Centre for Youth Mental Health - Research Publications
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ItemNo Preview AvailableAccommodating site variation in neuroimaging data using normative and hierarchical Bayesian models(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2022-10-29)The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
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ItemSite effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses(FRONTIERS MEDIA SA, 2022-10-31)Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.
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ItemNo Preview AvailableDeviations From Normative Age-Brain Associations in Over 3,000 Individuals With Major Depressive Disorder(ELSEVIER SCIENCE INC, 2019-05-15)Background: Major depressive disorder (MDD) is a complex heterogeneous disorder. Identifying brain alterations as indi- vidual deviations from normative patterns of brain-age asso- ciations, instead of patient group mean differences, can provide important insights into heterogeneous patterns of brain abnormalities observed in MDD. Methods: We estimated normative models of (1) age pre- dicting individual structural brain measures, and (2) structural brain measures predicting age (Brain Age model) using ma- chine learning in healthy individuals (N¼2,515) from the ENIGMA MDD consortium. We applied model parameters to independent samples of healthy individuals (N¼2,513) and MDD patients (N¼3,433) to obtain predicted values of brain structure (model 1) and age (model 2). Z-scores quantifying differences between predictive and true values were calcu- lated, representing individual deviations from the normative range. Results: The estimated normative models showed good model fit in the training sample; e.g. a correlation of R¼0.86 between actual and predicted age for the Brain Age Model, and good generalization to independent healthy and MDD samples. We identified heterogeneous patterns of brain deviations in MDD patients (model 1). Patients with more extreme deviations showed different clinical characteristics compared to patients residing within the normative range. Additionally, patients were estimated on average w1 year older than controls (model 2), but we also observed large between-person variation in brain age gaps. Further ana- lyses showed associations between brain age gap and clinical symptoms. Conclusions: Our work shows substantial heterogeneity in deviations from normal age-related variation in brain structure in individuals with MDD. The impact of and solutions for con- founding effects of scan site will also be discussed.
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ItemIndividual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort(SPRINGERNATURE, 2021-07)The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for individual patients and evaluate whether the loci of individual deviations recapitulated group-average brain maps of schizophrenia pathology. For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusion-weighted and structural MRI from healthy adults (n = 195). Individuals with schizophrenia (n = 322) were classified as either within the normative range for healthy individuals of the same age and sex (5-95% percentiles), infra-normal (<5% percentile) or supra-normal (>95% percentile). Repeating this classification for each tract and region yielded a deviation map for each individual. Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions. Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region. Furthermore, 79% of patients showed infra-normal deviations for at least one locus (healthy individuals: 59 ± 2%, p < 0.001). Thus, while infra-normal deviations were common among patients, their anatomical loci were highly inconsistent between individuals. Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r = -0.17, p = 0.006). We conclude that anatomical loci of schizophrenia-related changes are highly heterogeneous across individuals to the extent that group-consensus pathological maps are not representative of most individual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.