Sodium signal intensity of CSF using 1H-guided 23Na-MRI as a potential noninvasive biomarker in Alzheimer's disease.

BACKGROUND AND PURPOSE: Alzheimer's disease (AD) is characterized by cognitive decline and mnestic deficits. The pathophysiology of AD is not fully understood, which renders the development of accurate tools for early diagnosis and effective therapies exceedingly difficult. In this study, we investigated the use of 23Na-MRI to measure the relative sodium signal intensities (rSSIs) in CSF in patients with AD and healthy controls. METHODS: We prospectively recruited 11 patients with biomarker-diagnosed early-stage AD, as well as 12 cognitively healthy age-matched controls. All participants underwent 23Na-MRI to measure rSSI. Statistical analyses were performed to compare CSF sodium signal intensities between groups and to evaluate the specificity and sensitivity of the rSSI in the diagnosis of AD. RESULTS: RSSIs in CSF were significantly higher in AD patients (mean = 68.6% ± 7.7%) compared to healthy controls (mean = 56.9% ± 5.5%) (p < .001). There was also a significant negative correlation between rSSI in CSF and hippocampus and amygdala volumes (r = -.54 and -.49, p < .05) as well as a positive correlation with total CSF volumes (r = .81, p < .05). Receiver operating characteristic analysis showed high diagnostic accuracy for rSSI in discriminating between AD patients and healthy controls (area under the curve = .94). CONCLUSION: Our study provides evidence that rSSI in CSF is increased in AD patients in comparison to healthy controls. rSSI may serve as a potential marker for early detection and monitoring of disease progression. Larger, longitudinal studies are needed to confirm our findings and to investigate the association between rSSI in CSF and the severity of cognitive impairment.

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA.

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.