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. 2024 Apr 17;14(1):8848.
doi: 10.1038/s41598-024-59440-6.

ANTsX neuroimaging-derived structural phenotypes of UK Biobank

Nicholas J Tustison  1   2 , Michael A Yassa  3 , Batool Rizvi  3 , Philip A Cook  4 , Andrew J Holbrook  5 , Mithra T Sathishkumar  3 , Mia G Tustison  6 , James C Gee  4 , James R Stone  7 , Brian B Avants  7
Affiliations

ANTsX neuroimaging-derived structural phenotypes of UK Biobank

Nicholas J Tustison et al. Sci Rep. .

Abstract

UK Biobank is a large-scale epidemiological resource for investigating prospective correlations between various lifestyle, environmental, and genetic factors with health and disease progression. In addition to individual subject information obtained through surveys and physical examinations, a comprehensive neuroimaging battery consisting of multiple modalities provides imaging-derived phenotypes (IDPs) that can serve as biomarkers in neuroscience research. In this study, we augment the existing set of UK Biobank neuroimaging structural IDPs, obtained from well-established software libraries such as FSL and FreeSurfer, with related measurements acquired through the Advanced Normalization Tools Ecosystem. This includes previously established cortical and subcortical measurements defined, in part, based on the Desikan-Killiany-Tourville atlas. Also included are morphological measurements from two recent developments: medial temporal lobe parcellation of hippocampal and extra-hippocampal regions in addition to cerebellum parcellation and thickness based on the Schmahmann anatomical labeling. Through predictive modeling, we assess the clinical utility of these IDP measurements, individually and in combination, using commonly studied phenotypic correlates including age, fluid intelligence, numeric memory, and several other sociodemographic variables. The predictive accuracy of these IDP-based models, in terms of root-mean-squared-error or area-under-the-curve for continuous and categorical variables, respectively, provides comparative insights between software libraries as well as potential clinical interpretability. Results demonstrate varied performance between package-based IDP sets and their combination, emphasizing the need for careful consideration in their selection and utilization.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Illustration of the IDPs generated with ANTsX ecosystem tools. Using the gradient-distortion corrected versions of the T1 and FLAIR images, several categories of IDPs were tabulated. These include global brain and tissue volumes, cortical thicknesses averaged over the 62 DKT regions, WMH intensity load per lobe based on the SYSU algorithm, cortical and subcortical volumes from the DKT labeling, MTL regional volumes using DeepFLASH, and morphological cerebellum quantities.
Figure 2
Figure 2
Results for predicting Age (left) and Genetic Sex (right) using both ANTsX and FreeSurfer cortical thickness data averaged over the 62 cortical regions of the DKT parcellation. RMSE and AUC were used to quantify the predictive accuracy of Age and Genetic Sex, respectively.
Figure 3
Figure 3
Comparison of machine learning frameworks for training and prediction of selected continuous UKBB sociodemographic continuous variables (cf. Table 1) with the different IDP sets and their combination (FSL, FreeSurfer, ANTsX, and All).
Figure 4
Figure 4
Regression regions defined by the linear models represented in Fig. 3 showing the relationship between the predicted and actual target values. We also plot the median line for each model-based grouping as defined by the slope and list the average R2 values for each IDP set.
Figure 5
Figure 5
Comparison of prediction accuracy of selected binary and multilabel categorical UKBB sociodemographic variables (cf. Table 1) with the different IDP sets and their combination (FSL, FreeSurfer, ANTsX, and All). Smoking and Alcohol target variables have more than two labels.
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