Abnormal structural and functional network topological properties associated with left prefrontal, parietal, and occipital cortices significantly predict childhood TBI-related attention deficits: A semi-supervised deep learning study

Frontiers in Neuroscience

Neuroscience

Meng Cao¹  Xiaobo Li²  Jeffery M. Halperin³  Kai Wu⁴ 

[1] Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States;Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States;Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, United States;Department of Psychology, Queens College, City University of New York, New York, NY, United States;School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China;

关键词: pediatric;    traumatic brain injury;    attention deficits;    diffusion tensor imaging;    functional magnetic resonance imaging;    graph theory;    autoencoder;    semi-supervised deep learning technique;   

DOI  :  10.3389/fnins.2023.1128646

 received in 2022-12-21, accepted in 2023-02-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionTraumatic 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.MethodsFunctional 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.ResultsThe 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.DiscussionFindings 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.

【 授权许可】

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Copyright © 2023 Cao, Wu, Halperin and Li.