Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject’s sex and age. However, corrections for body size (i.e., height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1 ± 6.6 years old, 125 females). We show that body height correlates with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44 ≤ r ≤ 0.62). Intracranial volume (ICV) correlates with body height (r = 0.46) and the brain volumes and CSA-WM (0.37 ≤ r ≤ 0.77). In comparison, age correlates with cortical GM volume, precentral GM volume, and cortical thickness (-0.21 ≥ r ≥ -0.27). Body weight correlates with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20 ≥ r ≥ -0.23). Body weight further correlates with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r = -0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlates with brain volumes (0.39 ≤ r ≤ 0.64), and with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22 ≥ r ≥ -0.25). Linear mixture of age, sex, or sex and age, explained 2 ± 2%, 24 ± 10%, or 26 ± 10%, of data variance in brain volumetry and SC CSA. The amount of explained variance increased to 33 ± 11%, 41 ± 17%, or 46 ± 17%, when body height, ICV, or body height and ICV were added into the mixture model. In females, the explained variances halved suggesting another unidentified biological factor(s) determining females’ central nervous system (CNS) morphology. In conclusion, body size and ICV are significant biological variables. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure; and body size and ICV should be considered as covariates in statistical analyses. Normalization of different brain regions with ICV diminishes their correlations with body size, but simultaneously amplifies ICV-related variance (r = 0.72 ± 0.07) and suppresses volume variance of the different brain regions (r = 0.12 ± 0.19) in the normalized measurements.

Traditional tissue classification approaches in vivo use voxel intensities from conventional clinical magnetic resonance (MR) images for segmentation, which does not incorporate information about specific aspects of microstructure. With the Clustering for Anatomical Quantification and Evaluation (CAQE) framework, quantitative MRI measures can be used to classify tissue based only on microstructural features with no spatial enforcement, and pathological changes in disease can be evaluated. In this study, maps of whole-brain myelin water fraction, microscopic fractional anisotropy, and tissue heterogeneity were used to classify brain tissue in 25 healthy participants. CAQE was then applied to 25 participants with multiple sclerosis (MS), where tissue classifications indicated areas of increased demyelination and axonal injury in white matter compared with a healthy average tissue classification. Severity scores were derived from tissue classifications to quantify diffuse white matter damage, and correlated significantly with cognitive ability in MS. The CAQE framework can be adapted for other applications and extended to use different quantitative MRI measures.

Adolescents and young adults born with a complex congenital heart defect (CHD) are at risk for executive function (ExF) impairments, which contribute to the psychological and everyday burden of CHD. Cortical dysmaturation has been well described in fetuses and neonates with CHD and early evidence suggests that cortical alterations in thickness, surface area, and gyrification index are non-transient and can be observed in adolescents with CHD. However, cortical alterations have yet to be correlated with ExF deficits in youth with CHD. This study aims to use a data-driven approach to identify the most important cortical features associated with ExF deficits in adolescents and young adults with CHD. To do so, we combined two comparable datasets acquired at the Research Institute of the McGill University Health Centre and the University Children’s Hospital Zurich, each including both youth with CHD and healthy controls. For each participant, a high-resolution T1-weighted magnetic resonance image, a self-reported ExF assessment (the Behaviour Rating Inventory of Executive Function – Adult Scale), and their clinical and demographic characteristics were available. Corticometric Iterative Vertex-Based Estimation of Thickness (CIVET) was used to extract cortical thickness, cortical surface area, and local gyrification index measures. Using orthogonal projective non-negative matrix factorization (OPNMF), we identified non-overlapping spatial components that integrate cortical thickness, cortical surface area, and local gyrification index and capture structural covariance across these features. Behavioral partial least squares correlation (bPLS) analysis was then used to compute correlations between the individual variability in the OPNMF covariance patterns and ExF outcomes for each subject. A total of 56 youth with CHD who underwent cardiopulmonary bypass surgery before 3 years of age and 56 age- and sex-matched healthy controls were included in our analyses. Cortical grey matter volume, cortical thickness, and cortical surface area were found to be significantly reduced in CHD patients compared to controls. OPNMF identified 12 stable cortex-wide components summarizing the inter-subject variability in cortical thickness, cortical surface area, and local gyrification index. bPLS revealed two significant latent variables (LV) accounting for a total of 82.8% of the variance in the sample, each describing distinct patterns between the brain and cognitive data. LV1 summarized a pattern of belonging to the CHD group, worse scores on most Behaviour Rating Inventory of Executive Function – Adult Scale (BRIEF-A) scales, younger age at MRI, and female sex. This pattern was associated with increased cortical thickness, local gyrification index, and decreased cortical surface area in several OPNMF components. Finally, we identified a positive relationship between the LV1 brain-behavior pattern and total aortic cross-clamp time in the CHD group, indicating that longer aortic cross-clamp time was associated with worse neuropsychological outcomes. In this study, we uncover novel multivariate relationships between ExF and alterations in cortical thickness, surface area, and local gyrification index in adolescents and young adults with CHD using a data-driven approach. Although our findings highlight the important role played by the cortex in higher-order cognitive processes, future studies are needed to elucidate the individual contribution of individual and clinical attributes into the deficits observed in this population.