The Mind Research Network, 1101 Yale Blvd. NE, Albuquerque, NM 87106, United States.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Place NE, Atlanta, GA 30303, United States.
Alcohol. 2021 Jun;93:25-34. doi: 10.1016/j.alcohol.2021.03.001. Epub 2021 Mar 11.
Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n = 48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n = 24; 12 PAE, 12 SAC), and a females-only sample (n = 24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.
胎儿酒精谱系障碍（FASD）是一种与产前酒精暴露（PAE）相关的广泛的身体和神经行为异常，被认为是一个重大的公共卫生问题。由于缺乏诊断标准的共识和客观生物标志物的有限应用，FASD 的诊断进展一直受到阻碍。我们小组之前的研究利用静息态功能磁共振成像（fMRI）来测量功能网络连接（FNC），结果显示，与对照组相比，中度 PAE 导致 FNC 出现了一些性别和区域依赖的改变。鉴于 FNC 对中度 PAE 敏感，本研究探讨了使用 FNC 数据和机器学习方法来检测产前酒精暴露的方法，研究对象为一组接受产前酒精暴露和对照组的啮齿动物。我们利用先前从暴露于中度产前酒精（PAE）或蔗糖对照溶液（SAC）的成年大鼠中获得的静息态 fMRI 数据，评估通过空间群组独立成分分析（GICA）提取的静息态网络的 FNC。使用支持向量机（SVM）-基于算法对 FNC 数据进行二进制分类，并在雄性和雌性的汇总样本（n=48；12 只雄性 PAE，12 只雌性 PAE，12 只雄性 SAC，12 只雌性 SAC）、雄性样本（n=24；12 只 PAE，12 只 SAC）和雌性样本（n=24；12 只 PAE，12 只 SAC）中进行留一法交叉验证（LOOCV）。结果表明，二次 SVM（QSVM）核对女性 PAE 检测非常有效。基于 QSVM 核的分类结果显示，所有动物的准确率为 62.5%，雄性为 58.3%，雌性为 79.2%。此外，QSVM 权重的定性评估表明，几个海马和皮质网络的总体主题有助于 QSVM 做出正确的分类决策。我们的研究结果表明，使用 QSVM 和成年雌性 FNC 数据进行二进制分类是开发用于识别 FASD 的新型非侵入性技术的潜在候选方法。
