimentally that a previously proposed approach, combining random sampling and consensus labels, may need to be adapted to perform well in our application. Furthermore, this work aims to share practical experience in developing a machine learning application that assists highly variable interventional imaging for prostate cancer patients, to present robust and reproducible open-source implementations, and to report a set of comprehensive results and analysis comparing these practical, yet important, options in a real-world clinical application.

Parkinson’s Disease (PD) is one of the most common and diffused neurodegenerative diseases, with a prevalence of 0.3% in the general population of industrialized countries and ~1% in subjects over the age of 60 [1]. PD is associated with both genetic and neuroimaging factors. Despite the factors causing PD are still not completely clear, during the last decade there has been many significant advances in early clinical diagnosis, via brain imaging, and in our understanding of the genetics of Parkinson’s disease. Imaging genetics is an emerging longitudinal research field bridging genetic insights into the biology of complex diseases with quantitative neuroimaging phenotypes. Imaging genetics primarily focuses on identifying and characterizing how genes and genetic variation influence neuroanatomical and neurophysiological traits exploiting brain images. Until now have been proposed several imaging genetics methods to improve our knowledge on different complex neurodegenerative disease, such as Alzheimer’s disease, but only few studies focused on PD. Using PD as a case study, here we exploit the advantages of existing methods to analyze heterogeneous datasets. To do so, we designed and propose a multi-view imaging genetics workflow interpolating genotyping, transcriptomic data, and functional and morphological brain images. We show how to process and interpret data to retrieve several potential genetic variants, which could constitute potential genetic biomarkers of PD onset and progression. The method consists of three steps. Each phase explores different aspects and uses different tools to handle the different data type. We will move from the classical quality control phase, which corrects potential problems in the dataset, to the more technical and core part of our method, consisting of principal component analysis (PCA), Genome Wide Association Study (GWAS), and results merge, validation and interpretation. Each step carries out the analysis, by using different state-of-the-art tools and scripts written in various programming language like R, Python and Bash.

Longitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinson’s Disease (PD) as a case study. Parkinson’s disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases.

Interstitial lung diseases (ILDs) that present in childhood (chILD) are seen far less frequently than ILDs presenting in adults which themselves constitute rare disorders [1]. Histopathological [2, 3] and imaging [4] characterisation of chILD disease subtypes therefore lags behind adult ILDs. The field has also been constrained by comparisons with disease morphology in adults, despite the developmental differences in terms of growth and healing in the paediatric lung, which may alter disease patterns and distributions. The American Thoracic Society [5] and European [1] chILD management guidelines both specify a pivotal role for computed tomography (CT) imaging in the work-up of chILD patients to: 1) determine whether a chILD is present or not; and 2) where possible, to make a specific diagnosis of the underlying cause. For the second aim to be achieved, diagnostic reviews need to be reproducible between experts. Our study uniquely examined agreement between observers of varying experience in the CT evaluation of chILD to inform whether the current status of CT imaging and knowledge can be diagnostic of specific chILDs. We hypothesised that observer agreement for chILD groups and diagnoses would be limited. The study was not designed to relate CT agreement to final diagnosis. As a secondary analysis, we examined how CT interpretation differed between observers in children under and over 2 years of age., Making chILD diagnoses on CT is poorly reproducible, even amongst sub-specialists. CT might best improve diagnostic confidence in a multidisciplinary team setting when augmented with clinical, functional and haematological results. http://bit.ly/327jRCw

Alzheimer's disease (AD) is marked by the spread of misfolded amyloid-{beta} and tau proteins throughout the brain. While it is commonly believed that amyloid-{beta} abnormality drives the cascade of AD pathogenesis, several in vivo and post mortem studies indicate that in some subjects localized tau-based neurofibrillary tangles precede amyloid-{beta} pathology. This suggests that there may be multiple distinct subtypes of protein aggregation pathways within AD, with potentially different demographic, cognitive and comorbidity profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post mortem immunohistochemistry and in vivo positron emission tomography (PET) and cerebrospinal fluid (CSF) based measures of protein pathologies in two large observational cohorts. We consistently identified both amyloid-first and tau-first AD subtypes, where tau-first subjects had higher levels of soluble TREM2 compared to amyloid-first subjects. Our work provides insight into AD progression that may be valuable for interventional trials targeting amyloid-{beta} and tau.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.

The amygdala is one of the most widely connected structures in the primate brain and plays a key role in social and emotional behavior. Here, we present the first genome-wide association study (GWAS) of whole-brain resting-state amygdala networks to discern whether connectivity in these networks could serve as an endophenotype for social behavior. Leveraging published resting-state amygdala networks as a priori endophenotypes in a GWAS meta-analysis of two adolescent cohorts, we identified a common polymorphism on chr.8p23.2 (rs10105357 A/G, MAF (G)=0.35) associated with stronger connectivity in the medial amygdala network (beta=0.20, p=2.97×10−8). This network contains regions that support reward processes and affiliative behavior. People carrying two copies of the minor allele for rs10105357 participate in more prosocial behaviors (t=2.644, p=0.008) and have higher CSMD1 expression in the temporal cortex (t=3.281, p=0.002) than people with one or no copy of the allele. In post-mortem brains across the lifespan, we found that CSMD1 expression is relatively high in the amygdala (2.79 fold higher than white matter, p=1.80×10−29), particularly so for nuclei in the medial amygdala, reaching a maximum in later stages of development. Amygdala network endophenotyping has the potential to accelerate genetic discovery in disorders of social function, such as autism, in which CSMD1 may serve as a diagnostic and therapeutic target.

While data in healthcare is produced in quantities never imagined before, the feasibility of clinical studies is often hindered by the problem of data access and transfer, especially regarding privacy concerns. Federated learning allows privacy-preserving data analyses using decentralized optimization approaches keeping data securely decentralized. There are currently initiatives providing federated learning frameworks , which are however tailored to specific hardware and modeling approaches, and do not provide natively a deployable production-ready environment. To tackle this issue, herein we propose an open-source fed-erated learning frontend framework with application in healthcare. Our framework is based on a general architecture accommodating for different models and optimization methods. We present software components for clients and central node, and we illustrate the workflow for deploying learning models. We finally provide a real-world application to the federated analysis of multi-centric brain imaging data.

Aims Patients with idiopathic pulmonary fibrosis (IPF) receiving antifibrotic medication and patients with non-IPF fibrosing lung disease often demonstrate rates of annualised forced vital capacity (FVC) decline within the range of measurement variation (5.0%-9.9%). We examined whether change in visual CT variables could help confirm whether marginal FVC declines represented genuine clinical deterioration rather than measurement noise. Methods in two IPF cohorts (cohort 1: n=103, cohort 2: n=108), separate pairs of radiologists scored paired volumetric CTs (acquired between 6 and 24 months from baseline). Change in interstitial lung disease, honeycombing, reticulation, ground-glass opacity extents and traction bronchiectasis severity was evaluated using a 5-point scale, with mortality prediction analysed using univariable and multivariable Cox regression analyses. Both IPF populations were then combined to determine whether change in CT variables could predict mortality in patients with marginal FVC declines. Results on univariate analysis, change in all CT variables except ground-glass opacity predicted mortality in both cohorts. on multivariate analysis adjusted for patient age, gender, antifibrotic use and baseline disease severity (diffusing capacity for carbon monoxide), change in traction bronchiectasis severity predicted mortality independent of FVC decline. Change in traction bronchiectasis severity demonstrated good interobserver agreement among both scorer pairs. Across all study patients with marginal FVC declines, change in traction bronchiectasis severity independently predicted mortality and identified more patients with deterioration than change in honeycombing extent. Conclusions Change in traction bronchiectasis severity is a measure of disease progression that could be used to help resolve the clinical importance of marginal FVC declines., Wellcome Trust Clinical Research Career Development FellowshipWellcome Trust [209553/Z/17/Z]; MRC eMedLab Medical Bioinformatics Career Development Fellowship; Medical Research CouncilMedical Research Council UK (MRC) [MR/L016311/1]; European Union's Horizon 2020 research and innovation program [666992]; National Institute for Health Research Biomedical Research Centre at the University of Southampton, JJ was supported by a Wellcome Trust Clinical Research Career Development Fellowship (209553/Z/17/Z). AA holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship. This work was supported by the Medical Research Council (grant number MR/L016311/1). This project has received funding from the European Union's Horizon 2020 research and innovation program (grant agreement number 666992). CJB and MGJ were supported by the National Institute for Health Research Biomedical Research Centre at the University of Southampton.

The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analyzed from 1,069 non-epileptic controls and 1,249 patients: temporal lobe epilepsy with hippocampal sclerosis (N=599), temporal lobe epilepsy with normal MRI (N=275), genetic generalized epilepsy (N=182) and nonlesional extratemporal epilepsy (N=193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fiber tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at p“all epilepsies” lower fractional anisotropy was observed in most fiber tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. Less robust effects were seen with mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Those with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced differences in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and in mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of microstructural abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibers in a large multicentre study of epilepsy. Overall, epilepsy patients showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding new insights into pathological substrates that may be used to guide future therapeutic and genetic studies.

The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P

Genome-wide association studies have identified dozens of loci that alter the risk to develop Alzheimer’s disease. However, with the exception of the APOE-ε4 allele, most variants bear only little individual effect and have, therefore, limited diagnostic and prognostic value. Polygenic risk scores aim to collate the disease risk distributed across the genome in a single score. Recent works have demonstrated that polygenic risk scores designed for Alzheimer’s disease are predictive of clinical diagnosis, pathology confirmed diagnosis and changes in imaging biomarkers. Methodological innovations in polygenic risk modelling include the polygenic hazard score, which derives effect estimates for individual single nucleotide polymorphisms from survival analysis, and methods that account for linkage disequilibrium between genomic loci. In this work, using data from the Alzheimer’s disease neuroimaging initiative, we compared different approaches to quantify polygenic disease burden for Alzheimer’s disease and their association (beyond the APOE locus) with a broad range of Alzheimer’s disease-related traits: cross-sectional CSF biomarker levels, cross-sectional cortical amyloid burden, clinical diagnosis, clinical progression, longitudinal loss of grey matter and longitudinal decline in cognitive function. We found that polygenic scores were associated beyond APOE with clinical diagnosis, CSF-tau levels and, to a minor degree, with progressive atrophy. However, for many other tested traits such as clinical disease progression, CSF amyloid, cognitive decline and cortical amyloid load, the additional effects of polygenic burden beyond APOE were of minor nature. Overall, polygenic risk scores and the polygenic hazard score performed equally and given the ease with which polygenic risk scores can be derived; they constitute the more practical choice in comparison with polygenic hazard scores. Furthermore, our results demonstrate that incomplete adjustment for the APOE locus, i.e. only adjusting for APOE-ε4 carrier status, can lead to overestimated effects of polygenic scores due to APOE-ε4 homozygous participants. Lastly, on many of the tested traits, the major driving factor remained the APOE locus, with the exception of quantitative CSF-tau and p-tau measures.

Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns (subtypes) and individual severity (stages) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modelling situations within a single, consistent architecture.Code metadataCurrent code version v1.0Permanent link to code/repository used of this code version https://github.com/ucl-pond/pySuStaInLegal Code License MITCode versioning system used gitSoftware code languages, tools, and services used PythonCompilation requirements, operating environments & dependencies Linux, Mac, WindowsSupport email for questions leon.aksman@loni.usc.edu, p.wijeratne@ucl.ac.uk, alexandra.young@kcl.ac.uk

Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under-sampling and measurement errors and should be able to integrate multi-modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi-task learning based approach to modeling univariate trajectories across subjects that addresses these criteria.Our approach learns multiple subjects’ trajectories within a single model that allows for different types of information sharing, i.e.coupling, across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel-based coupling allows linking subjects’ trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI-derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET-based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature.Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori, we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.