Cao Meng, Wu Kai, Halperin Jeffery M, Li Xiaobo
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States.
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China.
Front Neurosci. 2023 Mar 2;17:1128646. doi: 10.3389/fnins.2023.1128646. eCollection 2023.
Traumatic brain injury (TBI) is a major public health concern in children. Children with TBI have elevated risk in developing attention deficits. Existing studies have found that structural and functional alterations in multiple brain regions were linked to TBI-related attention deficits in children. Most of these existing studies have utilized conventional parametric models for group comparisons, which have limited capacity in dealing with large-scale and high dimensional neuroimaging measures that have unknown nonlinear relationships. Nevertheless, none of these existing findings have been successfully implemented to clinical practice for guiding diagnoses and interventions of TBI-related attention problems. Machine learning techniques, especially deep learning techniques, are able to handle the multi-dimensional and nonlinear information to generate more robust predictions. Therefore, the current research proposed to construct a deep learning model, semi-supervised autoencoder, to investigate the topological alterations in both structural and functional brain networks in children with TBI and their predictive power for post-TBI attention deficits.
Functional magnetic resonance imaging data during sustained attention processing task and diffusion tensor imaging data from 110 subjects (55 children with TBI and 55 group-matched controls) were used to construct the functional and structural brain networks, respectively. A total of 60 topological properties were selected as brain features for building the model.
The model was able to differentiate children with TBI and controls with an average accuracy of 82.86%. Functional and structural nodal topological properties associated with left frontal, inferior temporal, postcentral, and medial occipitotemporal regions served as the most important brain features for accurate classification of the two subject groups. Post hoc regression-based machine learning analyses in the whole study sample showed that among these most important neuroimaging features, those associated with left postcentral area, superior frontal region, and medial occipitotemporal regions had significant value for predicting the elevated inattentive and hyperactive/impulsive symptoms.
Findings of this study suggested that deep learning techniques may have the potential to help identifying robust neurobiological markers for post-TBI attention deficits; and the left superior frontal, postcentral, and medial occipitotemporal regions may serve as reliable targets for diagnosis and interventions of TBI-related attention problems in children.
创伤性脑损伤（TBI）是儿童主要的公共卫生问题。患有TBI的儿童出现注意力缺陷的风险升高。现有研究发现，多个脑区的结构和功能改变与儿童TBI相关的注意力缺陷有关。这些现有研究大多使用传统参数模型进行组间比较，在处理具有未知非线性关系的大规模和高维神经影像测量方面能力有限。然而，这些现有发现均未成功应用于临床实践以指导TBI相关注意力问题的诊断和干预。机器学习技术，尤其是深度学习技术，能够处理多维和非线性信息以生成更可靠的预测。因此，当前研究提出构建一个深度学习模型，即半监督自动编码器，以研究TBI儿童脑结构和功能网络的拓扑改变及其对TBI后注意力缺陷的预测能力。
分别使用110名受试者（55名TBI儿童和55名年龄匹配的对照组儿童）在持续注意力处理任务期间的功能磁共振成像数据和扩散张量成像数据构建功能和结构脑网络。总共选择60个拓扑属性作为构建模型的脑特征。
该模型能够区分TBI儿童和对照组，平均准确率为82.86%。与左额叶、颞下回、中央后回和枕颞内侧区域相关的功能和结构节点拓扑属性是准确分类这两个受试者组的最重要脑特征。在整个研究样本中基于事后回归的机器学习分析表明，在这些最重要的神经影像特征中，与左中央后回区域、额上回区域和枕颞内侧区域相关的特征对预测注意力不集中和多动/冲动症状的升高具有显著价值。
本研究结果表明，深度学习技术可能有潜力帮助识别TBI后注意力缺陷可靠的神经生物学标志物；左额上回、中央后回和枕颞内侧区域可能是儿童TBI相关注意力问题诊断和干预的可靠靶点。
