Sharma Meghna, Arora Shaveta
The NorthCap University, Department of Computer Science and Engineering, Gurugram, Haryana, India.
J Med Imaging (Bellingham). 2024 Nov;11(6):064502. doi: 10.1117/1.JMI.11.6.064502. Epub 2024 Dec 20.
Various brain atlases are available to parcellate and analyze brain connections. Most traditional machine learning and deep learning studies analyzing Attention Deficit Hyperactivity Disorder (ADHD) have used either one or two brain atlases for their analysis. However, there is a lack of comprehensive research evaluating the impact of different brain atlases and associated factors such as connectivity measures and dimension reduction techniques on ADHD diagnosis.
This paper proposes an efficient and robust multimodality model that investigates various brain atlases utilizing different parcellation strategies and scales. Thirty combinations of six brain atlases and five distinct machine learning classifiers with optimized hyperparameters are implemented to identify the most promising brain atlas for ADHD diagnosis. These outcomes are validated using the statistical Friedman test. To enhance comprehensiveness, the impact of three different connectivity measures, each representing unique facets of brain connectivity, is also analyzed. Considering the extensive complexity of brain interconnections, the effect of various dimension reduction techniques on classification performance and execution time is also analyzed. The final model is integrated with phenotypic data to create an efficient multimodal ADHD classification model.
Experimental results on the ADHD-200 dataset demonstrate a significant variation in classification performance introduced by each factor. The proposed model outperforms many state-of-the-art ADHD approaches and achieves an accuracy of 77.59%, an area under the curve (AUC) score of 77.25% and an -score of 75.43%.
The proposed model offers clear guidance for researchers, helping to standardize atlas selection and associated factors and improve the consistency and accuracy of ADHD studies for more reliable clinical applications.
有多种脑图谱可用于划分和分析脑连接。大多数分析注意力缺陷多动障碍（ADHD）的传统机器学习和深度学习研究在分析时仅使用了一两种脑图谱。然而，缺乏全面的研究来评估不同脑图谱以及诸如连接性度量和降维技术等相关因素对ADHD诊断的影响。
本文提出了一种高效且稳健的多模态模型，该模型利用不同的划分策略和尺度来研究各种脑图谱。实现了六种脑图谱与五个具有优化超参数的不同机器学习分类器的三十种组合，以确定用于ADHD诊断最有前景的脑图谱。使用统计弗里德曼检验对这些结果进行验证。为提高全面性，还分析了三种不同连接性度量的影响，每种度量代表脑连接的独特方面。考虑到脑互连的广泛复杂性，还分析了各种降维技术对分类性能和执行时间的影响。最终模型与表型数据集成，以创建一个高效的多模态ADHD分类模型。
在ADHD - 200数据集上的实验结果表明，每个因素都会导致分类性能出现显著差异。所提出的模型优于许多当前最先进的ADHD方法，准确率达到77.59%，曲线下面积（AUC）得分77.25%，F1得分75.43%。
所提出的模型为研究人员提供了明确的指导，有助于规范图谱选择及相关因素，提高ADHD研究的一致性和准确性，以实现更可靠的临床应用。
