Multiscale heterogeneity of white matter morphometry in psychiatric disorders.

Background: Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. Methods: We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. Results: The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. Conclusions: The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

The gut-microbiome in adult Attention-deficit/hyperactivity disorder - A Meta-analysis.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition that persists into adulthood in the majority of individuals. While the gut-microbiome seems to be relevant for ADHD, the few publications on gut-microbial alterations in ADHD are inconsistent, in the investigated phenotypes, sequencing method/region, preprocessing, statistical approaches, and findings. To identify gut-microbiome alterations in adult ADHD, robust across studies and statistical approaches, we harmonized bioinformatic pipelines and analyses of raw 16S rRNA sequencing data from four adult ADHD case-control studies (NADHD=312, NNoADHD=305). We investigated diversity and differential abundance of selected genera (logistic regression and ANOVA-like Differential Expression tool), corrected for age and sex, and meta-analyzed the study results. Converging results were investigated for association with hyperactive/impulsive and inattentive symptoms across all participants. Beta diversity was associated with ADHD diagnosis but showed significant heterogeneity between cohorts, despite harmonized analyses. Several genera were robustly associated with adult ADHD; e.g., Ruminococcus_torques_group (LogOdds=0.17, pfdr=4.42 × 10-2), which was more abundant in adults with ADHD, and Eubacterium_xylanophilum_group (LogOdds= -0.12, pfdr=6.9 × 10-3), which was less abundant in ADHD. Ruminococcus_torques_group was further associated with hyperactivity/impulsivity symptoms and Eisenbergiella with inattention and hyperactivity/impulsivity (pfdr<0.05). The literature points towards a role of these genera in inflammatory processes. Irreproducible results in the field of gut-microbiota research, due to between study heterogeneity and small sample sizes, stress the need for meta-analytic approaches and large sample sizes. While we robustly identified genera associated with adult ADHD, that might overall be considered beneficial or risk-conferring, functional studies are needed to shed light on these properties.

Glycated hemoglobin, type 2 diabetes, and poor diabetes control are positively associated with impulsivity changes in aged individuals with overweight or obesity and metabolic syndrome.

Impulsivity has been proposed to have an impact on glycemic dysregulation. However, it remains uncertain whether an unfavorable glycemic status could also contribute to an increase in impulsivity levels. This study aims to analyze associations of baseline and time-varying glycemic status with 3-year time-varying impulsivity in older adults at high risk of cardiovascular disease. A 3-year prospective cohort design was conducted within the PREDIMED-Plus-Cognition substudy. The total population includes 487 participants (mean age = 65.2 years; female = 50.5%) with overweight or obesity and metabolic syndrome. Insulin resistance (HOMA-IR), glycated hemoglobin (HbA1c), presence of type 2 diabetes mellitus, and type 2 diabetes control were evaluated. Impulsivity was measured using the Impulsive Behavior Scale questionnaire and various cognitive measurements. Impulsivity z-scores were generated to obtain Global, Trait, and Behavioral Impulsivity domains. Linear mixed models were used to study the longitudinal associations across baseline, 1-year, and 3-year follow-up visits. HOMA-IR was not significantly related to impulsivity. Participants with higher HbA1c levels, type 2 diabetes, and poor control of diabetes showed positive associations with the Global Impulsivity domain over time, and those with higher HbA1c levels were further related to increases in the Trait and Behavioral Impulsivity domains over the follow-up visits. These results suggest a potential positive feedback loop between impulsivity and glycemic-related dysregulation.

Exploring the genetic architecture of brain structure and ADHD using polygenic neuroimaging-derived scores.

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of neuropsychiatric disorders and highlighted their complexity. Careful consideration of the polygenicity and complex genetic architecture could aid in the understanding of the underlying brain mechanisms. We introduce an innovative approach to polygenic scoring, utilizing imaging-derived phenotypes (IDPs) to predict a clinical phenotype. We leveraged IDP GWAS data from the UK Biobank, to create polygenic imaging-derived scores (PIDSs). As a proof-of-concept, we assessed genetic variations in brain structure between individuals with ADHD and unaffected controls across three NeuroIMAGE waves (n = 954). Out of the 94 PIDS, 72 exhibited significant associations with their corresponding IDPs in an independent sample. Notably, several global measures, including cerebellum white matter, cerebellum cortex, and cerebral white matter, displayed substantial variance explained for their respective IDPs, ranging from 3% to 5.7%. Conversely, the associations between each IDP and the clinical ADHD phenotype were relatively weak. These findings highlight the growing power of GWAS in structural neuroimaging traits, enabling the construction of polygenic scores that accurately reflect the underlying polygenic architecture. However, to establish robust connections between PIDS and behavioral or clinical traits such as ADHD, larger samples are needed. Our novel approach to polygenic risk scoring offers a valuable tool for researchers in the field of psychiatric genetics.

The Role of Diet and the Gut Microbiota in Reactive Aggression and Adult ADHD-An Exploratory Analysis.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition, of-ten persistent into adulthood and accompanied by reactive aggression. Associations of diet and the gut-microbiome with ADHD as well as emotional behaviors suggest potential clinical rele-vance of both. However, studies on diet and the gut-microbiome in human reactive aggression are lacking, and should investigate the interaction between diet and the gut-microbiome leading to behavioral changes to assess their potential clinical relevance. In this study, we investigated the interaction of diet and gut-microbiota with adult ADHD and reactive aggression in 77 adults with ADHD and 76 neurotypical individuals. We studied the relationships of ADHD and reactive ag-gression with dietary patterns, bacterial community and taxonomic differences of 16S-sequenced fecal microbiome samples, and potential mediating effects of bacterial genus abundance on signifi-cant diet-behavior associations. The key findings include: (1) An association of high-energy intake with reactive aggeression scores (pFDR = 4.01 × 10-02); (2) Significant associations of several genera with either reactive aggression or ADHD diagnosis with no overlap; and (3) No significant mediation effects of the selected genera on the association of reactive aggression with the high-energy diet. Our results suggest that diet and the microbiome are linked to reactive aggression and/or ADHD individually, and highlight the need to further study the way diet and the gut-microbiome inter-act.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Growing Up Together in Society (GUTS): A team science effort to predict societal trajectories in adolescence and young adulthood.

Our society faces a great diversity of opportunities for youth. The 10-year Growing Up Together in Society (GUTS) program has the long-term goal to understand which combination of measures best predict societal trajectories, such as school success, mental health, well-being, and developing a sense of belonging in society. Our leading hypothesis is that self-regulation is key to how adolescents successfully navigate the demands of contemporary society. We aim to test these questions using socio-economic, questionnaire (including experience sampling methods), behavioral, brain (fMRI, sMRI, EEG), hormonal, and genetic measures in four large cohorts including adolescents and young adults. Two cohorts are designed as test and replication cohorts to test the developmental trajectory of self-regulation, including adolescents of different socioeconomic status thereby bridging individual, family, and societal perspectives. The third cohort consists of an entire social network to examine how neural and self-regulatory development influences and is influenced by whom adolescents and young adults choose to interact with. The fourth cohort includes youth with early signs of antisocial and delinquent behavior to understand patterns of societal development in individuals at the extreme ends of self-regulation and societal participation, and examines pathways into and out of delinquency. We will complement the newly collected cohorts with data from existing large-scale population-based and case-control cohorts. The study is embedded in a transdisciplinary approach that engages stakeholders throughout the design stage, with a strong focus on citizen science and youth participation in study design, data collection, and interpretation of results, to ensure optimal translation to youth in society.

Shared genetics linking sociability with the brain's default mode network.

The brain's default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. In the present study, we examined the genetic relationship between sociability and DMN-related resting-state functional magnetic resonance imaging (rs-fMRI) traits. To this end, we used genome-wide association summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N=34,691-342,461). First, we examined global and local genetic correlations between sociability and the rs-fMRI traits. Second, to assess putatively causal relationships between the traits, we conducted bi-directional Mendelian randomisation (MR) analyses. Finally, we prioritised genes influencing both sociability and rs-fMRI traits by combining three methods: gene-expression eQTL MR analyses, the CELLECT framework using single-nucleus RNA-seq data, and network propagation in the context of a protein-protein interaction network. Significant local genetic correlations were found between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the frontal/cingulate and angular/temporal cortices. Sociability affected 12 rs-fMRI traits when allowing for weakly correlated genetic instruments. Combing all three methods for gene prioritisation, we defined 17 highly prioritised genes, with DRD2 and LINGO1 showing the most robust evidence across all analyses. By integrating genetic and transcriptomics data, our gene prioritisation strategy may serve as a blueprint for future studies. The prioritised genes could be explored as potential biomarkers for social dysfunction in the context of neuropsychiatric disorders and as drug target genes.

Shared familial risk for type 2 diabetes mellitus and psychiatric disorders: a nationwide multigenerational genetics study.

BACKGROUND: Psychiatric disorders and type 2 diabetes mellitus (T2DM) are heritable, polygenic, and often comorbid conditions, yet knowledge about their potential shared familial risk is lacking. We used family designs and T2DM polygenic risk score (T2DM-PRS) to investigate the genetic associations between psychiatric disorders and T2DM. METHODS: We linked 659 906 individuals born in Denmark 1990-2000 to their parents, grandparents, and aunts/uncles using population-based registers. We compared rates of T2DM in relatives of children with and without a diagnosis of any or one of 11 specific psychiatric disorders, including neuropsychiatric and neurodevelopmental disorders, using Cox regression. In a genotyped sample (iPSYCH2015) of individuals born 1981-2008 (n = 134 403), we used logistic regression to estimate associations between a T2DM-PRS and these psychiatric disorders. RESULTS: Among 5 235 300 relative pairs, relatives of individuals with a psychiatric disorder had an increased risk for T2DM with stronger associations for closer relatives (parents:hazard ratio = 1.38, 95% confidence interval 1.35-1.42; grandparents: 1.14, 1.13-1.15; and aunts/uncles: 1.19, 1.16-1.22). In the genetic sample, one standard deviation increase in T2DM-PRS was associated with an increased risk for any psychiatric disorder (odds ratio = 1.11, 1.08-1.14). Both familial T2DM and T2DM-PRS were significantly associated with seven of 11 psychiatric disorders, most strongly with attention-deficit/hyperactivity disorder and conduct disorder, and inversely with anorexia nervosa. CONCLUSIONS: Our findings of familial co-aggregation and higher T2DM polygenic liability associated with psychiatric disorders point toward shared familial risk. This suggests that part of the comorbidity is explained by shared familial risks. The underlying mechanisms still remain largely unknown and the contributions of genetics and environment need further investigation.

Genetic variants for head size share genes and pathways with cancer.

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions.

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation.

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Randomized controlled trial of individualized arousal-biofeedback for children and adolescents with disruptive behavior disorders (DBD).

Disruptive behavior disorders [including conduct disorder (CD) and oppositional defiant disorder (ODD)] are common childhood and adolescent psychiatric conditions often linked to altered arousal. The recommended first-line treatment is multi-modal therapy and includes psychosocial and behavioral interventions. Their modest effect sizes along with clinically and biologically heterogeneous phenotypes emphasize the need for innovative personalized treatment targeting impaired functions such as arousal dysregulation. A total of 37 children aged 8-14 years diagnosed with ODD/CD were randomized to 20 sessions of individualized arousal biofeedback using skin conductance levels (SCL-BF) or active treatment as usual (TAU) including psychoeducation and cognitive-behavioral elements. The primary outcome was the change in parents´ ratings of aggressive behavior measured by the Modified Overt Aggression Scale. Secondary outcome measures were subscales from the Child Behavior Checklist, the Inventory of Callous-Unemotional traits, and the Reactive-Proactive Aggression Questionnaire. The SCL-BF treatment was neither superior nor inferior to the active TAU. Both groups showed reduced aggression after treatment with small effects for the primary outcome and large effects for some secondary outcomes. Importantly, successful learning of SCL self-regulation was related to reduced aggression at post-assessment. Individualized SCL-BF was not inferior to active TAU for any treatment outcome with improvements in aggression. Further, participants were on average able to self-regulate their SCL, and those who best learned self-regulation showed the highest clinical improvement, pointing to specificity of SCL-BF regulation for improving aggression. Further studies with larger samples and improved methods, for example by developing BF for mobile use in ecologically more valid settings are warranted.

The gut-microbiome in adult Attention-deficit/hyperactivity disorder - A Meta-analysis.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition that persists into adulthood in the majority of individuals. While the gut-microbiome seems to be relevant for ADHD, the few publications on gut-microbial alterations in ADHD are inconsistent, in the investigated phenotypes, sequencing method/region, preprocessing, statistical approaches, and findings. To identify gut-microbiome alterations in adult ADHD, robust across studies and statistical approaches, we harmonized bioinformatic pipelines and analyses of raw 16S rRNA sequencing data from four adult ADHD case-control studies (N ADHD =312, N NoADHD =305). We investigated diversity and differential abundance of selected genera (logistic regression and ANOVA-like Differential Expression tool), corrected for age and sex, and meta-analyzed the study results. Converging results were investigated for association with hyperactive/impulsive and inattentive symptoms across all participants. Beta diversity was associated with ADHD diagnosis but showed significant heterogeneity between cohorts, despite harmonized analyses. Several genera were robustly associated with adult ADHD; e.g., Ruminococcus_torques_group (LogOdds=0.17, p fdr =4.42×10 -2 ), which was more abundant in adults with ADHD, and Eubacterium_xylanophilum_group (LogOdds= -0.12, p fdr =6.9 x 10 -3 ), which was less abundant in ADHD. Ruminococcus_torques_group was further associated with hyperactivity/impulsivity symptoms and Eisenbergiella with inattention and hyperactivity/impulsivity (p fdr <0.05). The literature points towards a role of these genera in inflammatory processes. Irreproducible results in the field of gut-microbiota research, due to between study heterogeneity and small sample sizes, stress the need for meta-analytic approaches and large sample sizes. While we robustly identified genera associated with adult ADHD, that might overall be considered beneficial or risk-conferring, functional studies are needed to shed light on these properties.