Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.

Background: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimics of behavioral phenotypes resembling schizophrenia-like symptoms in male offspring and depression-like symptoms in female offspring.

Purpose: To identify mechanisms of neuronal abnormalities in lactational Poly I:C offspring using quantitative MRI (qMRI) tools.

Study Type: Prospective.

DeepEMC-T mapping: Deep learning-enabled T mapping based on echo modulation curve modeling.

Purpose: Echo modulation curve (EMC) modeling enables accurate quantification of T relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T mapping, called DeepEMC-T mapping, to efficiently estimate accurate T maps from fewer echoes.

Phytoplankton cell-states: multiparameter fluorescence lifetime flow-based monitoring reveals cellular heterogeneity.

Phytoplankton are a major source of primary productivity. Their photosynthetic fluorescence are unique measures of their type, physiological state, and response to environmental conditions. Changes in phytoplankton photophysiology are commonly monitored by bulk fluorescence spectroscopy, where gradual changes are reported in response to different perturbations, such as light intensity changes.

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla.

Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools.

Probing muscle recovery following downhill running using precise mapping of MRI T relaxation times.

Purpose: Postexercise recovery rate is a vital component of designing personalized training protocols and rehabilitation plans. Tracking exercise-induced muscle damage and recovery requires sensitive tools that can probe the muscles' state and composition noninvasively.

Methods: Twenty-four physically active males completed a running protocol consisting of a 60-min downhill run on a treadmill at -10% incline and 65% of maximal heart rate.