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Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium

Kong, Xiang-Zhen ; Mathias, Samuel R. ; Guadalupe, Tulio ; [et al.]

Proceedings of the National Academy of Sciences ; 2018

In: Proceedings of the National Academy of Sciences Vol. 115, No. 22 ( 2018-05-29)

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In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 22 ( 2018-05-29)

Abstract: Hemispheric asymmetry is a cardinal feature of human brain organization. Altered brain asymmetry has also been linked to some cognitive and neuropsychiatric disorders. Here, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium presents the largest-ever analysis of cerebral cortical asymmetry and its variability across individuals. Cortical thickness and surface area were assessed in MRI scans of 17,141 healthy individuals from 99 datasets worldwide. Results revealed widespread asymmetries at both hemispheric and regional levels, with a generally thicker cortex but smaller surface area in the left hemisphere relative to the right. Regionally, asymmetries of cortical thickness and/or surface area were found in the inferior frontal gyrus, transverse temporal gyrus, parahippocampal gyrus, and entorhinal cortex. These regions are involved in lateralized functions, including language and visuospatial processing. In addition to population-level asymmetries, variability in brain asymmetry was related to sex, age, and intracranial volume. Interestingly, we did not find significant associations between asymmetries and handedness. Finally, with two independent pedigree datasets ( n = 1,443 and 1,113, respectively), we found several asymmetries showing significant, replicable heritability. The structural asymmetries identified and their variabilities and heritability provide a reference resource for future studies on the genetic basis of brain asymmetry and altered laterality in cognitive, neurological, and psychiatric disorders.

Type of Medium: Online Resource

ISSN: 0027-8424 , 1091-6490

URL: Article

DOI: 10.1073/pnas.1718418115

RVK:

TA 1000

RVK:

WA 15000

Language: English

Publisher: Proceedings of the National Academy of Sciences

Publication Date: 2018

detail.hit.zdb_id: 209104-5

detail.hit.zdb_id: 1461794-8

SSG: 11

SSG: 12

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Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years

Dima, Danai ; Modabbernia, Amirhossein ; Papachristou, Efstathios ; [et al.]

Wiley ; 2022

In: Human Brain Mapping Vol. 43, No. 1 ( 2022-01), p. 452-469

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In: Human Brain Mapping, Wiley, Vol. 43, No. 1 ( 2022-01), p. 452-469

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

Type of Medium: Online Resource

ISSN: 1065-9471 , 1097-0193

URL: Issue

URL: Article

DOI: 10.1002/hbm.v43.1

DOI: 10.1002/hbm.25320

Language: English

Publisher: Wiley

Publication Date: 2022

detail.hit.zdb_id: 1492703-2

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In vivo hippocampal subfield volumes in bipolar disorder—A mega‐analysis from The Enhancing Neuro Imaging Genetics through Meta‐Analysis Bipolar Disorder Working Group

Haukvik, Unn K. ; Gurholt, Tiril P. ; Nerland, Stener ; [et al.]

Wiley ; 2022

In: Human Brain Mapping Vol. 43, No. 1 ( 2022-01), p. 385-398

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In: Human Brain Mapping, Wiley, Vol. 43, No. 1 ( 2022-01), p. 385-398

Abstract: The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 ( d = −0.18), CA2/3 ( d = −0.11), CA4 ( d = −0.19), molecular layer ( d = −0.21), granule cell layer of dentate gyrus ( d = −0.21), hippocampal tail ( d = −0.10), subiculum ( d = −0.15), presubiculum ( d = −0.18), and hippocampal amygdala transition area ( d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Type of Medium: Online Resource

ISSN: 1065-9471 , 1097-0193

URL: Issue

URL: Article

DOI: 10.1002/hbm.v43.1

DOI: 10.1002/hbm.25249

Language: English

Publisher: Wiley

Publication Date: 2022

detail.hit.zdb_id: 1492703-2

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Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years

Frangou, Sophia ; Modabbernia, Amirhossein ; Williams, Steven C. R. ; [et al.]

Wiley ; 2022

In: Human Brain Mapping Vol. 43, No. 1 ( 2022-01), p. 431-451

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In: Human Brain Mapping, Wiley, Vol. 43, No. 1 ( 2022-01), p. 431-451

Abstract: Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large‐scale studies. In response, we used cross‐sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to infer age‐related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta‐analysis and one‐way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Type of Medium: Online Resource

ISSN: 1065-9471 , 1097-0193

URL: Issue

URL: Article

DOI: 10.1002/hbm.v43.1

DOI: 10.1002/hbm.25364

Language: English

Publisher: Wiley

Publication Date: 2022

detail.hit.zdb_id: 1492703-2

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Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Nunes, Abraham ; Schnack, Hugo G. ; Ching, Christopher R. K. ; [et al.]

Springer Science and Business Media LLC ; 2020

In: Molecular Psychiatry Vol. 25, No. 9 ( 2020-09), p. 2130-2143

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In: Molecular Psychiatry, Springer Science and Business Media LLC, Vol. 25, No. 9 ( 2020-09), p. 2130-2143

Abstract: Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Type of Medium: Online Resource

ISSN: 1359-4184 , 1476-5578

URL: Article

DOI: 10.1038/s41380-018-0228-9

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2020

detail.hit.zdb_id: 1502531-7

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