Blog
About

  • Record: found
  • Abstract: found
  • Article: not found

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

Read this article at

ScienceOpenPublisherPubMed
Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      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.

      Related collections

      Author and article information

      Affiliations
      [1 ] Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, 22 South Greene Street, Baltimore, MD, 21201, USA. rchen@umm.edu.
      [2 ] Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, 22 South Greene Street, Baltimore, MD, 21201, USA.
      Journal
      Neuroinformatics
      Neuroinformatics
      Springer Nature
      1559-0089
      1539-2791
      Apr 2016
      : 14
      : 2
      26662457 10.1007/s12021-015-9290-5 10.1007/s12021-015-9290-5

      Comments

      Comment on this article