Work

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

Year: 2024

Type: article

Abstract: 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. ANIMAL MODEL: Twenty Poly I:C rats and 20 healthy control rats, age 130 postnatal day. FIELD STRENGTH/SEQUENCE: -mapping protocol; echo planar imaging protocol for diffusion tensor imaging. ASSESSMENT: , proton density, and five diffusion metrics were generated for the offsprings. Seven regions of interest (ROIs) were segmented, followed by extracting 10 quantitative features for each ROI. STATISTICAL TESTS: Random forest machine learning (ML) tool was employed to identify MRI markers of disease and classify Poly I:C rats from healthy controls based on quantitative features. RESULTS: Poly I:C rats were identified from controls with an accuracy of 82.5 ± 25.9% for females and 85.0 ± 24.0% for males. Poly I:C females exhibited differences mainly in diffusion-derived parameters in the thalamus and the medial prefrontal cortex (MPFC), while males displayed changes primarily in diffusion-derived parameters in the corpus callosum and MPFC. DATA CONCLUSION: qMRI shows potential for identifying sex-specific brain abnormalities in the Poly I:C model of neurodevelopmental disorders. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

Source: Journal of Magnetic Resonance Imaging

DOI: 10.1002/jmri.29634