No abstract

No abstract

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

No abstract

Background: Depressive symptoms are rising in the general population and can lead to depression years later, but the contributing factors are less known. Although the link between sleep disturbances and depressive symptoms has been reported, the predictive role of sleep on depressive symptoms severity (DSS) and the impact of anxiety and brain structure on their interrelationship at the individual subject level remain poorly understood. Methods: Here, we used 1813 participants from three population-based datasets. We applied ensemble machine learning models to assess the predictive role of sleep, anxiety, and brain structure on DSS in the primary dataset (n = 1101), then we tested the generalizability of our findings in two independent datasets. In addition, we performed a mediation analysis to identify the effect of anxiety and brain structure on the link between sleep and DSS. Results: We observed that sleep quality could predict DSS (r = 0.43, rMSE = 2.73, R2 = 0.18), and adding anxiety strengthened its prediction (r = 0.67, rMSE = 2.25, R2 = 0.45). However, brain structure (alone or along with sleep/anxiety) did not predict DSS. Importantly, out-of-cohort validations of our findings in other samples provided similar findings. Further, anxiety scores (not brain structure) could mediate the link between sleep quality and DSS. Conclusion: Taken together, poor sleep quality and anxiety symptoms could predict DSS across three cohorts. We hope that our findings incentivize clinicians to consider the importance of screening and treating subjects with sleep and anxiety problems to reduce the burden of depressive symptoms.