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

IF 4.3 3区材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

ACS Applied Electronic Materials Pub Date : 2024-10-28 DOI:10.1002/jmri.29634

Rona Haker, Coral Helft, Emilya Natali Shamir, Moni Shahar, Hadas Solomon, Noam Omer, Tamar Blumenfeld-Katzir, Sharon Zlotzover, Yael Piontkewitz, Ina Weiner, Noam Ben-Eliezer

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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 T₁ mapping; multi-echo spin-echo T₂-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 T₁, T₂, 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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利用机器学习和定量 MRI 分析哺乳期神经发育多聚 I:C 大鼠精神分裂症和抑郁症模型大脑异常的特征。

背景：目的：使用定量核磁共振成像（qMRI）工具确定哺乳期Poly I:C后代神经元异常的机制：动物模型20只Poly I:C大鼠和20只健康对照组大鼠，出生后130天：7 T.用于 T1 映射的多翻转角度 FLASH 方案；用于 T2 映射的多回波自旋回波方案；用于弥散张量成像的回波平面成像方案：给哺乳母鼠注射病毒模拟物 Poly I:C 或生理盐水（对照组）。成年后，为后代生成 T1、T2、质子密度和五种扩散指标的定量图。对七个感兴趣区（ROI）进行分割，然后为每个感兴趣区提取 10 个定量特征：统计测试：采用随机森林机器学习（ML）工具识别疾病的 MRI 标记，并根据定量特征将多发性 I:C 大鼠与健康对照组进行分类：雌性多发性 I:C 大鼠从对照组中识别出来的准确率为 82.5 ± 25.9%，雄性为 85.0 ± 24.0%。数据结论：qMRI 显示了在神经发育障碍的 Poly I:C 模型中识别性别特异性大脑异常的潜力：不适用技术效率：第二阶段。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Electronic Materials Multiple-

CiteScore

7.20

自引率

4.30%

发文量

567