Neuroimaging studies within Cognitive Genetics Collaborative Research Organization aiming to replicate and extend works of <scp>ENIGMA</scp>

Koshiyama, Daisuke; Miura, Kenichiro; Nemoto, Kiyotaka; Okada, Naohiro; Matsumoto, Junya; Fukunaga, Masaki; Hashimoto, Ryota

doi:10.1002/hbm.25040

Cited by 28 publications

(27 citation statements)

References 101 publications

(119 reference statements)

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“…Thus, the existing GLM methods eliminate biologically meaningless and meaningful biases. Recently, several large‐scale investigations have considered the application of the meta‐analytic approach to multisite datasets (Koshiyama et al, 2020; Okada et al, 2016; Van Erp et al, 2018). However, this approach is limited in that (a) the publication bias, wherein negative results are less likely to be published, reveals positive results in the original data to be combined; (b) the quality of brain images and clinical assessments varies significantly; (c) individual‐based statistics cannot be obtained; and (d) survey literature and/or records are missing.…”

Section: Introductionmentioning

confidence: 99%

Comparison of traveling‐subject and ComBat harmonization methods for assessing structural brain characteristics

Maikusa

Zhu

Uematsu

et al. 2021

Human Brain Mapping

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Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases—caused by site differences in scanner/image‐acquisition protocols—negatively influence the reliability and reproducibility of image‐analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling‐subject (TS) dataset including 56 T1‐weighted MRI scans of 20 healthy participants in three different MRI procedures—20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively—was considered to compare the reproducibility of TS‐GLM, ComBat, and TS‐ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement‐bias indicator. The measurement‐bias reduction realized with different methods was evaluated by comparing test–retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test–retest datasets. The results revealed that TS‐GLM and TS‐ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS‐based harmonization outperforms ComBat for measurement‐bias reduction and is optimal for MRI data in well‐prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large‐scale study is an interesting future problem.

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Section: Introductionmentioning

confidence: 99%

Comparison of traveling‐subject and ComBat harmonization methods for assessing structural brain characteristics

Maikusa

Zhu

Uematsu

et al. 2021

Human Brain Mapping

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“…These findings show how large-scale consortia can rigorously address long-standing questions in the history of neuroscience, including hypotheses of disrupted brain asymmetry in disorders such as schizophrenia (hypothesized by Crow, 1990, with mixed evidence over the years). Koshiyama, Miura, et al (2020) describe the parallel development of the COCORO consortium in Japan. They have analyzed brain MRI and diffusion tensor imaging (DTI) data from a range of psychiatric disorders using the same protocols as used in ENIGMA .…”

Section: Normal Brain Variation and Statistical Charts For Brain Aging Across The Lifespanmentioning

confidence: 99%

The Enhancing NeuroImaging Genetics through Meta‐Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping

Thompson

Jahanshad

Schmaal

et al. 2021

Human Brain Mapping

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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 wellpowered 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 largescale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke.

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“…The diverse ethnic, racial, geographic, and clinical demography of consortium data has provided results that are more representative of the wider population while also permitting exploration of clinical and neurobiological subtypes of neuropsychiatric disorders (Dennis et al, 2020; Thompson et al, 2020). Neuroimaging results generated by consortia are more robust and reproducible than studies that are generated by a single laboratory (Koshiyama et al, 2020), provided that consortia apply uniform methods to data originating from multiple sites and scanners.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Sun

Rakesh

Haswell

et al. 2021

Preprint

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Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,343 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,067 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM were more sensitive to the detection of significant case-control differences in regional cortical thickness (Chi2(3) = 34.339, p < 0.001), and case-control differences in age-related cortical thinning (Chi2(3) = 15.128, p = 0.002). Specifically, ComBat-GAM led to larger effect size estimates of cortical thickness reductions (corrected p-values < 0.001), smaller age-appropriate declines (corrected p-values < 0.001), and lower female to male contrast (corrected p-values < 0.001) in cases compared to controls relative to other harmonization methods. Harmonization with ComBat-GAM also led to greater estimates of age-related declines in cortical thickness (corrected p-values < 0.001) in both cases and controls compared to other harmonization methods. Our results support the use of ComBat-GAM for harmonizing cortical thickness data aggregated from multiple sites and scanners to minimize confounds and increase statistical power.

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Neuroimaging studies within Cognitive Genetics Collaborative Research Organization aiming to replicate and extend works of ENIGMA

Cited by 28 publications

References 101 publications

Comparison of traveling‐subject and ComBat harmonization methods for assessing structural brain characteristics

Comparison of traveling‐subject and ComBat harmonization methods for assessing structural brain characteristics

The Enhancing NeuroImaging Genetics through Meta‐Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

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