The value of hippocampal sub-region imaging features for the diagnosis and severity grading of ASD in children.

IF 2.7 4区 医学 Q3 NEUROSCIENCES
Brain Research Pub Date : 2025-02-15 Epub Date: 2024-11-30 DOI:10.1016/j.brainres.2024.149369
Xiaofen Sun, Peng Zhang, Shitong Cheng, Xiaocheng Wang, Jingbo Deng, Yuefu Zhan, Jianqiang Chen
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引用次数: 0

Abstract

Background: Hippocampal structural changes in Autism Spectrum Disorder (ASD) are inconsistent. This study investigates hippocampal subregion changes in ASD patients to reveal intrinsic hippocampal anomalies.

Methods: A retrospective study from Hainan Children's Hospital database (2020-2023) included ASD patients and matched controls. We classified ASD participants based on severity, dividing all subjects into four groups: normal, mild, moderate, and severe. High-resolution T1-weighted MRI images were analyzed for hippocampal subregion segmentation and volume calculations using Freesurfer. Texture features were extracted via the Gray-Level Co-occurrence Matrix. The Receiver Operating Characteristic curve was used to evaluate seven random forest predictive models constructed from volume, subregion, and texture features, as well as their combinations following feature selection.

Results: The study included 114 ASD patients (98 boys, 2-8 years; 16 girls, 2-6 years; 17 mild, 57 moderate, 40 severe) and 111 healthy controls (HCs). No significant differences in volumes were found between ASD patients and HCs (adjusted P-value >0.05). The seven random forest models showed that single volume and texture features performed poorly for ASD classification; however, integrating various feature types improved AUC values. Further selection of texture, subregion, and volume features enhanced AUC performance across normal and varying severity categories, demonstrating the potential value of specific subregions and integrated features in ASD diagnosis.

Conclusion: Random forest models revealed that hippocampal volume, texture features, and subregion characteristics are crucial for diagnosing and assessing the severity of ASD. Integrating selected texture and subregion features optimized diagnostic efficacy, while combining texture, subregion, and volume features further improved severity grading effectiveness.

海马亚区影像学特征对儿童ASD诊断及严重程度分级的价值。
背景:自闭症谱系障碍(ASD)海马结构变化不一致。本研究通过对ASD患者海马亚区变化的研究来揭示其固有的海马异常。方法:回顾性研究海南省儿童医院数据库(2020-2023),包括ASD患者和匹配的对照组。我们根据严重程度对ASD参与者进行分类,将所有受试者分为四组:正常、轻度、中度和重度。分析高分辨率t1加权MRI图像,使用Freesurfer进行海马亚区分割和体积计算。通过灰度共生矩阵提取纹理特征。利用Receiver Operating Characteristic curve对由体积、子区域和纹理特征构建的7个随机森林预测模型及其在特征选择后的组合进行了评价。结果:研究纳入114例ASD患者(男童98例,2-8 岁;女孩16名,2-6岁 ;17例轻度,57例中度,40例重度)和111例健康对照(hc)。ASD患者与hcc患者在体积上无显著差异(调整p值 >0.05)。7种随机森林模型表明,单一的体积和纹理特征对ASD分类效果较差;然而,整合各种特征类型提高了AUC值。进一步选择纹理、子区域和体积特征可以增强正常和不同严重程度类别的AUC表现,证明特定子区域和综合特征在ASD诊断中的潜在价值。结论:随机森林模型显示海马体积、纹理特征和亚区特征对诊断和评估ASD的严重程度至关重要。将选择的纹理和子区域特征相结合可优化诊断效果,将纹理、子区域和体积特征相结合可进一步提高严重程度分级效果。
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来源期刊
Brain Research
Brain Research 医学-神经科学
CiteScore
5.90
自引率
3.40%
发文量
268
审稿时长
47 days
期刊介绍: An international multidisciplinary journal devoted to fundamental research in the brain sciences. Brain Research publishes papers reporting interdisciplinary investigations of nervous system structure and function that are of general interest to the international community of neuroscientists. As is evident from the journals name, its scope is broad, ranging from cellular and molecular studies through systems neuroscience, cognition and disease. Invited reviews are also published; suggestions for and inquiries about potential reviews are welcomed. With the appearance of the final issue of the 2011 subscription, Vol. 67/1-2 (24 June 2011), Brain Research Reviews has ceased publication as a distinct journal separate from Brain Research. Review articles accepted for Brain Research are now published in that journal.
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