Buser, N. J. | bioRxiv Science

bioRxiv2020

Quantifying Numerical and Spatial Reliability of Amygdala and Hippocampal Subdivisions in FreeSurfer

On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N=923), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable: [~]95% of hippocampal subfields have "excellent" numerical reliability (ICCs[≥]0.90), however, only 67% of amygdala subnuclei met this same threshold. Spatial reliability was similarly reasonable, with 58% of hippocampal subfields and 44% of amygdala subnuclei having Dice coefficients[≥]0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.

neuroscience↗

bioRxiv2019

Variations in Structural MRI Quality Impact Measures of Brain Anatomy: Relations with Age

Subject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy or indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to more directly measure image quality from T1-weighted images to understand if these measures of image quality: 1) relate to rigorous quality-control checks visually completed by human raters; 2) are associated with sociodemographic variables of interest; 3) influence regional estimates of cortical surface area, cortical thickness, and subcortical volumes from the commonly-used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range (N=388; ages 5-21). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical surface area, cortical thickness, and subcortical volumes from multiple regions across the brain ([~]23.4% of all areas investigated). We believe these results are important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.

neuroscience↗