A Collection of Brain Network Datasets

This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics.Anatomical and functional MRI images of the brain have been used to understand the functional connectivity of the human brain and are particularly important in identifying underlying neurodegenerative conditions such as Alzheimer's, Parkinson's, and Autism. Recently, the study of the brain in the form of brain networks using machine learning and graph analytics has become increasingly popular, especially to predict the early onset of these conditions. A brain network, represented as a graph, retains richer structural and positional information that traditional examination methods are unable to capture. However, the lack of brain network data transformed from functional MRI images prevents researchers from data-driven explorations. One of the main difficulties lies in the complicated domain-specific preprocessing steps and the exhaustive computation required to convert data from MRI images into brain networks. We bridge this gap by collecting a large amount of available MRI images from existing studies, working with domain experts to make sensible design choices, and preprocessing the MRI images to produce a collection of brain network datasets. The datasets originate from 6 different sources, cover 4 neurodegenerative conditions, and consist of a total of 2,688 subjects.Due to the data protocol, we are unable to release the ADNI dataset here. The data will be released via the ADNI external data submissions within their data system.We test our graph datasets on 5 machine learning models commonly used in neuroscience and on a recent graph-based analysis model to validate the data quality and to provide domain baselines. To lower the barrier to entry and promote the research in this interdisciplinary field, we release our complete preprocessing details, codes, and brain network data: https://github.com/brainnetuoa/data_driven_network_neuroscience.To stay informed about the new updates of the datasets, kindly provide us with your email address:https://forms.gle/KGAajR6LEysXWKvKAUpdated on 10/09/2024:Please note that we have identified 14 subjects in the PPMI (Parkinson's Progression Markers Initiative) dataset, prodromal group, where the time-series images include only 10 time slots. The invalid subjects are:sub-prodromal103857sub-prodromal120622sub-prodromal146573sub-prodromal40737sub-prodromal52874sub-prodromal55560sub-prodromal56680sub-prodromal58027sub-prodromal58680sub-prodromal59390sub-prodromal59483sub-prodromal59503sub-prodromal71658sub-prodromal75422We have removed the invalid images, and updated the dataset by including both the parcellated images (ppmi_v2.zip) and the preprocessed images (Ppmi_Preprocessed_v2.z*).

本文展示了一组全面且高质量的人类脑功能网络数据集，旨在促进神经科学、机器学习和图分析领域的交叉研究。通过使用脑部解剖和功能MRI图像，本研究深入理解了人类大脑的功能连接性，这对于识别潜在的神经退行性疾病，如阿尔茨海默病、帕金森病和自闭症等，尤为重要。近年来，运用机器学习和图分析对脑网络的研究日益盛行，尤其是在预测这些疾病的早期发病方面。脑网络以图的形式呈现，保留了比传统检查方法更丰富的结构和位置信息。然而，由于缺乏由功能MRI图像转换而来的脑网络数据，研究人员的数据驱动探索受到了限制。主要困难之一在于将MRI图像转换为脑网络所需的复杂领域特定预处理步骤和庞大的计算量。本团队通过收集现有研究中的大量MRI图像，与领域专家合作进行合理的方案设计，并对MRI图像进行预处理，从而构建了一个脑网络数据集集合。这些数据集源自6个不同的来源，涵盖了4种神经退行性疾病，共计包含2,688名受试者。由于数据协议的限制，我们无法在此发布ADNI数据集。数据将通过ADNI外部数据提交系统发布。我们对这些图数据集在神经科学中常用的5种机器学习模型以及一种基于图的最新分析模型进行了测试，以验证数据质量并提供领域基准。为了降低进入门槛并促进这一跨学科领域的研究，我们发布了完整的预处理细节、代码和脑网络数据：https://github.com/brainnetuoa/data_driven_network_neuroscience。如需了解数据集的最新更新，请提供您的电子邮件地址：https://forms.gle/KGAajR6LEysXWKvKA。更新于2024年10月9日：请注意，我们在PPMI（帕金森病进展标志计划）数据集中识别出14名受试者，其时间序列图像仅包含10个时间槽。无效的受试者包括：sub-prodromal103857、sub-prodromal120622、sub-prodromal146573、sub-prodromal40737、sub-prodromal52874、sub-prodromal55560、sub-prodromal56680、sub-prodromal58027、sub-prodromal58680、sub-prodromal59390、sub-prodromal59483、sub-prodromal59503、sub-prodromal71658、sub-prodromal75422。我们已经移除了无效图像，并通过包括分割图像（ppmi_v2.zip）和预处理图像（Ppmi_Preprocessed_v2.z*）的方式更新了数据集。