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

Neuroinformatics. 2016 Apr;14(2):191-9. doi: 10.1007/s12021-015-9290-5.


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.

Keywords: Brain-behavior analysis; Functional magnetic resonance imaging; Resting-state; Seed-based analysis; Software.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Analysis of Variance
  • Autism Spectrum Disorder / pathology
  • Behavior
  • Biomarkers / metabolism
  • Brain / anatomy & histology
  • Brain / diagnostic imaging*
  • Brain / pathology
  • Brain Mapping*
  • Child
  • Data Mining / statistics & numerical data*
  • Female
  • Functional Laterality
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging
  • Male
  • Neural Pathways / anatomy & histology
  • Neural Pathways / diagnostic imaging*
  • Oxygen / blood


  • Biomarkers
  • Oxygen