基于提出的脑功能网络分析方法NeuroMark，我们开发并发布了相应的工具箱，帮助用户方便地分析脑功能影像数据。在本网页中，我们简要介绍了NeuroMark论文（已发表）的摘要、相关工具箱和相关文档。

Background:

It is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed.

Method:

We propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/ disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers).

Results:

Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.

Conclusion:

We proposed an ICA-based framework to generalize and standardize the calculation of possible functional connectivity features that leverages the benefits of a data-driven approach and also provides comparability across multiple analyses. Via four different example studies, we highlight the validity of this framework. We hope this will be a useful stepping stone towards eventual application of such approaches in the clinic.

文章链接:

https://www.sciencedirect.com/science/article/pii/S2213158220302126

共享文件:

NeuroMark脑功能网络分析是一种对大规模脑功能影像数据进行分析并从中提取脑功能网络和相关测度的方法。基于本方法，我们开发并公开了相应的软件工具包，帮助用户更加便捷的对脑功能影像数据进行分析。在这里，我们共享了以下相关文件：

(1) NeuroMarkICs.rar: 存放以下三个文件。NeuroMark100IC.nii文件包含了100 个独立成分（IC），其中53个成分用作网络模板；Domain_ICN_ID.mat文件包含了上述53个成分的ID及其对应的功能域；Note.txt作为说明文档，对各个文件进行简要介绍。

            NeuroMarkICs.rar

(2) NeuroMarkManual.pdf: NeuroMark软件工具包的说明手册，详细介绍了该软件的开发目的、安装步骤、操作流程等内容。

            NeuroMarkManual.pdf

(3) NeuroMark脑功能网络分析方法的软件工具包使用64位Windows10的操作系统，基于MATLAB2018b开发，下载链接如下:

Download	Version	Upload Time
NeuroMark.rar	Version 1	2021.08.06
NeuroMark.rar	Version 2	2022.01.24
NeuroMark.zip NeuroMark.rar	Version 3	2022.03.31
NeuroMark.rar NeuroMark.zip	Version 4	2022.05.06
NeuroMark.zip NeuroMark.rar	Version 5	2022.05.31
NeuroMarkv.zip NeuroMarkv.rar	Version 6	2022.10.04

使用本软件工具包，请引用：

Y. Du, Z. Fu, J. Sui et al., “NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders,” NeuroImage: Clinical, vol. 28, pp. 102375, 2020.

Y. Du and Y. Fan, "Group information guided ICA for fMRI data analysis," NeuroImage, pp. 157-197, 2013.