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

利用机器学习和定量磁共振成像技术对哺乳期神经发育聚肌胞苷酸(Poly I:C)诱导的精神分裂症和抑郁症大鼠模型进行脑部异常特征分析

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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: 7 T. Multiflip-angle FLASH protocol for T(1) mapping; multi-echo spin-echo T(2)-mapping protocol; echo planar imaging protocol for diffusion tensor imaging. ASSESSMENT: Nursing dams were injected with the viral mimic Poly I:C or saline (control group). In adulthood, quantitative maps of T(1), T(2), 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.

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