RESUMO
PURPOSE: Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. METHODS: Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. RESULTS: Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. CONCLUSIONS: Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.
RESUMO
BACKGROUND: Abnormalities in the regulation of physiological arousal and interoceptive processing are implicated in the expression and maintenance of specific psychiatric conditions and symptoms. We undertook a cross-sectional characterisation of patients accessing secondary mental health services, recording measures relating to cardiac physiology and interoception, to understand how physiological state and interoceptive ability relate transdiagnostically to affective symptoms. METHODS: Participants were patients (n = 258) and a non-clinical comparison group (n = 67). Clinical diagnoses spanned affective disorders, complex personality presentations and psychoses. We first tested for differences between patient and non-clinical participants in terms of cardiac physiology and interoceptive ability, considering interoceptive tasks and a self-report measure. We then tested for correlations between cardiac and interoceptive measures and affective symptoms. Lastly, we explored group differences across recorded clinical diagnoses. RESULTS: Patients exhibited lower performance accuracy and confidence in heartbeat discrimination and lower heartbeat tracking confidence relative to comparisons. In patients, greater anxiety and depression predicted greater self-reported interoceptive sensibility and a greater mismatch between performance accuracy and sensibility. This effect was not observed in comparison participants. Significant differences between patient groups were observed for heart rate variability (HRV) although post hoc differences were not significant after correction for multiple comparisons. Finally, accuracy in heartbeat tracking was significantly lower in schizophrenia compared to other diagnostic groups. CONCLUSIONS: The multilevel characterisation presented here identified certain physiological and interoceptive differences associated with psychiatric symptoms and diagnoses. The clinical stratification and therapeutic targeting of interoceptive mechanisms is therefore of potential value in treating certain psychiatric conditions.
