Deep learning identification of reward-related neural substrates of preadolescent irritability: A novel 3D CNN application for fMRI

Q4 Neuroscience
Johanna C. Walker , Conner Swineford , Krupali R. Patel , Lea R. Dougherty , Jillian Lee Wiggins
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引用次数: 0

Abstract

The recent emergence of deep learning methods, particularly convolutional neural networks (CNNs), applied to fMRI data presents a promising avenue in psychiatry research, offering advantages over traditional analyses by requiring minimal assumptions and enabling detection of higher-level patterns and intricate, nonlinear relationships within inherently complex fMRI data. Irritability, defined as a lowered threshold for angry responses to blocked rewards, is a promising neurodevelopmental marker for mental health risk due to its robust, transdiagnostic predictive power in youth. In this study, data from the Adolescent Brain and Cognitive Development (ABCD) baseline sample (N = 6065) were utilized for a novel application of a 3D CNN to whole-brain fMRI data acquired during the reward anticipation period of the monetary incentive delay task to predict parent-reported youth irritability severity, measured dimensionally. Regression activation mapping (RAM) was employed to extract feature maps of brain regions most predictive of irritability severity from the model. The model demonstrated satisfactory accuracy, with a mean squared error (MSE) of 1.82, and predicted irritability severity scores with a mean absolute error (MAE) of 0.48 ± 1.54 SD from the true scores. Notably, feature maps revealed bilateral representation of key regions implicated in emotional response and reward processing, including the caudate nucleus, amygdala, parahippocampal gyrus, and hippocampus. This study underscores the potential for 3D CNNs to predict significant, dimensional clinical outcomes such as irritability severity using fMRI data.

Abstract Image

深度学习识别青春期前易怒的奖励相关神经基质:一种新的3D CNN应用于fMRI
最近出现的深度学习方法,特别是卷积神经网络(cnn),应用于fMRI数据,为精神病学研究提供了一条有前途的途径,通过要求最小的假设和能够在固有复杂的fMRI数据中检测更高层次的模式和复杂的非线性关系,提供了优于传统分析的优势。易怒被定义为对被阻断的奖励做出愤怒反应的较低阈值,由于其在青少年中具有强大的跨诊断预测能力,因此是一种很有前途的心理健康风险神经发育标志物。在这项研究中,来自青少年大脑和认知发展(ABCD)基线样本(N = 6065)的数据被用于3D CNN对在金钱激励延迟任务的奖励预期期间获得的全脑功能磁共振成像数据的新颖应用,以预测父母报告的青少年暴躁程度,测量维度。采用回归激活映射(RAM)从模型中提取最能预测烦躁程度的脑区特征图。该模型显示出令人满意的准确性,均方误差(MSE)为1.82,预测易怒严重程度评分的平均绝对误差(MAE)为0.48±1.54 SD。值得注意的是,特征图揭示了涉及情绪反应和奖励处理的关键区域的双侧表征,包括尾状核、杏仁核、海马旁回和海马体。这项研究强调了3D cnn预测重要的临床结果的潜力,如使用功能磁共振成像数据预测易怒严重程度。
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来源期刊
Neuroimage. Reports
Neuroimage. Reports Neuroscience (General)
CiteScore
1.90
自引率
0.00%
发文量
0
审稿时长
87 days
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