fMRI-Based Multi-class DMDC Model Efficiently Decodes the Overlaps between ASD and ADHD.

IF 1 Q4 NEUROSCIENCES
Zahra Zolghadr, Seyed Amir Hossein Batouli, Hamid Alavi Majd, Lida Shafaghi, Yadollah Mehrabi
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引用次数: 0

Abstract

Introduction: Neurodevelopmental disorders comprise a group of neuropsychiatric conditions. Presently, behavior-based diagnostic approaches are utilized in clinical settings, but the overlapping features among these disorders obscure their recognition and management. Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have common characteristics across various levels, from genes to symptoms. Designing a computational framework based on the neuroimaging findings could provide a discriminative tool for ultimate more efficient treatment. Machine learning approaches, specifically classification methods are among the most applied techniques to reach this goal.

Methods: We applied a novel two-level multi-class data maximum dispersion classifier (DMDC) algorithm to classify the functional neuroimaging data (utilizing datasets: ADHD-200 and autism brain imaging data exchange (ABIDE)) into two categories: Neurodevelopmental disorders (ASD and ADHD) or healthy participants, based on calculated functional connectivity values (statistical temporal correlation).

Results: Our model achieved a total accuracy of 62% for healthy controls. Specifically, it demonstrated an accuracy of 51% for healthy subjects, 61% for autism spectrum disorder, and 84% for ADHD. The support vector machine (SVM) model achieved an accuracy of 46% for both the healthy control and ASD groups, while the ADHD group classification accuracy was estimated to be 84%. These two models showed similar classification indices for the ADHD group. However, the discrimination power was higher in the ASD class.

Conclusion: The method employed in this study demonstrated acceptable performance in classifying disorders and healthy conditions compared to the more commonly used SVM method. Notably, functional connections associated with the cerebellum showed discriminative power.

基于 fMRI 的多类 DMDC 模型能有效解码 ASD 与 ADHD 之间的重叠。
简介神经发育障碍是一组神经精神疾病。目前,临床上采用的是基于行为的诊断方法,但这些障碍的重叠特征使其识别和管理变得模糊不清。注意缺陷多动障碍(ADHD)和自闭症谱系障碍(ASD)在从基因到症状的各个层面都有共同特征。根据神经影像学的研究结果设计一个计算框架,可以为最终更有效的治疗提供一个鉴别工具。机器学习方法,特别是分类方法,是实现这一目标的最常用技术之一:我们采用了一种新颖的两级多类数据最大分散分类器(DMDC)算法来对功能神经影像数据进行分类(利用数据集:ADHD-200 和自闭症脑成像交换数据集):ADHD-200和自闭症脑成像数据交换(ABIDE))分为两类:根据计算出的功能连接值(统计时间相关性),将神经发育障碍(ASD 和 ADHD)或健康参与者分为两类:结果:我们的模型对健康对照组的总准确率达到 62%。结果:我们的模型对健康对照组的准确率为 62%,对健康受试者的准确率为 51%,对自闭症谱系障碍的准确率为 61%,对多动症的准确率为 84%。支持向量机(SVM)模型对健康对照组和自闭症谱系障碍组的分类准确率为 46%,而对多动症组的分类准确率估计为 84%。这两种模型对多动症组的分类指数相似。然而,ASD 组的分辨力更高:结论:与更常用的 SVM 方法相比,本研究采用的方法在疾病和健康状况分类方面表现出了可接受的性能。值得注意的是,与小脑相关的功能连接显示出了鉴别力。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
64
审稿时长
4 weeks
期刊介绍: BCN is an international multidisciplinary journal that publishes editorials, original full-length research articles, short communications, reviews, methodological papers, commentaries, perspectives and “news and reports” in the broad fields of developmental, molecular, cellular, system, computational, behavioral, cognitive, and clinical neuroscience. No area in the neural related sciences is excluded from consideration, although priority is given to studies that provide applied insights into the functioning of the nervous system. BCN aims to advance our understanding of organization and function of the nervous system in health and disease, thereby improving the diagnosis and treatment of neural-related disorders. Manuscripts submitted to BCN should describe novel results generated by experiments that were guided by clearly defined aims or hypotheses. BCN aims to provide serious ties in interdisciplinary communication, accessibility to a broad readership inside Iran and the region and also in all other international academic sites, effective peer review process, and independence from all possible non-scientific interests. BCN also tries to empower national, regional and international collaborative networks in the field of neuroscience in Iran, Middle East, Central Asia and North Africa and to be the voice of the Iranian and regional neuroscience community in the world of neuroscientists. In this way, the journal encourages submission of editorials, review papers, commentaries, methodological notes and perspectives that address this scope.
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