A Comparison Between Early Presentation of Dementia with Lewy Bodies, Alzheimer's Disease, and Parkinson's Disease: Evidence from Routine Primary Care and UK Biobank Data.

OBJECTIVE: The purpose of this study was to simultaneously contrast prediagnostic clinical characteristics of individuals with a final diagnosis of dementia with Lewy Bodies (DLB), Parkinson's disease (PD), and Alzheimer's disease (AD) compared with controls without neurodegenerative disorders. METHODS: Using the longitudinal THIN database in the United Kingdom, we tested the association of each neurodegenerative disorder with a selected list of symptoms and broad families of treatments, and compared the associations between disorders to detect disease-specific effects. We replicated the main findings in the UK Biobank. RESULTS: We used data of 28,222 patients with PD, 20,214 with AD, 4,682 with DLB, and 20,214 healthy controls. All neurodegenerative disorders were significantly associated with the presence of multiple clinical characteristics before their diagnosis, including sleep disorders, falls, psychiatric symptoms, and autonomic dysfunctions. When comparing patients with DLB with patients with PD and patients with AD patients, falls, psychiatric symptoms, and autonomic dysfunction were all more strongly associated with DLB in the 5 years preceding the first neurodegenerative diagnosis. The use of statins was lower in patients who developed PD and higher in patients who developed DLB compared to patients with AD. In patients with PD, the use of statins was associated with the development of dementia in the 5 years following PD diagnosis. INTERPRETATION: Prediagnostic presentations of falls, psychiatric symptoms, and autonomic dysfunctions were more strongly associated with DLB than PD and AD. This study also suggests that although several associations with medications are similar in neurodegenerative disorders, statin usage is negatively associated with PD but positively with DLB and AD as well as development of dementia in PD. ANN NEUROL 2023;94:259-270.

Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium.

Identifying brain alterations associated with suicidal thoughts and behaviors (STBs) in young people is critical to understanding their development and improving early intervention and prevention. The ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium analyzed neuroimaging data harmonized across sites to examine brain morphology associated with STBs in youth. We performed analyses in three separate stages, in samples ranging from most to least homogeneous in terms of suicide assessment instrument and mental disorder. First, in a sample of 577 young people with mood disorders, in which STBs were assessed with the Columbia Suicide Severity Rating Scale (C-SSRS). Second, in a sample of young people with mood disorders, in which STB were assessed using different instruments, MRI metrics were compared among healthy controls without STBs (HC; N = 519), clinical controls with a mood disorder but without STBs (CC; N = 246) and young people with current suicidal ideation (N = 223). In separate analyses, MRI metrics were compared among HCs (N = 253), CCs (N = 217), and suicide attempters (N = 64). Third, in a larger transdiagnostic sample with various assessment instruments (HC = 606; CC = 419; Ideation = 289; HC = 253; CC = 432; Attempt=91). In the homogeneous C-SSRS sample, surface area of the frontal pole was lower in young people with mood disorders and a history of actual suicide attempts (N = 163) than those without a lifetime suicide attempt (N = 323; FDR-p = 0.035, Cohen's d = 0.34). No associations with suicidal ideation were found. When examining more heterogeneous samples, we did not observe significant associations. Lower frontal pole surface area may represent a vulnerability for a (non-interrupted and non-aborted) suicide attempt; however, more research is needed to understand the nature of its relationship to suicide risk.

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Are Sex Differences in Human Brain Structure Associated With Sex Differences in Behavior?

On average, men and women differ in brain structure and behavior, raising the possibility of a link between sex differences in brain and behavior. But women and men are also subject to different societal and cultural norms. We navigated this challenge by investigating variability of sex-differentiated brain structure within each sex. Using data from the Queensland Twin IMaging study (n = 1,040) and Human Connectome Project (n = 1,113), we obtained data-driven measures of individual differences along a male-female dimension for brain and behavior based on average sex differences in brain structure and behavior, respectively. We found a weak association between these brain and behavioral differences, driven by brain size. These brain and behavioral differences were moderately heritable. Our findings suggest that behavioral sex differences are, to some extent, related to sex differences in brain structure but that this is mainly driven by differences in brain size, and causality should be interpreted cautiously.

White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group.

Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

Can network analysis of self-reported psychopathology shed light on the core phenomenology of bipolar disorders in adolescents and young adults?

OBJECTIVES: Network analysis is increasingly applied to psychopathology research. We used it to examine the core phenomenology of emerging bipolar disorder (BD I and II) and 'at risk' presentations (major depression with a family history of BD). METHODOLOGY: The study sample comprised a community cohort of 1867 twin and nontwin siblings (57% female; mean age ~26) who had completed self-report ratings of (i) depression-like, hypomanic-like and psychotic-like experiences; (ii) family history of BD; and (iii) were assessed for mood and psychotic syndromes using the Composite International Diagnostic Interview (CIDI). Symptom networks were compared for recent onset BD versus other cohort members and then for individuals at risk of BD (depression with/without a family history of BD). RESULTS: The four key symptoms that differentiated recent onset BD from other cohort members were: anergia, psychomotor speed, hypersomnia and (less) loss of confidence. The four key symptoms that differentiated individuals at high risk of BD from unipolar depression were anergia, psychomotor speed, impaired concentration and hopelessness. However, the latter network was less stable and more error prone. CONCLUSIONS: We are encouraged by the overlaps between our findings and those from two recent publications reporting network analyses of BD psychopathology, especially as the studies recruited from different populations and employed different network models. However, the advantages of applying network analysis to youth mental health cohorts (which include many individuals with multimorbidity) must be weighed against the disadvantages including basic issues such as judgements regarding the selection of items for inclusion in network models.

Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold.

Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability within groups of networks, i.e., the structure of connection similarities and differences across a set of networks. We propose a statistical framework to model these variations based on manifold-valued latent factors. Each network adjacency matrix is decomposed as a weighted sum of matrix patterns with rank one. Each pattern is described as a random perturbation of a dictionary element. As a hierarchical statistical model, it enables the analysis of heterogeneous populations of adjacency matrices using mixtures. Our framework can also be used to infer the weight of missing edges. We estimate the parameters of the model using an Expectation-Maximization-based algorithm. Experimenting on synthetic data, we show that the algorithm is able to accurately estimate the latent structure in both low and high dimensions. We apply our model on a large data set of functional brain connectivity matrices from the UK Biobank. Our results suggest that the proposed model accurately describes the complex variability in the data set with a small number of degrees of freedom.

Region-specific sex differences in the hippocampus.

The hippocampus is a brain region critical for learning and memory, and is also implicated in several neuropsychiatric disorders that show sex differences in prevalence, symptom expression, and mean age of onset. On average, males have larger hippocampal volumes than females, but findings are inconclusive after adjusting for overall brain size. Although the hippocampus is a heterogenous structure, few studies have focused on sex differences in the hippocampal subfields - with little consensus on whether there are regionally specific sex differences in the hippocampus after adjusting for brain size, or whether it is important to adjust for total hippocampal volume (HPV). Here, using two young adult cohorts from the Queensland Twin IMaging study (QTIM; N â€‹= â€‹727) and the Human Connectome Project (HCP; N â€‹= â€‹960), we examined differences between males and females in the volumes of 12 hippocampal subfields, extracted using FreeSurfer 6.0. After adjusting the subfield volumes for either HPV or brain size (brain segmentation volume (BSV)) using four controlling methods (allometric, covariate, residual and matching), we estimated the percentage difference of the sex effect (males versus females) and Cohen's d using hierarchical general linear models. Males had larger volumes compared to females in the parasubiculum (up to 6.04%; Cohen's d â€‹= â€‹0.46) and fimbria (up to 8.75%; d â€‹= â€‹0.54) after adjusting for HPV. These sex differences were robust across the two cohorts and multiple controlling methods, though within cohort effect sizes were larger for the matched approach, due to the smaller sub-sample. Additional sex effects were identified in the HCP cohort and combined (QTIM and HCP) sample (hippocampal fissure (up to 6.79%), presubiculum (up to 3.08%), and hippocampal tail (up to -0.23%)). In contrast, no sex differences were detected for the volume of the cornu ammonis (CA)2/3, CA4, Hippocampus-Amygdala Transition Area (HATA), or the granule cell layer of the dentate gyrus (GCDG). These findings show that, independent of differences in HPV, there are regionally specific sex differences in the hippocampus, which may be most prominent in the fimbria and parasubiculum. Further, given sex differences were less consistent across cohorts after controlling for BSV, adjusting for HPV rather than BSV may benefit future studies. This work may help in disentangling sex effects, and provide a better understanding of the implications of sex differences for behaviour and neuropsychiatric disorders.

A unified framework for association and prediction from vertex-wise grey-matter structure.

The recent availability of large-scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex-wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N = 9,497 for discovery, N = 4,323 for replication) and the Human Connectome Project (N = 1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP. We then extended the model for a bivariate analysis to estimate grey-matter correlation between phenotypes, which revealed that body size (i.e., height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the morphometricity (confirmed using a conditional analysis), providing possible insight into previous MRI case-control results for psychiatric disorders where case status is associated with body mass index. Our LMM framework also allowed to predict some of the associated phenotypes from the vertex-wise measures, in two independent samples. Finally, we demonstrated additional new applications of our approach (a) region of interest (ROI) analysis that retain the vertex-wise complexity; (b) comparison of the information retained by different MRI processings.

Genetic Complexity of Cortical Structure: Differences in Genetic and Environmental Factors Influencing Cortical Surface Area and Thickness.

Quantifying the genetic architecture of the cerebral cortex is necessary for understanding disease and changes to the brain across the lifespan. Prior work shows that both surface area (SA) and cortical thickness (CT) are heritable. However, we do not yet understand the extent to which region-specific genetic factors (i.e., independent of global effects) play a dominant role in the regional patterning or inter-regional associations across the cortex. Using a population sample of young adult twins (N = 923), we show that the heritability of SA and CT varies widely across regions, generally independent of measurement error. When global effects are controlled for, we detected a complex pattern of genetically mediated clusters of inter-regional associations, which varied between hemispheres. There were generally weak associations between the SA of different regions, except within the occipital lobe, whereas CT was positively correlated within lobar divisions and negatively correlated across lobes, mostly due to genetic covariation. These findings were replicated in an independent sample of twins and siblings (N = 698) from the Human Connectome Project. The different genetic contributions to SA and CT across regions reveal the value of quantifying sources of covariation to appreciate the genetic complexity of cortical structures.

Nick Martin's Contribution to GxE Research.

The study and identification of genotype-environment interactions (GxE) has been a hot topic in the field of human genetics for several decades. Yet the extent to which GxE contributes to human behavior variability, and its mechanisms, remains largely unknown. Nick Martin has contributed important advances to the field of GxE for human behavior, which include methodological developments, novel analyses and reviews. Here, we will first review Nick's contributions to the GxE research, which started during his PhD and consistently appears in many of his over 1000 publications. Then, we recount a project that led to an article testing the diathesis-stress model for the origins of depression. In this publication, we observed the presence of an interaction between polygenic risk scores for depression (the risk in our 'genotype') and stressful life events (the experiences from our 'environment'), which provided the first empirical support of this model.

A Fast Method for Estimating Statistical Power of Multivariate GWAS in Real Case Scenarios: Examples from the Field of Imaging Genetics.

In GWAS of imaging phenotypes (e.g., by the ENIGMA and CHARGE consortia), the growing number of phenotypes considered presents a statistical challenge that other fields are not experiencing (e.g. psychiatry and the Psychiatric Genetics Consortium). However, the multivariate nature of MRI measurements may also be an advantage as many of the MRI phenotypes are correlated and multivariate methods could be considered. Here, we compared the statistical power of a multivariate GWAS versus the current univariate approach, which consists of multiple univariate analyses. To do so, we used results from twin models to estimate pertinent vectors of SNP effect sizes on brain imaging phenotypes, as well as the residual correlation matrices, necessary to estimate analytically the statistical power. We showed that for subcortical structure volumes and hippocampal subfields, a multivariate GWAS yields similar statistical power to the current univariate approach. Our analytical approach is as accurate but ~ 1000 times faster than simulations and we have released the code to facilitate the investigation of other scenarios, may they be outside the field of imaging genetics.

Twenty-Five and Up (25Up) Study: A New Wave of the Brisbane Longitudinal Twin Study.

The aim of the 25 and Up (25Up) study was to assess a wide range of psychological and behavioral risk factors behind mental illness in a large cohort of Australian twins and their non-twin siblings. Participants had already been studied longitudinally from the age of 12 and most recently in the 19Up study (mean age = 26.1 years, SD = 4.1, range = 20-39). This subsequent wave follows up these twins several years later in life (mean age = 29.7 years, SD = 2.2, range =  22-44). The resulting data set enables additional detailed investigations of genetic pathways underlying psychiatric illnesses in the Brisbane Longitudinal Twin Study (BLTS). Data were collected between 2016 and 2018 from 2540 twins and their non-twin siblings (59% female, including 341 monozygotic complete twin-pairs, 415 dizygotic complete pairs and 1028 non-twin siblings and singletons). Participants were from South-East Queensland, Australia, and the sample was of predominantly European ancestry. The 25Up study collected information on 20 different mental disorders, including depression, anxiety, substance use, psychosis, bipolar and attention-deficit hyper-activity disorder, as well as general demographic information such as occupation, education level, number of children, self-perceived IQ and household environment. In this article, we describe the prevalence, comorbidities and age of onset for all 20 examined disorders. The 25Up study also assessed general and physical health, including physical activity, sleep patterns, eating behaviors, baldness, acne, migraines and allergies, as well as psychosocial items such as suicidality, perceived stress, loneliness, aggression, sleep-wake cycle, sexual identity and preferences, technology and internet use, traumatic life events, gambling and cyberbullying. In addition, 25Up assessed female health traits such as morning sickness, breastfeeding and endometriosis. Furthermore, given that the 25Up study is an extension of previous BLTS studies, 86% of participants have already been genotyped. This rich resource will enable the assessment of epidemiological risk factors, as well as the heritability and genetic correlations of mental conditions.

The Genetic Relationship Between Psychological Distress, Somatic Distress, Affective Disorders, and Substance Use in Young Australian Adults: A Multivariate Twin Study.

Psychological distress (PSYCH), somatic distress (SOMA), affective disorders (AD), and substance use (SU) frequently co-occur. The genetic relationship between PSYCH and SOMA, however, remains understudied. We examined the genetic and environmental influences on these two disorders and their comorbid AD and SU using structural equation modeling. Self-reported PSYCH and SOMA were measured in 1,548 twins using the two subscales of a 12-item questionnaire, the Somatic and Psychological Health Report. Its reliability and psychometric properties were examined. Six ADs, involvement of licit and illicit substance, and two SU disorders were obtained from 1,663-2,132 twins using the World Mental Health Composite International Diagnostic Interview and/or from an online adaption of the same. SU phenotypes (heritability: 49-79%) were found to be more heritable than the affective disorder phenotypes (heritability: 32-42%), SOMA (heritability: 25%), and PSYCH (heritability: 23%). We fit separate non-parametric item response theory models for PSYCH, SOMA, AD, and SU. The IRT scores were used as the refined phenotypes for fitting multivariate genetic models. The best-fitting model showed the similar amount of genetic overlap between PSYCH-AD (genetic correlation rG = 0.49) and SOMA-AD (rG =0.53), as well as between PSYCH-SU (rG = 0.23) and SOMA-SU (rG = 0.25). Unique environmental factors explained 53% to 76% of the variance in each of these four phenotypes, whereas additive genetic factors explained 17% to 46% of the variance. The covariance between the four phenotypes was largely explained by unique environmental factors. Common genetic factor had a significant influence on all the four phenotypes, but they explained a moderate portion of the covariance.

Validation and psychometric properties of the Somatic and Psychological HEalth REport (SPHERE) in a young Australian-based population sample using non-parametric item response theory.

BACKGROUND: The Somatic and Psychological HEalth REport (SPHERE) is a 34-item self-report questionnaire that assesses symptoms of mental distress and persistent fatigue. As it was developed as a screening instrument for use mainly in primary care-based clinical settings, its validity and psychometric properties have not been studied extensively in population-based samples. METHODS: We used non-parametric Item Response Theory to assess scale validity and item properties of the SPHERE-34 scales, collected through four waves of the Brisbane Longitudinal Twin Study (N = 1707, mean age = 12, 51% females; N = 1273, mean age = 14, 50% females; N = 1513, mean age = 16, 54% females, N = 1263, mean age = 18, 56% females). We estimated the heritability of the new scores, their genetic correlation, and their predictive ability in a sub-sample (N = 1993) who completed the Composite International Diagnostic Interview. RESULTS: After excluding items most responsible for noise, sex or wave bias, the SPHERE-34 questionnaire was reduced to 21 items (SPHERE-21), comprising a 14-item scale for anxiety-depression and a 10-item scale for chronic fatigue (3 items overlapping). These new scores showed high internal consistency (alpha > 0.78), moderate three months reliability (ICC = 0.47-0.58) and item scalability (Hi > 0.23), and were positively correlated (phenotypic correlations r = 0.57-0.70; rG = 0.77-1.00). Heritability estimates ranged from 0.27 to 0.51. In addition, both scores were associated with later DSM-IV diagnoses of MDD, social anxiety and alcohol dependence (OR in 1.23-1.47). Finally, a post-hoc comparison showed that several psychometric properties of the SPHERE-21 were similar to those of the Beck Depression Inventory. CONCLUSIONS: The scales of SPHERE-21 measure valid and comparable constructs across sex and age groups (from 9 to 28 years). SPHERE-21 scores are heritable, genetically correlated and show good predictive ability of mental health in an Australian-based population sample of young people.

Genetics and brain morphology.

A wealth of empirical evidence is accumulating on the genetic mediation of brain structure phenotypes. This comes from twin studies that assess heritability and genetic covariance between traits, candidate gene associations, and genome-wide association studies (GWAS) that can identify specific genetic variants. Here we review the major findings from each of these approaches and consider how they inform on the genetic architecture of brain structure. The findings from twin studies show there is a strong genetic influence (heritability) on brain structure, and overlap of genetic effects (pleiotropy) between structures, and between structure and cognition. However, there is also evidence for genetic specificity, with distinct genetic effects across some brain regions. Candidate gene associations show little convergence; most have been under powered to detect effect sizes of the magnitude now expected. GWAS have identified 19 genetic variants for brain structure, though no replicated associations account for more than 1% of the variance. Together these studies are revealing new insights into the genetic architecture of brain morphology. As the scope of inquiry broadens, including measures that capture the complexity of the brain, along with larger samples and new analyses, such as genome-wide common trait analysis (GCTA) and polygenic scores, which combine variant effects for a phenotype, as well as whole-genome sequencing, more genetic variants for brain structure will be identified. Increasingly, large-scale multi-site studies will facilitate this next wave of studies, and promise to enhance our understanding of the etiology of variation in brain morphology, as well as brain disorders.

Heritability of head motion during resting state functional MRI in 462 healthy twins.

Head motion (HM) is a critical confounding factor in functional MRI. Here we investigate whether HM during resting state functional MRI (RS-fMRI) is influenced by genetic factors in a sample of 462 twins (65% female; 101 MZ (monozygotic) and 130 DZ (dizygotic) twin pairs; mean age: 21 (SD = 3.16), range 16-29). Heritability estimates for three HM components-mean translation (MT), maximum translation (MAXT) and mean rotation (MR)-ranged from 37 to 51%. We detected a significant common genetic influence on HM variability, with about two-thirds (genetic correlations range 0.76-1.00) of the variance shared between MR, MT and MAXT. A composite metric (HM-PC1), which aggregated these three, was also moderately heritable (h(2) = 42%). Using a sub-sample (N = 35) of the twins we confirmed that mean and maximum translational and rotational motions were consistent "traits" over repeated scans (r = 0.53-0.59); reliability was even higher for the composite metric (r = 0.66). In addition, phenotypic and cross-trait cross-twin correlations between HM and resting state functional connectivities (RS-FCs) with Brodmann areas (BA) 44 and 45, in which RS-FCs were found to be moderately heritable (BA44: h(2) = 0.23 (sd = 0.041), BA45: h(2) = 0.26 (sd = 0.061)), indicated that HM might not represent a major bias in genetic studies using FCs. Even so, the HM effect on FC was not completely eliminated after regression. HM may be a valuable endophenotype whose relationship with brain disorders remains to be elucidated.

A quantified comparison of cortical atlases on the basis of trait morphometricity.

BACKGROUND: Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences. METHODS: Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas. RESULTS: Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated. DISCUSSION: Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.

Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability.

The temporo-basal region of the human brain is composed of the collateral, the occipito-temporal, and the rhinal sulci. We manually rated (using a novel protocol) the connections between rhinal/collateral (RS-CS), collateral/occipito-temporal (CS-OTS) and rhinal/occipito-temporal (RS-OTS) sulci, using the MRI of nearly 3400 individuals including around 1000 twins. We reported both the associations between sulcal polymorphisms as well with a wide range of demographics (e.g. age, sex, handedness). Finally, we also estimated the heritability, and the genetic correlation between sulcal connections. We reported the frequency of the sulcal connections in the general population, which were hemisphere dependent. We found a sexual dimorphism of the connections, especially marked in the right hemisphere, with a CS-OTS connection more frequent in females (approximately 35-40% versus 20-25% in males) and an RS-CS connection more common in males (approximately 40-45% versus 25-30% in females). We confirmed associations between sulcal connections and characteristics of incomplete hippocampal inversion (IHI). We estimated the broad sense heritability to be 0.28-0.45 for RS-CS and CS-OTS connections, with hints of dominant contribution for the RS-CS connection. The connections appeared to share some of their genetic causing factors as indicated by strong genetic correlations. Heritability appeared much smaller for the (rarer) RS-OTS connection.

The role of critical immune genes in brain disorders: insights from neuroimaging immunogenetics.

Genetic variants in the human leukocyte antigen and killer cell immunoglobulin-like receptor regions have been associated with many brain-related diseases, but how they shape brain structure and function remains unclear. To identify the genetic variants in HLA and KIR genes associated with human brain phenotypes, we performed a genetic association study of ∼30 000 European unrelated individuals using brain MRI phenotypes generated by the UK Biobank (UKB). We identified 15 HLA alleles in HLA class I and class II genes significantly associated with at least one brain MRI-based phenotypes (P < 5 × 10-8). These associations converged on several main haplotypes within the HLA. In particular, the human leukocyte antigen alleles within an ancestral haplotype 8.1 were associated with multiple MRI measures, including grey matter volume, cortical thickness (TH) and diffusion MRI (dMRI) metrics. These alleles have been strongly associated with schizophrenia. Additionally, associations were identified between HLA-DRB1*04∼DQA1*03:01∼DQB1*03:02 and isotropic volume fraction of diffusion MRI in multiple white matter tracts. This haplotype has been reported to be associated with Parkinson's disease. These findings suggest shared genetic associations between brain MRI biomarkers and brain-related diseases. Additionally, we identified 169 associations between the complement component 4 (C4) gene and imaging phenotypes. We found that C4 gene copy number was associated with cortical TH and dMRI metrics. No KIR gene copy numbers were associated with image-derived phenotypes at genome-wide threshold. To address the multiple testing burden in the phenome-wide association study, we performed a multi-trait association analysis using trait-based association test that uses extended Simes procedure and identified MRI image-specific associations. This study contributes to insight into how critical immune genes affect brain-related traits as well as the development of neurological and neuropsychiatric disorders.