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Catana, C.

Publications and source records attributed to Catana, C..

2 recordsLinked to original sources

Simultaneous EEG-PET-MRI identifies temporally coupled, spatially structured hemodynamic and metabolic dynamics across wakefulness and NREM sleep

Sleep entails significant changes in cerebral hemodynamics and metabolism. Yet, the way these processes evolve throughout wakefulness and sleep and their spatiotemporal dependence remain largely unknown. Here, by integrating a novel functional PET technique with simultaneous EEG-fMRI, we reveal a tightly coupled temporal progression of global hemodynamics and metabolism during the descent into NREM sleep, with large hemodynamic fluctuations emerging as global glucose metabolism declines, both of which track EEG arousal dynamics. Furthermore, we identify two distinct network patterns that emerge during NREM sleep: an oscillating, high-metabolism sensorimotor network remains active and dynamic, whereas hemodynamic and metabolic activity in the default-mode network is suppressed. These results elucidate how sleep diminishes awareness while preserving sensory responses, and uncover a complex, alternating balance of neuronal, hemodynamic, and metabolic dynamics in the sleeping brain. This work also demonstrates the potential of EEG-PET-MRI to explore neuro-hemo-metabolic dynamics underlying cognition and arousal in humans.

neuroscience↗

DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics

Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data could provide a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wide clinical use. We designed and validated a deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data, including strain rate (SR) and regional strain polar maps, consisting of segmentation and motion estimation convolutional neural networks developed and trained using healthy and cardiovascular disease (CVD) subjects (n=150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 {+/-} 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 4 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was excellent (>0.95) for strain, moderate to excellent for SR (0.690-0.963), and good to excellent (0.826-0.994) in most polar map segments. Absolute relative change was within ~5% for strain, within ~10% for SR, and <1% in half of polar map segments. In conclusion, we developed and evaluated a DL-based, end-to-end fully-automatic workflow for global and regional myocardial strain analysis to quantitatively characterize cardiac mechanics of healthy and CVD subjects based on ubiquitously acquired cine-MRI data.

bioinformatics↗