The study of fear extinction represents an important example of translational neuroscience in psychiatry and promises to improve the understanding and treatment of anxiety and fear-related disorders. We present the results of a set of meta-analyses of human fear extinction studies in healthy participants, conducted with functional magnetic resonance imaging (fMRI) and reporting whole-brain results. Meta-analyses of fear extinction learning primarily implicate consistent activation of brain regions linked to threat appraisal and experience, including the dorsal anterior cingulate and anterior insular cortices. An overlapping anatomical result was obtained from the meta-analysis of extinction recall studies, except when studies directly compared an extinguished threat stimulus to an unextinguished threat stimulus (instead of a safety stimulus). In this latter instance, more consistent activation was observed in dorsolateral and ventromedial prefrontal cortex regions, together with other areas including the hippocampus. While our results partially support the notion of a shared neuroanatomy between human and rodent models of extinction processes, they also encourage an expanded account of the neural basis of human fear extinction.

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A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

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

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA WIERENGA ET AL.

Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a convergence of peaks of statistical significance, and reduce the assessment of the evidence to a binary classification exclusively based on p-values (i.e., voxels can only be "statistically significant" or "non-statistically significant"). Here, we detail how to conduct a meta-analysis using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), a novel method that uses a standard permutation test to assess whether effects are not null. We also show how to grade the strength of the evidence according to a set of criteria that considers a range of statistical significance levels (from more liberal to more conservative), the amount of data or the detection of potential biases (e.g., small-study effect and excess of significance). To exemplify the procedure, we detail the conduction of a meta-analysis of voxel-based morphometry studies in obsessive-compulsive disorder, and we provide all the data already extracted from the manuscripts to allow the reader to replicate the metaanalysis easily. SDM-PSI can also be used for meta-analyses of functional magnetic resonance imaging, diffusion tensor imaging, position emission tomography and surface-based morphometry studies.

BackgroundEstablishing neurobiological markers of posttraumatic stress disorder (PTSD) is essential to aid in diagnosis and treatment development. Fear processing deficits are central to PTSD, and their neural signatures may be used as such markers.MethodsHere, we conducted a meta-analysis of seven Pavlovian fear conditioning fMRI studies comparing 156 patients with PTSD and 148 trauma-exposed healthy controls (TEHC) using seed-based d-mapping, to contrast neural correlates of experimental phases, namely conditioning, extinction learning, and extinction recall.ResultsPatients with PTSD, as compared to TEHCs, exhibited increased activation in the anterior hippocampus (extending to the amygdala) and medial prefrontal cortex during conditioning; in the anterior hippocampus-amygdala regions during extinction learning; and in the anterior hippocampus-amygdala and medial prefrontal areas during extinction recall. Yet, patients with PTSD have shown an overall decreased activation in the thalamus during all phases in this meta-analysis.ConclusionFindings from this metanalysis suggest that PTSD is characterized by increased activation in areas related to salience and threat, and lower activation in the thalamus, a key relay hub between subcortical areas. If replicated, these fear network alterations may serve as objective diagnostic markers for PTSD, and potential targets for novel treatment development, including pharmacological and brain stimulation interventions. Future longitudinal studies are needed to examine whether these observed network alteration in PTSD are the cause or the consequence of PTSD.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to examine age‐related trajectories inferred from cross‐sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter‐individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age‐related morphometric patterns.