Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

Pomponio, Raymond; Erus, Güray; Habes, Mohamad; Doshi, Jimit; Srinivasan, Dhivya; Mamourian, Elizabeth; Bashyam, Vishnu; Nasrallah, Ilya M.; Satterthwaite, Theodore D.; Fan, Yong; Launer, Lenore J.; Masters, Colin L.; Maruff, Paul; Zhuo, Chuanjun; Völzke, Henry; Johnson, Sterling C.; Fripp, Jürgen; Koutsouleris, Nikolaos; Wolf, Daniel H.; Gur, Raquel E.; Gur, Ruben C.; Morris, John C.; Albert, Marilyn; Grabe, Hans J.; Resnick, Susan M.; Bryan, R. Nick; Wolk, David A.; Shinohara, Russell T.; Shou, Haochang; Davatzikos, Christos

doi:10.1016/j.neuroimage.2019.116450

Cited by 350 publications

(377 citation statements)

References 57 publications

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“…Many harmonization efforts are underway to allow cross-scanner transfer, which would allow the adaptation of these normative charts to data collected on different scanners within the UK Biobank, as well as data collected on new scanners elsewhere. These harmonization methods include ComBat 28 and its variants (ComBat-GAM 29 , longitudinal ComBat 30 , and CovBat 31 ), hierarchical Bayesian models that model the mean, variance, and other moments of the cohort data 32 , and transfer learning models. Additional approaches can be applied to harmonize the diffusion MRI data directly 33,34 , and they deserve further study, as they may further sensitize and boost statistical power in analyses such as those presented in this paper.…”

Section: Discussionmentioning

confidence: 99%

Age and Sex Effects on Advanced White Matter Microstructure Measures in 15,628 Older Adults: A UK Biobank Study

Lawrence

Nabulsi

Santhalingam

et al. 2020

Preprint

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The brain’s white matter microstructure, as assessed using diffusion-weighted MRI (DWI), changes significantly with age and also exhibits significant sex differences. Here we examined the ability of a traditional diffusivity metric (fractional anisotropy derived from diffusion tensor imaging, DTI-FA) and advanced diffusivity metrics (fractional anisotropy derived from the tensor distribution function, TDF-FA; neurite orientation dispersion and density imaging measures of intra-cellular volume fraction, NODDI-ICVF; orientation dispersion index, NODDI-ODI; and isotropic volume fraction, NODDI-ISOVF) to detect sex differences in white matter aging. We also created normative aging reference curves based on sex. Diffusion tensor imaging (DTI) applies a single-tensor diffusion model to single-shell DWI data, while the tensor distribution function (TDF) fits a continuous distribution of tensors to single-shell DWI data. Neurite orientation dispersion and density imaging (NODDI) fits a multi-compartment model to multi-shell DWI data to distinguish intra- and extra-cellular contributions to diffusion. We analyzed these traditional and advanced diffusion measures in a large population sample available through the UK Biobank (15,394 participants; age-range: 45-80 years) by using linear regression and fractional polynomials. Advanced diffusivity metrics (NODDI-ODI, NODDI-ISOVF, TDF-FA) detected significant sex differences in aging, whereas a traditional metric (DTI-FA) did not. These findings suggest that future studies examining sex differences in white matter aging may benefit from including advanced diffusion measures.

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Section: Discussionmentioning

confidence: 99%

Age and Sex Effects on Advanced White Matter Microstructure Measures in 15,628 Older Adults: A UK Biobank Study

Lawrence

Nabulsi

Santhalingam

et al. 2020

Preprint

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“…Finally, an unfortunate drawback to most neuroscience discoveries is the limited translation of results to clinical practice. Establishing biological benchmarks for sex-specific brain changes across the lifespan can break this pattern [Pomponio et al, 2020]. The ENIGMA consortium has received federal funding to develop normative charts of brain structure in males and females across the lifespan using multimodal neuroimaging [Dima et al, 2020;Wierenga et al, 2020].…”

Section: Using What We Know To Inform Where We Go: Roles For Big Datamentioning

confidence: 99%

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Salminen¹,

Tubi²,

Bright³

et al. 2020

Preprint

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Sex differences are found in the incidence and expression of psychiatric and neurodegenerative conditions, and many studies suggest these differences are influenced by innate biological differences between males and females and risk factors that interact with these differences. However, few studies have used neuroimaging to examine brain signatures of disease separately by sex, and many studies of sex differences have been based on small samples and their findings have not been replicated in larger cohorts. Large-scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analyses (ENIGMA) consortium, the UK Biobank, Human Connectome Project, and others offer an unprecedented source of power to address important questions about the role of sex as a risk or protective factor for suboptimal brain health, as well as sex-specific neuroimaging phenotypes in brain-related illnesses. Here we review the existing neuroimaging literature on sex differences in the human brain in healthy adults and those with the most common and debilitating psychiatric and age-related neurodegenerative conditions. Finally, we discuss key gaps in this literature and opportunities of large-scale collaborative efforts to identify, characterize, and explain how biological sex influences the humanbrain in health and disease.

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“…It has recently been adapted to neuroimaging studies and applied to diverse data types including diffusion tensor imaging (DTI) ( Fortin et al, 2017 ), cortical thicknesses ( Fortin et al, 2018 ), functional connectivity measurements ( Yu et al, 2018 ), and radiomic features derived from positron emission tomography (PET) imaging ( Orlhac et al, 2018 ). ComBat has also recently been extended to cross-sectional studies of structural brain changes across the lifespan using a generalized additive model framework ( Pomponio et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data

et al. 2020

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While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

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Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

Cited by 350 publications

References 57 publications

Age and Sex Effects on Advanced White Matter Microstructure Measures in 15,628 Older Adults: A UK Biobank Study

Age and Sex Effects on Advanced White Matter Microstructure Measures in 15,628 Older Adults: A UK Biobank Study

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data

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