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Cerebrovascular imaging assessments are particularly challenging in adolescent cohorts, where not all modalities are appropriate, and rapid brain maturation alters hemodynamics at both macro- and microvascular scales. In a preliminary sample of healthy adolescents (n=12, 8-25 years), we investigated relationships between 4D flow MRI-derived blood velocity and blood flow in bilateral anterior, middle, and posterior cerebral arteries and BOLD cerebrovascular reactivity in associated vascular territories. As hypothesized, higher velocities in large arteries are associated with an earlier response to a vasodilatory stimulus (cerebrovascular reactivity delay) in the downstream territory. Higher blood flow through these arteries is associated with a larger BOLD response to a vasodilatory stimulus (cerebrovascular reactivity amplitude) in the associated territory. These trends are consistent in a case study of adult moyamoya disease. In our small adolescent cohort, macrovascular-microvascular relationships for velocity/delay and flow/CVR change with age, though underlying mechanisms are unclear. Our work emphasizes the need to better characterize this key stage of human brain development, when cerebrovascular hemodynamics are changing, and standard imaging methods offer limited insight into these processes. We provide important normative data for future comparisons in pathology, where combining macro- and microvascular assessments may better help us prevent, stratify, and treat cerebrovascular disease.

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage., Competing Interests: Declaration of competing interest The authors declare no competing financial interests.