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Advanced Connectivity Analysis (ACA): a Large Scale Functional Connectivity Data Mining Environment

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Abstract

Using resting-state functional magnetic resonance imaging (rs-fMRI) to study functional connectivity is of great importance to understand normal development and function as well as a host of neurological and psychiatric disorders. Seed-based analysis is one of the most widely used rs-fMRI analysis methods. Here we describe a freely available large scale functional connectivity data mining software package called Advanced Connectivity Analysis (ACA). ACA enables large-scale seed-based analysis and brain-behavior analysis. It can seamlessly examine a large number of seed regions with minimal user input. ACA has a brain-behavior analysis component to delineate associations among imaging biomarkers and one or more behavioral variables. We demonstrate applications of ACA to rs-fMRI data sets from a study of autism.

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Acknowledgments

This research has been funded by the Center for Health-related Informatics and Bioimaging (CHIB) under Maryland’s MPower Initiative.

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Authors and Affiliations

  1. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, 22 South Greene Street, Baltimore, MD, 21201, USA

    Rong Chen, Erika Nixon & Edward Herskovits

Authors
  1. Rong Chen
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  2. Erika Nixon
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  3. Edward Herskovits
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Corresponding author

Correspondence to Rong Chen.

Appendix

Appendix

An Example Study

In this example, we demonstrate how to implement a large-scale seed-based analysis using ACA. This example is included in the ACA package as a test data set. We analyzed imaging and behavior data from the Nathan Kline Institute (NKI)/Rockland sample (downloaded from http://fcon_1000.projects.nitrc.org/indi/pro/nki.html). The NKI/Rockland sample includes 204 normal subjects. For each subject, a ten minute rs-fMRI scan was provided. In this example, our primary outcome variable was age. The validation behavior variable was the score of the Delis-Kaplan Executive Function sorting test (confirmed correct sorts), which is a problem solving task (Homack et al. 2005). This behavior variable has been implicated in general overall executive function which is associated with age.

The T1 and rs-fMRI data were preprocessed using ACA. For each seed region, the rsFC maps for all subjects were saved in a directory. For seed-based analysis, the first step was to create a project file. We created a CSV file to define the imaging data, consisting of three columns: the first column was the subject name, the second column was the primary outcome variable, and the third column represented whether or not this subject was excluded. Then we created a CSV file, which included the behavior variables. The first column of the behavior variable file was the subject ID; and the subsequent variables were behavior variables. For both the imaging and behavior CSV files, we created an associated data type file. If a variable was continuous, its type was coded as ‘1’. If a variable was categorical, its type was coded as ‘2’.

When the imaging and behavior CSV files were complete, we conducted the analysis. The user interface of ACA is simple. We opened a Linux/Unix terminal, went to the project home directory which included the raw rsFC maps, and the imaging and behavior CSV file, and then typed commands. For step by step analysis, we first typed aca_sba_ana_1a.sh to prepare data and generate study masks. Then we typed aca_sba_ana_2a.sh, aca_sba_ana_2b.sh, or aca_sba_ana_2c.sh for biomarker detection. aca_sba_ana_2a.sh is for biomarker detection based on regression, aca_sba_ana_2b.sh is based on ANOVA, and aca_sba_ana_2c.sh is GAMMA-based biomarker detection. In this example, the primary outcome variable was a continuous variable; therefore, we used aca_sba_ana_2a.sh for biomarker detection. After biomarker detection, we typed aca_sba_ana_3a.sh for brain-behavior analysis. Then we typed aca_sba_summarize_1.sh to summarize the detected biomarkers.

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Chen, R., Nixon, E. & Herskovits, E. Advanced Connectivity Analysis (ACA): a Large Scale Functional Connectivity Data Mining Environment. Neuroinform 14, 191–199 (2016). https://doi.org/10.1007/s12021-015-9290-5

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