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NeuroMark: A brain functional network analysis method, associated toolbox, and related documents

时间:2019-09-21 09:01:36   来源:  点击:[853]

Based on the proposed NeuroMark, a brain functional network analysis method, we develop and release a corresponding toolbox to help users analyze brain functional imaging data conveniently. Here, we briefly introduce the abstract of our published paper, associated toolbox, and related documents.

 

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.

 

Link of the paper:

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

 

Sharing files (For details, please check the attachments including the NeuroMark toolbox and related documents):

(1) NeuroMarkICs.rar includes 100 independent components (ICs), in which 53 ICs are used as the network templates. The IC IDs of the 53 templates and their functional domains are included in Domain_ICN_ID.mat.

NeuroMarkICs.rar


(2) Toolbox:                                    

DownloadVersionUpload Time
NeuroMark.rar
Version 12021.08.06
NeuroMark.rar
Version 22022.01.24

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

NeuroMark.zip

NeuroMark.rar

Version 6

2022.10.04


(3) NeuroMarkManual.pdf is the manual of the toolbox.

.NeuroMarkManual.pdf


Please cite as:

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.