| 1. |
|
|
| 2. |
- Elvsashagen, T, et al.
(author)
-
The genetic architecture of human brainstem structures and their involvement in common brain disorders
- 2020
-
In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 4016-
-
Journal article (peer-reviewed)abstract
- Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson’s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.
|
|
| 3. |
|
|
| 4. |
- Schwarz, E, et al.
(author)
-
Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder
- 2019
-
In: Translational psychiatry. - : Springer Science and Business Media LLC. - 2158-3188. ; 9:1, s. 12-
-
Journal article (peer-reviewed)abstract
- Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
- Sønderby, Ida E., et al.
(author)
-
1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans
- 2021
-
In: Translational Psychiatry. - : Nature Publishing Group. - 2158-3188. ; 11:1
-
Journal article (peer-reviewed)abstract
- Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.
|
|
| 9. |
|
|
| 10. |
- van der Meer, Dennis, et al.
(author)
-
Association of Copy Number Variation of the 15q11.2 BP1-BP2 Region With Cortical and Subcortical Morphology and Cognition
- 2020
-
In: JAMA psychiatry. - : American Medical Association (AMA). - 2168-6238 .- 2168-622X. ; 77:4, s. 420-430
-
Journal article (peer-reviewed)abstract
- Importance: Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities.Objective: To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance.Design, Setting, and Participants: In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019.Main Outcomes and Measures: The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort.Results: Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = -0.41; SE, 0.08; P = 4.9 × 10-8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10-7), and a smaller nucleus accumbens (Cohen d = -0.27; SE, 0.07; P = 7.3 × 10-5). There was also a significant negative dose response on cortical thickness (β = -0.24; SE, 0.05; P = 6.8 × 10-7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks.Conclusions and Relevance: These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders.
|
|
| 11. |
- van der Meer, D, et al.
(author)
-
Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes
- 2020
-
In: Molecular psychiatry. - : Springer Science and Business Media LLC. - 1476-5578 .- 1359-4184. ; 25:11, s. 3053-3065
-
Journal article (peer-reviewed)abstract
- The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer’s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields’ genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p < 1 × 10–16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.
|
|
| 12. |
|
|
| 13. |
|
|
| 14. |
|
|
| 15. |
|
|
| 16. |
|
|
| 17. |
|
|
| 18. |
|
|
| 19. |
|
|
| 20. |
|
|
| 21. |
|
|
| 22. |
|
|
| 23. |
- Alnaes, Dag, et al.
(author)
-
Brain Heterogeneity in Schizophrenia and Its Association With Polygenic Risk
- 2019
-
In: JAMA psychiatry. - : AMER MEDICAL ASSOC. - 2168-6238 .- 2168-622X. ; 76:7, s. 739-748
-
Journal article (peer-reviewed)abstract
- ImportanceBetween-individual variability in brain structure is determined by gene-environment interactions, possibly reflecting differential sensitivity to environmental and genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner cortices and smaller subcortical volumes in patients with schizophrenia. However, group-level comparisons may mask considerable within-group heterogeneity, which has largely remained unnoticed in the literature. ObjectivesTo compare brain structural variability between individuals with schizophrenia and healthy controls and to test whether respective variability reflects the polygenic risk score (PRS) for schizophrenia in an independent sample of healthy controls. Design, Setting, and ParticipantsThis case-control and polygenic risk analysis compared MRI-derived cortical thickness and subcortical volumes between healthy controls and patients with schizophrenia across 16 cohorts and tested for associations between PRS and MRI features in a control cohort from the UK Biobank. Data were collected from October 27, 2004, through April 12, 2018, and analyzed from December 3, 2017, through August 1, 2018. Main Outcomes and MeasuresMean and dispersion parameters were estimated using double generalized linear models. Vertex-wise analysis was used to assess cortical thickness, and regions-of-interest analyses were used to assess total cortical volume, total surface area, and white matter, subcortical, and hippocampal subfield volumes. Follow-up analyses included within-sample analysis, test of robustness of the PRS threshold, population covariates, outlier removal, and control for image quality. ResultsA comparison of 1151 patients with schizophrenia (mean [SD] age,33.8[10.6] years; 68.6% male [n=790] and 31.4% female [n=361]) with 2010 healthy controls (mean [SD] age,32.6[10.4] years; 56.0% male [n=1126] and 44.0% female [n=884]) revealed higher heterogeneity in schizophrenia for cortical thickness and area (t = 3.34), cortical (t=3.24) and ventricle (t range, 3.15-5.78) volumes, and hippocampal subfields (t range, 2.32-3.55). In the UK Biobank sample of 12 490 participants (mean [SD] age,55.9 [7.5] years; 48.2% male [n=6025] and 51.8% female [n=6465]), higher PRS was associated with thinner frontal and temporal cortices and smaller left CA2/3 (t=-3.00) but was not significantly associated with dispersion. Conclusions and RelevanceThis study suggests that schizophrenia is associated with substantial brain structural heterogeneity beyond the mean differences. These findings may reflect higher sensitivity to environmental and genetic perturbations in patients, supporting the heterogeneous nature of schizophrenia. A higher PRS was associated with thinner frontotemporal cortices and smaller hippocampal subfield volume, but not heterogeneity. This finding suggests that brain variability in schizophrenia results from interactions between environmental and genetic factors that are not captured by the PRS. Factors contributing to heterogeneity in frontotemporal cortices and hippocampus are key to furthering our understanding of how genetic and environmental factors shape brain biology in schizophrenia.
|
|
| 24. |
- Córdova-Palomera, Aldo, et al.
(author)
-
Genetic control of variability in subcortical and intracranial volumes
- 2021
-
In: Molecular Psychiatry. - : Nature Publishing Group. - 1359-4184 .- 1476-5578. ; 26:8, s. 3876-3883
-
Journal article (peer-reviewed)abstract
- Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8-89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health.
|
|
| 25. |
- Gurholt, Tiril P., et al.
(author)
-
Population-based body-brain mapping links brain morphology with anthropometrics and body composition
- 2021
-
In: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 11:1
-
Journal article (peer-reviewed)abstract
- Understanding complex body-brain processes and the interplay between adipose tissue and brain health is important for understanding comorbidity between psychiatric and cardiometabolic disorders. We investigated associations between brain structure and anthropometric and body composition measures using brain magnetic resonance imaging (MRI; n=24,728) and body MRI (n=4973) of generally healthy participants in the UK Biobank. We derived regional and global measures of brain morphometry using FreeSurfer and tested their association with (i) anthropometric measures, and (ii) adipose and muscle tissue measured from body MRI. We identified several significant associations with small effect sizes. Anthropometric measures showed negative, nonlinear, associations with cerebellar/cortical gray matter, and brain stem structures, and positive associations with ventricular volumes. Subcortical structures exhibited mixed effect directionality, with strongest positive association for accumbens. Adipose tissue measures, including liver fat and muscle fat infiltration, were negatively associated with cortical/cerebellum structures, while total thigh muscle volume was positively associated with brain stem and accumbens. Regional investigations of cortical area, thickness, and volume indicated widespread and largely negative associations with anthropometric and adipose tissue measures, with an opposite pattern for thigh muscle volume. Self-reported diabetes, hypertension, or hypercholesterolemia were associated with brain structure. The findings provide new insight into physiological body-brain associations suggestive of shared mechanisms between cardiometabolic risk factors and brain health. Whereas the causality needs to be determined, the observed patterns of body-brain relationships provide a foundation for understanding the underlying mechanisms linking psychiatric disorders with obesity and cardiovascular disease, with potential for the development of new prevention strategies.
|
|
