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Quantitative susceptibility mapping (QSM) is an MRI-based, computational method for anatomically localizing and measuring concentrations of specific biomarkers in tissue such as iron. Growing research suggests QSM is a viable method for evaluating the impact of iron overload in neurological disorders and on cognitive performance in aging. Several software toolboxes are currently available to reconstruct QSM maps from 3D GRE MR Images. However, few if any software packages currently exist that offer fully automated pipelines for QSM-based data analyses: from DICOM images to region-of-interest (ROI) based QSM values. Even less QSM-based software exist that offer quality control measures for evaluating the QSM output. Here, we address these gaps in the field by introducing and demonstrating the reliability and external validity of Ironsmith; an open-source, fully automated pipeline for creating and processing QSM maps, extracting QSM values from subcortical and cortical brain regions (89 ROIs) and evaluating the quality of QSM data using SNR measures and assessment of outlier regions on phase images. Ironsmith also features automatic filtering of QSM outlier values and precise CSF-only QSM reference masks that minimize partial volume effects. Testing of Ironsmith revealed excellent intra- and inter-rater reliability. Finally, external validity of Ironsmith was demonstrated via an anatomically selective relationship between motor performance and Ironsmith-derived QSM values in motor cortex. In sum, Ironsmith provides a freely-available, reliable, turn-key pipeline for QSM-based data analyses to support research on the impact of brain iron in aging and neurodegenerative disease.

Cerebrovascular reactivity (CVR), which measures the ability of cerebral blood vessels to dilate or constrict in response to vasoactive stimuli such as CO2 inhalation, is an important index of the brain's vascular health. Quantification of CVR using BOLD MRI with hypercapnia challenge has shown great promises in research and clinical studies. However, in order for it to be used as a potential imaging biomarker in large-scale and multi-site studies, the reliability of CO2-CVR quantification across different MRI acquisition platforms and researchers/raters must be examined. The goal of this report from the MarkVCID small vessel disease biomarkers consortium is to evaluate the reliability of CO2-CVR quantification in three studies. First, the inter-rater reliability of CO2-CVR data processing was evaluated by having raters from 5 MarkVCID sites process the same 30 CVR datasets using a cloud-based CVR data processing pipeline. Second, the inter-scanner reproducibility of CO2-CVR quantification was assessed in 10 young subjects across two scanners of different vendors. Third, test-retest repeatability was evaluated in 20 elderly subjects from 4 sites with a scan interval of less than 2 weeks. In all studies, the CO2 CVR measurements were performed using the fixed inspiration method, where the subjects wore a nose clip and a mouthpiece and breathed room air and 5% CO2 air contained in a Douglas bag alternatively through their mouth. The results showed that the inter-rater CoV of CVR processing was 0.08 ± 0.08% for whole-brain CVR values and ranged from 0.16% to 0.88% in major brain regions, with ICC of absolute agreement above 0.9959 for all brain regions. Inter-scanner CoV was found to be 6.90 ± 5.08% for whole-brain CVR values, and ranged from 4.69% to 12.71% in major brain regions, which are comparable to intra-session CoVs obtained from the same scanners on the same day. ICC of consistency between the two scanners was 0.8498 for whole-brain CVR and ranged from 0.8052 to 0.9185 across major brain regions. In the test-retest evaluation, test-retest CoV across different days was found to be 18.29 ± 17.12% for whole-brain CVR values, and ranged from 16.58% to 19.52% in major brain regions, with ICC of absolute agreement ranged from 0.6480 to 0.7785. These results demonstrated good inter-rater, inter-scanner, and test-retest reliability in healthy volunteers, and suggested that CO2-CVR has suitable instrumental properties for use as an imaging biomarker of cerebrovascular function in multi-site and longitudinal observational studies and clinical trials.

Excessive brain iron negatively affects working memory and related processes but the impact of cortical iron on task-relevant, cortical brain networks is unknown. We hypothesized that high cortical iron concentration may disrupt functional circuitry within cortical networks supporting working memory performance. Fifty-five healthy older adults completed an N-Back working memory paradigm while functional magnetic resonance imaging (fMRI) was performed. Participants also underwent quantitative susceptibility mapping (QSM) imaging for assessment of non-heme brain iron concentration. Additionally, pseudo continuous arterial spin labeling scans were obtained to control for potential contributions of cerebral blood volume and structural brain images were used to control for contributions of brain volume. Task performance was positively correlated with strength of task-based functional connectivity (tFC) between brain regions of the frontoparietal working memory network. However, higher cortical iron concentration was associated with lower tFC within this frontoparietal network and with poorer working memory performance after controlling for both cerebral blood flow and brain volume. Our results suggest that high cortical iron concentration disrupts communication within frontoparietal networks supporting working memory and is associated with reduced working memory performance in older adults.