PDF(Pubmed)
Abstract：
BACKGROUND: Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain-specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real-world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders.
OBJECTIVE: In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network.
RESULTS: In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline.
CONCLUSIONS: We are confident that this study can help more researchers enter the field and create accessible fMRI and deep-learning diagnostic models for neurological disorders.
OBJECTIVE: In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network.
RESULTS: In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline.
CONCLUSIONS: We are confident that this study can help more researchers enter the field and create accessible fMRI and deep-learning diagnostic models for neurological disorders.
摘要：
背景：深度学习（DL）方法正在迅速改变研究人员对神经系统疾病进行分类的方式。例如,结合功能磁共振成像(fMRI)和DL已经帮助研究人员识别神经系统疾病的功能生物标志物(例如，大脑激活和连接)和试点创新诊断模型。然而,执行DL分析所需的知识通常是特定领域的，并且在脑科学中没有广泛教授(例如，心理学,神经科学,和认知科学)。相反，神经系统诊断和神经影像学训练(例如，fMRI）在很大程度上仅限于大脑和医学科学。反过来,这些学科知识障碍和不同的专业可能成为阻碍fMRI和DL管道结合的障碍。fMRI和DL方法的复杂性也阻碍了它们的临床采用和对现实世界诊断的推广。例如,大多数当前的模型不是为临床环境设计的，也不是为学生等非专业人群使用的，临床医生,和医护人员。因此，有越来越多的辅助工具(例如，软件和编程软件包），旨在简化和增加fMRI和DL管道的可及性，以诊断神经系统疾病。
目的：在本研究中，我们介绍了一些流行的DL和fMRI辅助工具的入门指南。我们还使用辅助工具（例如，Optuna,GIFT,和ABIDE预处理存储库)，功能磁共振成像,和卷积神经网络。
结果：反过来，我们为研究人员提供了辅助工具指南，并给出了简化的功能磁共振成像和DL管道的示例。
结论：我们相信这项研究可以帮助更多的研究人员进入该领域，并为神经系统疾病创建可访问的fMRI和深度学习诊断模型。
目的：在本研究中，我们介绍了一些流行的DL和fMRI辅助工具的入门指南。我们还使用辅助工具（例如，Optuna,GIFT,和ABIDE预处理存储库)，功能磁共振成像,和卷积神经网络。
结果：反过来，我们为研究人员提供了辅助工具指南，并给出了简化的功能磁共振成像和DL管道的示例。
结论：我们相信这项研究可以帮助更多的研究人员进入该领域，并为神经系统疾病创建可访问的fMRI和深度学习诊断模型。
