Search

Objectives Methods Results Conclusions We report the effect of quality of therapy delivery on outcomes in a randomized, controlled trial of behavioural activation (BA) and guided self‐help (GSH) for depression in adults with intellectual disabilities.A study specific measure of quality was used in a linear mixed effect model to determine the effects therapy and therapy quality on therapy outcome.There was a significant interaction between quality and treatment type, with lower quality therapy associated with better outcome for GSH but poorer outcome for BA, with little difference in outcomes at higher levels of therapy quality.Factors suggesting high quality in individualized BA may indicate problematic engagement for GSH. More research into processes in therapy for people with intellectual disabilities is required. [ABSTRACT FROM AUTHOR]

Longitudinal research is crucial to fully assess the putative impact of the COVID‐19 pandemic on children with an intellectual disability in special school settings—ideally drawing on data pre‐pandemic to be able to evaluate later impact. Data on challenging behaviour and adaptive skills were collected annually for 348 students in one special school across four time points pre‐pandemic and one time point post‐pandemic. Data were analysed using multilevel models with repeated observations over the five time points. There was a decrease in aggressive and destructive behaviours and a decrease in adaptive skills at the post‐pandemic time point, after accounting for other important covariates. There was no evidence of a change in stereotyped or self‐injurious challenging behaviours. Other research using longitudinal methods is rare, but the current findings are consistent with previous research reporting on the impact of COVID‐19 on children and young people, particularly from parent reports. Future considerations for schools include adopting appropriate strategies to support learners to reintegrate back into education. Further research is needed to look at the longer‐term impact of the pandemic on challenging behaviour in children with an intellectual disability. [ABSTRACT FROM AUTHOR]

The wellbeing of parents of children with autism residing in mainland China remains understudied. We aimed to examine whether and how parental perceived social support, individualism, and collectivism acted together to moderate the relationships between child behavior problems and parental psychological distress in Chinese parents of children with autism. With convenience and snowball sampling, data on 268 primary caregiver parents of children with autism were collected from an online cross‐sectional survey. Linear regression analysis indicated that child behavior problems were significantly associated with increased psychological distress in Chinese parents of children with autism. There was no evidence to support the stress‐buffering model of social support in moderation analysis of the association between child behavior problems and parental psychological distress. Nonetheless, increased social support was associated with lower levels of parental psychological distress. Moderated moderation analyses did not support a role for individualism or collectivism as a moderator of the putative buffering role of social support. However, there was evidence that parental individualism was associated with increased parental psychological distress. Our findings highlight that child behavior problems are a robust correlate of parental psychological distress, and parental social support may act as a compensatory factor promoting less psychological distress rather than having a protective role. The role of social support and cultural values in the wellbeing of parents of children with autism in China requires additional exploration, including longitudinal research designs. Lay Summary: Child behavior problems typically decrease the wellbeing of parents of children with autism, whilst perceived support increases wellbeing. However, these relationships have been rarely explored in Chinese parents. We asked parents of children with autism living in mainland China about their psychological distress, perceived support and cultural values, and their children's behavior problems. We found that higher levels of child behavior problems and individualistic cultural values were associated with increased parental psychological distress, and a higher level of perceived support and was associated with less psychological distress. These findings confirm the importance of addressing child behavior problems and increasing parental perception of social support in improving wellbeing of parents of children with autism. More work is needed to understand why parental individualistic values, discrepant from the typical collectivistic values in Chinese society, may be related to increased psychological distress in Chinese parents of children with autism. [ABSTRACT FROM AUTHOR]

Background: Large‐scale multi‐study analyses are required to ensure reproducibility, reliability and generalizability in mapping neurodegeneration and risk for ADRD. However, the heterogeneity in data collection paradigms can complicate and confound data pooling; data harmonization is essential. Longitudinal studies add to the complexity of harmonization as a variety of follow‐up time points and time encoding schemes are used. Here, we pool data from 8 publicly available longitudinal neuroimaging studies on neurodegeneration and dementia to highlight differences in: 1) diagnostic categorization of controls, and people with mild cognitive impairment, and dementia; 2) the extent of follow‐up visits across neuroimaging and clinical assessments; and 3) encodings of various meta‐data elements including scanner manufacturer, sex, and handedness. To allow a systematic approach to multi‐study dementia research, we propose an initial ontological framework for longitudinal data archival and retrieval, capable of capturing similar data elements within given themes while ensuring that unique differences across studies are retained. Method: AIBL, ADNI‐1, ADNI2/GO, ADNI‐3, OASIS‐2, OASIS‐3, PREVENT‐AD, and MIRIAD, were accessed. Study designs, inclusion and exclusion criteria, and downloaded data elements were used to describe diagnostic criteria, imaging data, demographic features, and longitudinal data collection schemes. We create common terms to map data elements across studies, and a consistent naming scheme for longitudinal data. Result: Figure 1 shows the breakdown of diagnostic labels per cohort, and highlights how different cognitive labels may be assigned to people with the same performance scores, for example, on the Mini Mental State Examination (MMSE). Figure 2 highlights the differences in longitudinal data collection schemes across cohorts, and in labels of commonly collected elements. Using common terms provided an easy way to simultaneously query data across all studies, while retaining a map back to the original terms. Figure 3 showcases our proposed naming scheme for capturing longitudinal data elements using a relational ontological framework. This framework successfully harmonized data across study demographics, timepoints, and imaging properties. Conclusion: Multi‐study data analyses are becoming common practice thanks to large scale efforts such as ENIGMA and GAAIN. Harmonization of meta‐data across studies will allow for efficient pooling of data for tracking disease progression and related risk over time. [ABSTRACT FROM AUTHOR]

Background: Poor comprehenders are traditionally identified as having below‐average reading comprehension, average‐range word reading, and a discrepancy between the two. While oral language tends to be low in poor comprehenders, reading is a complex trait and heterogeneity may go undetected by group‐level comparisons. Methods: We took a preregistered data‐driven approach to identify poor comprehenders and examine whether multiple distinct cognitive profiles underlie their difficulties. Latent mixture modelling identified reading profiles in 6846 children from the Avon Longitudinal Study of Parents and Children, based on reading and listening comprehension assessments at 8–9 years. A second mixture model examined variation in the cognitive profiles of weak comprehenders, using measures of reading, language, working memory, nonverbal ability, and inattention. Results: A poor comprehender profile was not identified by the preregistered model. However, by additionally controlling for overall ability, a 6‐class model emerged that incorporated a profile with relatively weak comprehension (N = 947, 13.83%). Most of these children had weak reading comprehension in the context of good passage reading, accompanied by weaknesses in vocabulary and nonverbal ability. A small subgroup showed more severe comprehension difficulties in the context of additional cognitive impairments. Conclusions: Isolated impairments in specific components of reading are rare, yet a data‐driven approach can be used to identify children with relatively weak comprehension. Vocabulary and nonverbal ability were most consistently weak within this group, with broader cognitive difficulties also apparent for a subset of children. These findings suggest that poor comprehension is best characterised along a continuum, and considered in light of multiple risks that influence severity. [ABSTRACT FROM AUTHOR]

The reactions under which interstitial structures of Pd form are profoundly important and prevalent in catalysis; the formation and stability of Pd hydride structures are well understood, however, interstitial structures of the carbide and nitride are relatively under explored. This work reports a systematic study of the formation and stability of PdCx and PdNx at elevated temperatures and different atmospheres using in situ Pd L3 edge XANES spectroscopy. These studies were further complemented by the application of 14N MAS‐NMR experiments and computational DFT investigations. The experiments confirmed that PdCx was significantly more stable than PdNx; 14N MAS‐NMR provided direct confirmation on the formation of the nitride, however, the XANES studies evidenced very limited stability under the conditions employed. Moreover, the results suggest that the formation of the nitride imparts some structural changes that are not entirely reversible under the conditions used in these experiments. This work provides important insights into the stability of interstitial structures of Pd and the conditions in which they could be employed for directed catalytic processes. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to investigate the mediating role of child brain structure in the relationship between prenatal gestational diabetes mellitus (GDM) exposure and child adiposity. Methods: This was a cross‐sectional study of 9– to 10‐year‐old participants and siblings across the US. Data were obtained from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study®. Brain structure was evaluated by magnetic resonance imaging. GDM exposure was self‐reported, and discordance for GDM exposure within biological siblings was identified. Mixed effects and mediation models were used to examine associations among prenatal GDM exposure, brain structure, and adiposity markers with sociodemographic covariates. Results: The sample included 8521 children (7% GDM‐exposed), among whom there were 28 sibling pairs discordant for GDM exposure. Across the entire study sample, prenatal exposure to GDM was associated with lower global and regional cortical gray matter volume (GMV) in the bilateral rostral middle frontal gyrus and superior temporal gyrus. GDM‐exposed siblings also demonstrated lower global cortical GMV than unexposed siblings. Global cortical GMV partially mediated the associations between prenatal GDM exposure and child adiposity markers. Conclusions: The results identify brain markers of prenatal GDM exposure and suggest that low cortical GMV may explain increased obesity risk for offspring prenatally exposed to GDM. [ABSTRACT FROM AUTHOR]

Summary: Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large‐scale coordinated meta‐analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5 [13.9] years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA)‐Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP‐Trend, University Medicine Greifswald, Greifswald, Germany) recruited population‐based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging‐based brain morphometry using an insomnia brain score was then assessed. Within each cohort, we used an ordinary least‐squares linear regression to investigate the link between the individual regional cortical and subcortical volumes and the presence of insomnia symptoms. Then, we performed a fixed‐effects meta‐analysis across cohorts based on the first‐level results. For the insomnia brain score, weighted logistic ridge regression was performed on one sample (Freiburg), which separated patients with ID from controls to train a model based on the segmentation measurements. Afterward, the insomnia brain scores were validated using the other two samples. The model was used to predict the log‐odds of the subjects with insomnia given individual insomnia‐related brain atrophy. After adjusting for multiple comparisons, we did not detect any significant associations between insomnia symptoms and cortical or subcortical volumes, nor could we identify a global insomnia‐related brain atrophy pattern. Thus, we observed inconsistent brain morphology differences between individuals with and without insomnia across three independent cohorts. Further large‐scale cross‐sectional/longitudinal studies using both structural and functional neuroimaging are warranted to decipher the neurobiology of insomnia. [ABSTRACT FROM AUTHOR]

Understanding the evolutionarily conserved feature of functional laterality in the habenula has been attracting attention due to its potential role in human cognition and neuropsychiatric disorders. Deciphering the structure of the human habenula remains to be challenging, which resulted in inconsistent findings for brain disorders. Here, we present a large‐scale meta‐analysis of the left–right differences in the habenular volume in the human brain to provide a clearer picture of the habenular asymmetry. We searched PubMed, Web of Science, and Google Scholar for articles that reported volume data of the bilateral habenula in the human brain, and assessed the left–right differences. We also assessed the potential effects of several moderating variables including the mean age of the participants, magnetic field strengths of the scanners and different disorders by using meta‐regression and subgroup analysis. In total 52 datasets (N = 1427) were identified and showed significant heterogeneity in the left–right differences and the unilateral volume per se. Moderator analyses suggested that such heterogeneity was mainly due to different MRI scanners and segmentation approaches used. While inversed asymmetry patterns were suggested in patients with depression (leftward) and schizophrenia (rightward), no significant disorder‐related differences relative to healthy controls were found in either the left–right asymmetry or the unilateral volume. This study provides useful data for future studies of brain imaging and methodological developments related to precision habenula measurements, and also helps to further understand potential roles of the habenula in various disorders. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disorder that progresses over time, but its exact cause remains unknown. While risk genomic loci for AD‐related biomarkers have been identified in previous studies, these findings have mostly been based on European cohorts. As a result, there is limited research on the African American (AA) population. To address this research gap, we conducted a Genome‐Wide Association Study (GWAS) using whole genome sequencing (WGS) data from Alzheimer's Disease Sequencing Project (ADSP) to identify AD‐related variants in the AA population. Method: WGS data and harmonized phenotypes from the cognitive, fluid biomarker, and neuropathology domains were downloaded from the ADSP database. Quality control (QC) was performed on the WGS data through removing high missing call rate SNPs, rare genetic variants, genetically closely related subjects, subjects with high missing call rate, and genomic loci that failed the Hardy‐Weinberg equilibrium among cognitive normal cohort. After QC 9,815,631 SNPs and 33,324 subjects were preserved. A total of 5,397 subjects in AA population were selected for the subsequent GWAS analysis. Scalable and Accurate Implementation of Generalized mixed model (SAIGE) was used to perform association tests between genetic variants and 39 phenotypic traits controlled for age, sex, and the first 10 principal components. Functional mapping and annotation (FUMA) was used to perform the post GWAS annotations. Result: Thirty‐nine phenotypic traits from cognitive, fluid biomarker, and neuropathology domains were screened but only the "infarcts and lacunes", "PD braak (dichotomous)", and "whole brain vascular disease" of the neuropathology data had significant GWAS signals. We identified one risk locus for each of the phenotypic traits: lead SNP rs7604487 significantly associated with "infarcts and lacunes"; lead SNP rs7093080 significantly associated with "PD braak (dichotomous)"; and lead SNP rs623123 significantly associated with "whole brain vascular disease" (Figure 1). These associations were insignificant in the ADSP cohort with European ancestry. Conclusion: Our GWAS on AD related biomarkers identified three genetic loci associated with multiple neuropathological processes among the AA population. These findings have a potential to contribute to a better understanding of the AD genetic risk in the AA population. It warrants further investigation to replicate the discovery in independent cohorts. [ABSTRACT FROM AUTHOR]

Background: Late‐life ambient air pollution exposures are associated with increased Alzheimer's disease (AD) risk1. Cortical thinning in temporal areas vulnerable to AD is associated with memory decline, neuropathological changes, and increased AD risk. However, most neuroimaging studies on air pollution neurotoxicity analyzed volumetric indices that are not sensitive to early cortical changes, and focused on discrete regions of interest (ROI) selected a priori rather than taking an ROI approach across the whole brain. Therefore, the mechanisms linking exposures to early neuropathological processes in older age are poorly understood. Method: We examined data from 1068 cognitively unimpaired women from the Women's Health Initiative Memory Study who underwent neuroimaging in 2005‐6 (Meanage = 77.8±3.7). Long‐term residential exposures to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were quantified as the 3‐year average of monthly estimates prior to MRI using spatiotemporal models with regionalized universal kriging. Brain scans were processed using FreeSurfer‐v.5.3.0 to estimate cortical thickness in 34 bilateral regions parcellated with the Desikan‐Killiany atlas. Exposure effects on AD‐vulnerable regions were assessed by calculating an AD signature (higher value representing thicker cortices) of surface‐area‐weighted thickness in the bilateral entorhinal, fusiform, inferior temporal, and middle temporal cortices2. We used linear mixed models to estimate exposure effects on standardized regional cortical thickness and the AD signature, adjusting for sociodemographic, lifestyle, and clinical characteristics, and a random effect for scanner manufacturer. Result: After adjusting for covariates and multiple comparisons using the False discovery rate (FDR) method, higher exposures to PM2.5 and NO2 were associated with thinner cortices in at least 31 brain regions (FDR‐adjusted p's<0.05). There were also significant adverse effects of PM2.5 (β = ‐0.055, 95%CI = [‐0.07,‐0.04] per 1µg/m3) and NO2 (β = ‐0.022, 95%CI = [‐0.03,‐0.01] per 1ppb) on the AD signature, equivalent to 1.1 and 0.5 years of aging, respectively. Across all analyses the strongest effects were observed in the frontal lobe, especially the motor cortex. Conclusion: In cognitively unimpaired older women, higher exposures to PM2.5 and NO2 were associated with widespread cortical thinning, including regions sensitive to AD. However, the strongest associations in the frontal lobe suggest a greater impact of air pollution neurotoxicity on brain aging rather than AD risk. [ABSTRACT FROM AUTHOR]

Background: Microstructural abnormalities likely precede macrostructural changes in the Alzheimer's disease (AD) cascade. Diffusion MRI (dMRI) is sensitive to microstructural properties of brain tissue but few studies have evaluated dMRI measures in cortical gray matter where many early AD histopathological changes occur. Event‐based modeling (EBM) is a data‐driven approach for probabilistically sequencing cross‐sectional biomarkers in the order that they likely become abnormal. Here, we used EBM to examine the sequence of changes in cortical dMRI measures relative to more widely used amyloid, tau, brain volume, and cognitive biomarkers in two independent AD studies. Method: T1w, dMRI, and amyloid‐PET (FBB/FBP) data were analyzed in 461 ADNI3 participants and 188 OASIS3 participants (Figure 1A). Some ADNI3 participants also had tau‐PET (AV‐1451) and CSF pTAU‐181 and Aβ1‐42 data. In addition to DTI, novel single‐shell adaptations of multi‐shell models, NODDI‐DTI and MAP‐AMURA, were fit to the dMRI data. 10 mean cortical dMRI measures were extracted using FreeSurfer parcellated T1w‐images (Figure 1B). T1w cortical and hippocampal volumes were also extracted, and all MRI measures harmonized using ComBat. Mean cortical amyloid‐PET SUVRs were converted to centiloids. Associations between each of 17 biomarkers (Figure 1C) and cognitive impairment (CI –MCI+Dementia– vs CN) were tested in ADNI3. Significant biomarkers were included in an ADNI3 EBM to determine event ordering.The EBM was validated in OASIS3 and used to classify clinical diagnosis. Result: All biomarkers except DTI FA showed significant differences between CN and CI ADNI3 participants (Figure 2A). ADNI3 EBM confirmed existing AD models: CSF amyloid abnormalities preceded PET, followed by CSF pTau, while cognitive and volumetric abnormalities occurred at later stages. Changes in all dMRI biomarkers preceded CSF or PET Tau, cognitive, and volumetric abnormalities (Figure 2B). Estimated disease stages in ADNI3 distinguish dementia from CN with an AUC = 0.96 (10‐fold cross‐validation). OASIS3 disease stages, estimated using ADNI3 biomarker distributions excluding CSF measures and tau‐PET, classified diagnosis with an AUC = 0.85 (Figure 3). Conclusion: Cortical microstructural measure abnormalities, including greater diffusivity, lower restriction and lower dispersion potentially due to neurite loss and lower dendritic arborization complexity, may precede those detectable with conventional T1w biomarkers. [ABSTRACT FROM AUTHOR]

Metabolic illnesses (MET) are detrimental to brain integrity and are common comorbidities in patients with mental illnesses, including major depressive disorder (MDD). We quantified effects of MET on standard regional brain morphometric measures from 3D brain MRI as well as diffusion MRI in a large sample of UK BioBank participants. The pattern of regional effect sizes of MET in non‐psychiatric UKBB subjects was significantly correlated with the spatial profile of regional effects reported by the largest meta‐analyses in MDD but not in bipolar disorder, schizophrenia or Alzheimer's disease. We used a regional vulnerability index (RVI) for MET (RVI‐MET) to measure individual's brain similarity to the expected patterns in MET in the UK Biobank sample. Subjects with MET showed a higher effect size for RVI‐MET than for any of the individual brain measures. We replicated elevation of RVI‐MET in a sample of MDD participants with MET versus non‐MET. RVI‐MET scores were significantly correlated with the volume of white matter hyperintensities, a neurological consequence of MET and age, in both groups. Higher RVI‐MET in both samples was associated with obesity, tobacco smoking and frequent alcohol use but was unrelated to antidepressant use. In summary, MET effects on the brain were regionally specific and individual similarity to the pattern was more strongly associated with MET than any regional brain structural metric. Effects of MET overlapped with the reported brain differences in MDD, likely due to higher incidence of MET, smoking and alcohol use in subjects with MDD. [ABSTRACT FROM AUTHOR]

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/. [ABSTRACT FROM AUTHOR]

Precision Teaching is a behavior measurement system that emphasizes the development of behavioral repertoires and utilizes Standard Celeration Charts as its primary tool. This system has been applied across various areas, including mainstream and special education, and has successfully improved academic, motor, communication, and other skills. While previous systematic reviews have highlighted important aspects of Precision Teaching, a more comprehensive evaluation is needed to consider all its different applications and recent developments in conceptualizing it. Therefore, this systematic review and meta‐analysis will assess the effectiveness of Precision Teaching in accelerating human behavior, identify all the areas of its application, and review the technical aspects of its implementation. The review aims to provide a comprehensive understanding of the system and its potential benefits for individuals in different settings. [ABSTRACT FROM AUTHOR]

Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion‐weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model—diffusion tensor imaging (DTI)—and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease. [ABSTRACT FROM AUTHOR]

Background: We examined whether a series of variables were related to the number of psychiatric inpatients using publicly available data about English psychiatric bed utilisation and NHS workforce. Method: Using linear regression, with auto‐regressive errors, we examined relationships between variables over time using data from December 2013 to March 2021. Results: Over time, the number of inpatients reduced by either 6.58 or 8.07 per month depending upon the dataset utilised, and the number of community nurses and community nursing support staff reduced by 7.43 and 2.14 nurses per month, respectively. Increasing numbers of consultant psychiatrists were associated with fewer inpatients over time. More care and treatment reviews (CTRs) were associated with more admissions over time, while more post‐admission CTRs were associated with increased discharges over time. Conclusions: Future studies should examine whether psychiatric bed utilisation elsewhere within the NHS by people with intellectual disabilities has increased. [ABSTRACT FROM AUTHOR]

We consider how analysis of brain lateralization using functional transcranial Doppler ultrasound (fTCD) data can be brought in line with modern statistical methods typically used in functional magnetic resonance imaging (fMRI). Conventionally, a laterality index is computed in fTCD from the difference between the averages of each hemisphere's signal within a period of interest (POI) over a series of trials. We demonstrate use of generalized linear models (GLMs) and generalized additive models (GAM) to analyze data from individual participants in three published studies (N = 154, 73 and 31), and compare this with results from the conventional POI averaging approach, and with laterality assessed using fMRI (N = 31). The GLM approach was based on classic fMRI analysis that includes a hemodynamic response function as a predictor; the GAM approach estimated the response function from the data, including a term for time relative to epoch start (simple GAM), plus a categorical index corresponding to individual epochs (complex GAM). Individual estimates of the fTCD laterality index are similar across all methods, but error of measurement is lowest using complex GAM. Reliable identification of cases of bilateral language appears to be more accurate with complex GAM. We also show that the GAM‐based approach can be used to efficiently analyze more complex designs that incorporate interactions between tasks. [ABSTRACT FROM AUTHOR]

Phomoxanthone A is a naturally occurring molecule and a powerful anti‐cancer agent, although its mechanism of action is unknown. To facilitate the determination of its biological target(s), we used affinity‐based labelling using a phomoxanthone A probe. Labelled proteins were pulled down, subjected to chemoproteomics analysis using LC‐MS/MS and ATP synthase was identified as a likely target. Mitochondrial ATP synthase was validated in cultured cells lysates and in live intact cells. Our studies show sixty percent inhibition of ATP synthase by 260 μM phomoxanthone A. [ABSTRACT FROM AUTHOR]

Background: The populations of the Middle East and North Africa countries (MENA) are predicted to contribute to the world's sharpest increases in rates of Alzheimer's disease and related dementias (ADRD) in the coming years (GBD 2022), yet this group is underrepresented in health studies. The UK Biobank provides one of the largest and well‐characterized datasets on aging immigrants in the UK. We compare risk factors for ADRDs between UK immigrants from MENA countries to those of immigrants from other countries. Method: Immigrants from MENA countries (N = 3557), India (N = 2959), Germany (N = 1103), and genetically‐determined British individuals (N = 1929) born outside of the UK in any of the three areas of interest were included in the study. There are multiple definitions of MENA: here, MENA was defined broadly to include the union of all included countries; Figure 1 highlights the distribution in this sample. We focused on variables associated with suboptimal brain aging and ADRD risk: APOE genotypes; education; an algorithmically defined diet quality score (Zhuang, 2021); BMI; incidence of parental death before 65 years; and the Townsend deprivation index (TDI) as a proxy of socioeconomic status (SES). Chi‐squared or Mann‐Whitney tests provided statistics on distribution discrepancies for categorical or continuous data, respectively. We also charted hippocampal volumes with age for the subset of each ethnic group who underwent neuroimaging. Result: Figure 2 highlights group demographics and distributions. Compared to all other groups (Figure 3), MENA groups had higher TDI (lower SES), lowest diet quality, and highest BMIs; these differences remained even in college educated individuals. MENA also reported a greater incidence of maternal deaths before age 65. German immigrants were more highly educated than all other groups. ApoE4 alleles were significantly less prevalent in MENA and Indian countries than groups of European ancestry (Figure 4). Hippocampal volumes, adjusted for intracranial volume, had similar age‐trends across groups. Conclusion: Many ADRD risk factors differ substantially across different ethnic groups, even when the proportion with a college education is not different. It is imperative to understand underlying lifestyle and sociological factors associated with immigration, to provide adequate healthcare for all aging individuals. [ABSTRACT FROM AUTHOR]

Background: Apolipoprotein (ApoE) E4 (e4) carriers are at increased risk for Alzheimer's disease (AD) (Strittmatter, 2012), which is often characterized by accelerated hippocampal atrophy and cognitive decline after 65 (Reinvang, 2013). However, not all e4 carriers develop AD. In those at heightened genetic risk, it is important to evaluate lifestyle factors that might slow brain aging, and delay dementia onset. Poorer sleep quality, physical activity, and diet have been associated with several AD‐related processes ‐ including increased beta‐amyloid burden, decline in vascular integrity, reduced glucose metabolism, and neuronal dysfunction (Mander 2017, Radak 2010, Sezgin 2014). Smoking (Durazzo, 2014) and excessive alcohol consumption (Venkataraman, 2017) have also been shown to promote AD pathology and risk. Moreover, social contact and education may enhance cognitive reserve (Livingston, 2020). Here, we investigated the role of these factors in promoting structural brain resilience in a population susceptible to AD from the UK Biobank (UKB; Miller, 2016). Method: UKB participants over age 65 with e3e4 or e4e4 genotype were included (1415M, 1355F). A novel deep learning method (Lam, 2020) was used to calculate predicted brain age (a machine‐learning estimate of a person's age from their brain MRI). Participants were labeled as resilient if their predicted age was 3+ years younger than their actual age. Lifestyle factors evaluated may be found in Table 1. Association rule learning (Agrawal, 1993) using the mlxtend python library was used to identify sets of factors most often associated with resiliency. Result: 604/2770 of the e4 carriers were labeled as resilient (Figure 1). The antecedent set with the highest likelihood (42%) of being associated with resiliency included following the ideal recommendations for physical activity, infrequent alcohol consumption, averaging at least 7 hours of sleep/night, participating in leisure/social activities, and never having smoked. These measures also appeared in other likely antecedent sets (Table 2). Conclusion: We identified several lifestyle factors that may promote resilience to brain aging in those at elevated risk for AD. We aim to replicate our findings using a larger dataset with brain age calculated, and train sex‐specific models as well as testing them in people of more diverse ancestry. [ABSTRACT FROM AUTHOR]

Background: Diffusion magnetic resonance imaging (dMRI) provides insight into white matter (WM) microstructural changes in Alzheimer's disease and mild cognitive impairment (MCI). The Alzheimer's Disease Neuroimaging Initiative's (ADNI) has currently extended its ADNI3 imaging protocol to include dMRI from Siemens, Philips and General Electric (GE) scanners, resulting in seven vendor‐specific dMRI acquisition protocols (Table1). Here we present the updated ADNI3 preprocessing pipeline1 (Figure1) for diffusion tensor imaging (DTI) metrics made available via ADNI's database, and evaluate their utility for detecting WM differences associated with clinical impairment. Method: Raw dMRI downloaded from ADNI are first denoised using DiPy's principal component analysis (PCA) denoising algorithms: local PCA for zero‐padded k‐space data (GE), or Marchenko‐Pastur PCA for data with the original acquisition matrix (Siemens/Philips). dMRI are then corrected for Gibbs ringing, eddy currents using FSL's eddy_cuda with repol outlier estimation and slice‐to‐volume correction, intensity inhomogeneity with ANTS N4, and echo‐planar imaging distortions with a three‐channel non‐linear registration of the subject‐level preprocessed average B0 image to the subject's skull‐stripped T1‐weighted MRI. Bias field correction is applied again to dMRI with FSL‐FAST. DTI fractional anisotropy (FA), axial, mean and radial diffusivity (AxD, MD, RD) are estimated with FSL's dtifit, using weighted least squares. The JHU ICBM‐DTI‐81 atlas FA is warped to subject‐level FA maps using ANTS, and the transformations are applied to atlas labels. Subject‐level mean and robust mean (using M‐estimator from R's 'WRS2' package) of DTI metrics within 73 WM regions of interest (ROIs; Table2) are extracted. Here, we fit linear mixed effects models of diagnosis (N=733) and clinical dementia rating (CDR‐sob; N=682; Table3) in 28 of the 73 available ROIs, covarying for age, sex, age*sex, and including nested protocol|site variables as random effects; multiple comparisons were corrected for using false discovery rate (q=0.05). Result: Lower anisotropy and greater diffusivity were associated with greater impairment, particularly in the hippocampal cingulum (Figure2). Results from mean and robust mean metrics were comparable. Overall, our results follow previous work2,3 using publicly available ADNI dMRI. Conclusion: Subject‐level ADNI3 dMRI measures, sensitive to clinical indices of impairment as presented here, are available at https://ida.loni.usc.edu/. References: [1 ]Thomopoulos (2021), https://doi.org/10.1117/12.2606337; [2] Nir (2013), https://doi.org/10.1016/j.nicl.2013.07.006; [3] Zavaliangos‐Petropulu (2019), https://doi.org/10.3389/fninf.2019.00002. [ABSTRACT FROM AUTHOR]

Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain‐age). The choice of the ML approach in estimating brain‐age in youth is important because age‐related brain changes in this age‐group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5–22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9–10 years and another comprising 594 individuals aged 5–21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree‐based, and kernel‐based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross‐validation folds, number of extreme outliers, and sample size. Tree‐based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain‐age in youth. [ABSTRACT FROM AUTHOR]

Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual's brain‐wide similarity to the expected SMI patterns derived from meta‐analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual's similarity to genome‐wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI‐MDD (Cohen's d = 0.20, p = 1 × 10−23) and PRS‐MDD (d = 0.17, p = 1 × 10−15) than nonpsychiatric controls. UKBB participants with BD and SSD showed significant elevation in the respective RVIs (d = 0.65 and 0.60; p = 3 × 10−5 and.009, respectively) and PRS (d = 0.57 and 1.34; p =.002 and.002, respectively). Elevated RVI‐SSD were replicated in an independent sample (d = 0.53, p = 5 × 10−5). RVI‐MDD and RVI‐SSD but not RVI‐BD were associated with childhood adversity (p <.01). In nonpsychiatric controls, elevation in RVI and PRS were associated with lower cognitive performance (p < 10−5) in six out of seven domains and showed specificity with disorder‐associated deficits. In summary, the RVI is a novel brain index for SMI and shows similar or better specificity for SMI than PRS, and together they may complement each other in the efforts to characterize the genomic to brain level risks for SMI. [ABSTRACT FROM AUTHOR]

Background: Numerous GWAS studies of Alzheimer's disease (AD) have identified over 70 AD risk variants using SNP‐based genotyping or sequencing data. Recently, a new whole‐genome tiling (WGT) representation of whole‐genome sequencing (WGS) data has been proposed to enable an innovative definition of an individual's genome; this WGT representation can support supervised and unsupervised machine learning. In this study, we perform a new AD GWAS study on the WGT representation of the ADNI WGS data. Methods: The detailed description, genome tiling pipeline, and a publicly available example of WGT data are available at: https://curii.co/su92l‐j7d0g‐swtofxa2rct8495. In our analysis, we first performed quality control, imputation, and one‐hot encoding of tile variants (Fig. 1). Then, for each genome tile, we used the likelihood ratio test to compare two logistic regression models to get a single p‐value, where a full model used the tile variants and covariates to predict disease status, and a null model used only covariates including age, sex, education, APOE4, and first 20 PCs. Participants included 1,504 subjects (1,032 cases and 472 controls). In comparison, set‐based GWAS analysis was performed using PLINK 1.9 on ADNI SNP‐based WGS data. Results: 8,560,743 tiles passed the QC process and were included in our analysis. The likelihood ratio test yielded 35,582 significant tiles with Bonferroni correction. A set‐based GWAS comparative study among all significant tiles using SNP‐based WGS data identified 1,535 sets with at least one significant SNP variant. Among 1,535 sets, 1,066 sets passed uncorrected p≤0.05; 115 sets passed p≤0.005; and 15 sets passed p≤0.0005 (Fig. 2). Conclusions: Our initial investigation of the tiling data shows that the WGT representation has promising power for identifying significant tiles that cannot be detected using the SNP representation. Complementary to the genotype values examined in traditional SNP analysis, the WGT analysis focuses on examining the haplotype variants within each tile and can capture the interaction pattern among SNPs within the haplotype. This initial AD GWAS study on WGT data demonstrates the promise of the tile representation for revealing novel genetic risk and protective factors in AD. [ABSTRACT FROM AUTHOR]

Data on sex ratios, age classes, reproductive success and health status are key metrics to manage populations, yet can be difficult to collect in wild cetacean populations. Long‐term individual‐based studies provide a unique opportunity to apply unoccupied aerial system (UAS) photogrammetry to non‐invasively measure body morphometrics of individuals with known life history information. The aims of this study were (1) to compare length measurements from UAS photogrammetry with laser photogrammetry and (2) to explore whether UAS measurements of body width could be used to remotely determine pregnancy status, sex or age class in a well‐studied bottlenose dolphin population in Scotland. We carried out five boat‐based surveys in July and August 2017, with concurrent photo‐identification, UAS and laser photogrammetry. Photographs were measured using bespoke programmes, MorphMetriX for UAS photos and a Zooniverse project for laser photos. In total 64 dolphins were identified using photo‐ID, 54 of which had concurrent UAS body length and 47 with laser body length measurements. We also measured body widths at 10% increments from 10% to 90% of body length for 48 individuals of known sex, age class and/or pregnancy status. There was no significant difference in the length of individuals measured with UAS and laser photogrammetry. Discriminant analyses of the body width–length (WL) ratios expected to change during pregnancy, correctly assigned pregnancy status for 14 of the 15 females of known pregnancy status. Only one pregnant female was incorrectly assigned as not pregnant. However, our results showed that length and body width cannot accurately allocate these bottlenose dolphins to sex or age class using photogrammetry techniques alone. The present study illustrates that UAS and laser photogrammetry measurements are comparable for small cetaceans and demonstrates that UAS measurements of body WL ratio can accurately assign pregnancy status in bottlenose dolphins. [ABSTRACT FROM AUTHOR]

Objective: This study tested the hypothesis, in a prospective cohort study design, that maternal saturated free fatty acid (sFFA) concentration during pregnancy is prospectively associated with offspring (newborn) hypothalamic (HTH) microstructure and to explore the functional relevance of this association with respect to early-childhood body fat percentage (BF%).Methods: In N = 94 healthy newborns (born mean 39.3 [SD 1.5] weeks gestation), diffusion-weighted magnetic resonance imaging was performed shortly after birth (25.3 [12.5] postnatal days), and a subgroup (n = 37) underwent a dual-energy x-ray absorptiometry scan in early childhood (4.7 [SD 0.7] years). Maternal sFFA concentration during pregnancy was quantified in fasting blood samples via liquid chromatography-mass spectrometry. Infant HTH microstructural integrity was characterized using mean diffusivity (MD). Multiple linear regression was used to test the association between maternal sFFA and HTH MD, accounting for newborn sex, age at scan, mean white matter MD, and image quality. Multiple linear regression models also tested the association between HTH MD and early-childhood BF%, accounting for breastfeeding status.Results: Maternal sFFA during pregnancy accounted for 8.3% of the variation in newborn HTH MD (β-std = 0.25; p = 0.006). Furthermore, newborn HTH MD prospectively accounted for 15% of the variation in early-childhood BF% (β-std = 0.32; p = 0.019).Conclusions: These findings suggest that maternal overnutrition during pregnancy may influence the development of the fetal hypothalamus, which, in turn, may have clinical relevance for childhood obesity risk. [ABSTRACT FROM AUTHOR]

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open‐source, containerized, user‐friendly tool that facilitates reproducible analysis of task‐based and resting‐state fMRI data through uniform application of preprocessing, quality assessment, single‐subject feature extraction, and group‐level statistics. It provides state‐of‐the‐art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post‐processing functions at the individual subject level, including calculation of task‐based activation, seed‐based connectivity, network‐template (or dual) regression, atlas‐based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low‐frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed‐effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post‐processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe. [ABSTRACT FROM AUTHOR]

This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder‐related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes. [ABSTRACT FROM AUTHOR]

Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio‐metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning "BrainAge" index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD− (N = 964), SMI−/CMD+ (N = 3,765), SMI−/CMD− (N = 8,083). SMI (F = 40.47, p = 2.06 × 10−10) and CMD (F = 24.69, p = 6.82 × 10−7) significantly, independently impacted whole‐brain QRI in SMI+. SSD had the largest effect (Cohen's d = 1.42) then BD (d = 0.55), and MDD (d = 0.15). Hypertension had a significant effect on SMI+ (d = 0.19) and SMI− (d = 0.14). SMI effects were direct, independent of MD, and remained significant after correcting for effects of antipsychotic medications. Whole‐brain QRI was significantly (p < 10−16) associated with the volume of white matter hyperintensities (WMH). However, WMH did not show significant association with SMI and was driven by CMD, chiefly hypertension (p < 10−16). We used a simple and robust index, QRI, the demonstrate additive effect of SMI and CMD on accelerated brain aging. We showed a greater effect of psychiatric illnesses on QRI compared to cardio‐metabolic illness. Our findings suggest that subjects with SMI should be among the targets for interventions to protect against age‐related cognitive decline. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we present a transdisciplinary framework and testable hypotheses regarding the process of fetal programming of energy homeostasis brain circuitry. Our model proposes that key aspects of energy homeostasis brain circuitry already are functional by the time of birth (with substantial interindividual variation); that this phenotypic variation at birth is an important determinant of subsequent susceptibility for energy imbalance and childhood obesity risk; and that this brain circuitry exhibits developmental plasticity, in that it is influenced by conditions during intrauterine life, particularly maternal–placental–fetal endocrine, immune/inflammatory, and metabolic processes and their upstream determinants. We review evidence that supports the scientific premise for each element of this formulation, identify future research directions, particularly recent advances that may facilitate a better quantification of the ontogeny of energy homeostasis brain networks, highlight animal and in vitro‐based approaches that may better address the determinants of interindividual variation in energy homeostasis brain networks, and discuss the implications of this formulation for the development of strategies targeted towards the primary prevention of childhood obesity. [ABSTRACT FROM AUTHOR]

This article locates the emergence of new thinking on the left around work, age and assets within the lineage of 'class composition' analysis arising from the autonomist movement in mid‐twentieth century Italy. With reference to contemporary debates around electoral and political strategy within the Labour Party, the article critically appraises the potential applicability of this extra‐parliamentary 'militant methodology' for the present day identification of ontologically and epistemologically privileged class actors and political subjects in a first past the post parliamentary system. Noting the resemblance of such approaches to the orthodox Marxist 'gravedigger thesis', which reads off from economic relations the existence of agents of social transformation, the article argues that this analytical and strategic framing represents a flawed approach for Labour and the wider left. Other approaches are needed to navigate successfully the pluralist construction of consent through coalition building and compromise on which parliamentary politics rests. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding spatiotemporally varying animal distributions can inform ecological understanding of species' behavior (e.g., foraging and predator/prey interactions) and support development of management and conservation measures. Data from an array of echolocation‐click detectors (C‐PODs) were analyzed using Bayesian spatiotemporal modeling to investigate spatial and temporal variation in occurrence and foraging activity of harbor porpoises (Phocoena phocoena) and how this variation was influenced by daylight and presence of bottlenose dolphins (Tursiops truncatus). The probability of occurrence of porpoises was highest on an offshore sandbank, where the proportion of detections with foraging clicks was relatively low. The porpoises' overall distribution shifted throughout the summer and autumn, likely influenced by seasonal prey availability. Probability of porpoise occurrence was lowest in areas close to the coast, where dolphin detections were highest and declined prior to dolphin detection, leading potentially to avoidance of spatiotemporal overlap between porpoises and dolphins. Increased understanding of porpoises' seasonal distribution, key foraging areas, and their relationship with competitors can shed light on management options and potential interactions with offshore industries. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi‐)genetics. Finally, we highlight points where FreeSurfer‐based hippocampal subfield studies may be optimized. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Alcohol use disorder (AUD) and cannabis use disorder (CUD) are associated with brain alterations particularly involving fronto‐cerebellar and meso‐cortico‐limbic circuitry. However, such abnormalities have additionally been reported in other psychiatric conditions, and until recently there has been few large‐scale investigations to compare such findings. The current study uses the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) consortium method of standardising structural brain measures to quantify case–control differences and to compare brain‐correlates of substance use disorders with those published in relation to other psychiatric disorders. Using the ENIGMA protocols, we report effect sizes derived from a meta‐analysis of alcohol (seven studies, N = 798, 54% are cases) and cannabis (seven studies, N = 447, 45% are cases) dependent cases and age‐ and sex‐matched controls. We conduct linear analyses using harmonised methods to process and parcellate brain data identical to those reported in the literature for ENIGMA case–control studies of major depression disorder (MDD), schizophrenia (SCZ) and bipolar disorder so that effect sizes are optimally comparable across disorders. R elationships between substance use disorder diagnosis and subcortical grey matter volumes and cortical thickness were assessed with intracranial volume, age and sex as co‐variates. After correcting for multiple comparisons, AUD case–control meta‐analysis of subcortical regions indicated significant differences in the thalamus, hippocampus, amygdala and accumbens, with effect sizes (0.23) generally equivalent to, or larger than |0.23| those previously reported for other psychiatric disorders (except for the pallidum and putamen). On measures of cortical thickness, AUD was associated with significant differences bilaterally in the fusiform gyrus, inferior temporal gyrus, temporal pole, superior frontal gyrus, and rostral and caudal anterior cingulate gyri. Meta‐analysis of CUD case–control studies indicated reliable reductions in amygdala, accumbens and hippocampus volumes, with the former effect size comparable to, and the latter effect size around half of that reported for alcohol and SCZ. CUD was associated with lower cortical thickness in the frontal regions, particularly the medial orbitofrontal region, but this effect was not significant after correcting for multiple testing. This study allowed for an unbiased cross‐disorder comparison of brain correlates of substance use disorders and showed alcohol‐related brain anomalies equivalent in effect size to that found in SCZ in several subcortical and cortical regions and significantly greater alterations than those found in MDD in several subcortical and cortical regions. Although modest, CUD results overlapped with findings reported for AUD and other psychiatric conditions, but appear to be most robustly related to reduce thickness of the medial orbitofrontal cortex. [ABSTRACT FROM AUTHOR]

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long‐term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas‐based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network‐based hippocampal segmentation method, does not rely solely on a single atlas‐based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well‐accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy. [ABSTRACT FROM AUTHOR]

Gray matter volume (GMV) in frontal cortical and limbic regions is susceptible to cocaine‐associated reductions in cocaine‐dependent individuals (CD) and is negatively associated with duration of cocaine use. Gender differences in CD individuals have been reported clinically and in the context of neural responses to cue‐induced craving and stress reactivity. The variability of GMV in select brain areas between men and women (e.g., limbic regions) underscores the importance of exploring interaction effects between gender and cocaine dependence on brain structure. Therefore, voxel‐based morphometry data derived from the ENIGMA Addiction Consortium were used to investigate potential gender differences in GMV in CD individuals compared to matched controls (CTL). T1‐weighted MRI scans and clinical data were pooled from seven sites yielding 420 gender‐ and age‐matched participants: CD men (CDM, n = 140); CD women (CDW, n = 70); control men (CTLM, n = 140); and control women (CTLW, n = 70). Differences in GMV were assessed using a 2 × 2 ANCOVA, and voxelwise whole‐brain linear regressions were conducted to explore relationships between GMV and duration of cocaine use. All analyses were corrected for age, total intracranial volume, and site. Diagnostic differences were predominantly found in frontal regions (CD < CTL). Interestingly, gender × diagnosis interactions in the left anterior insula and left lingual gyrus were also documented, driven by differences in women (CDW < CTLW). Further, lower right hippocampal GMV was associated with greater cocaine duration in CDM. Given the importance of the anterior insula to interoception and the hippocampus to learning contextual associations, results may point to gender‐specific mechanisms in cocaine addiction. [ABSTRACT FROM AUTHOR]

Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA‐Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA‐Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA‐Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is a major cause of disability worldwide, but the heterogeneous nature of TBI with respect to injury severity and health comorbidities make patient outcome difficult to predict. Injury severity accounts for only some of this variance, and a wide range of preinjury, injury‐related, and postinjury factors may influence outcome, such as sex, socioeconomic status, injury mechanism, and social support. Neuroimaging research in this area has generally been limited by insufficient sample sizes. Additionally, development of reliable biomarkers of mild TBI or repeated subconcussive impacts has been slow, likely due, in part, to subtle effects of injury and the aforementioned variability. The ENIGMA Consortium has established a framework for global collaboration that has resulted in the largest‐ever neuroimaging studies of multiple psychiatric and neurological disorders. Here we describe the organization, recent progress, and future goals of the Brain Injury working group. [ABSTRACT FROM AUTHOR]

Patients with schizophrenia have patterns of brain deficits including reduced cortical thickness, subcortical gray matter volumes, and cerebral white matter integrity. We proposed the regional vulnerability index (RVI) to translate the results of Enhancing Neuro Imaging Genetics Meta‐Analysis studies to the individual level. We calculated RVIs for cortical, subcortical, and white matter measurements and a multimodality RVI. We evaluated RVI as a measure sensitive to schizophrenia‐specific neuroanatomical deficits and symptoms and studied the timeline of deficit formations in: early (≤5 years since diagnosis, N = 45, age = 28.8 ± 8.5); intermediate (6–20 years, N = 30, age 43.3 ± 8.6); and chronic (21+ years, N = 44, age = 52.5 ± 5.2) patients and healthy controls (N = 76, age = 38.6 ± 12.4). All RVIs were significantly elevated in patients compared to controls, with the multimodal RVI showing the largest effect size, followed by cortical, white matter and subcortical RVIs (d = 1.57, 1.23, 1.09, and 0.61, all p < 10−6). Multimodal RVI was significantly correlated with multiple cognitive variables including measures of visual learning, working memory and the total score of the MATRICS consensus cognitive battery, and with negative symptoms. The multimodality and white matter RVIs were significantly elevated in the intermediate and chronic versus early diagnosis group, consistent with ongoing progression. Cortical RVI was stable in the three disease‐duration groups, suggesting neurodevelopmental origins of cortical deficits. In summary, neuroanatomical deficits in schizophrenia affect the entire brain; the heterochronicity of their appearance indicates both the neurodevelopmental and progressive nature of this illness. These deficit patterns may be useful for early diagnosis and as quantitative targets for more effective treatment strategies aiming to alter these neuroanatomical deficit patterns. [ABSTRACT FROM AUTHOR]