BrainNET: Inference of Brain Network Topology Using Machine Learning

Gowtham Krishnan Murugesan, Chandan Ganesh, Sahil Nalawade, Elizabeth M. Davenport, Ben Wagner, Won Hwa Kim, Joseph A. Maldjian

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. Methods: BrainNET is based on extremely randomized trees to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open-source simulated fMRI data of 50 subjects in 28 different simulations under various confounding conditions with known ground truth were used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated in a publicly available attention-deficit/hyperactivity disorder (ADHD) data set, including 134 typically developing children (mean age: 12.03, males: 83), 75 ADHD inattentive (mean age: 11.46, males: 56), and 93 ADHD combined (mean age: 11.86, males: 77) subjects. Network topologies in ADHD were inferred using BrainNET, correlation, and PC. Graph metrics were extracted to determine differences between the ADHD groups. Results: BrainNET demonstrated excellent performance across all simulations and varying confounders in identifying the true presence of connections. In the ADHD data set, BrainNET was able to identify significant changes (p < 0.05) in graph metrics between groups. No significant changes in graph metrics between ADHD groups were identified using correlation and PC. Conclusion: We describe BrainNET, a new network inference method to estimate fMRI connectivity that was adapted from gene regulatory methods. BrainNET out-performed Pearson correlation and PC in fMRI simulation data and real-world ADHD data. BrainNET can be used independently or combined with other existing methods as a useful tool to understand network changes and to determine the true network topology of the brain under various conditions and disease states. Developed a new functional magnetic resonance image (fMRI) network inference method named as BrainNET using machine learning. BrainNET out-performed Pearson correlation and partial correlation in fMRI simulation data and real-world attention-deficit/hyperactivity disorder data. BrainNET does not need to be pretrained and can be applied to infer fMRI network topology independently on individual subjects and for varying number of nodes.

Original languageEnglish (US)
Pages (from-to)422-435
Number of pages14
JournalBrain Connectivity
Issue number8
StatePublished - Oct 2020


  • brain
  • connectivity analysis
  • fMRI
  • machine learning

ASJC Scopus subject areas

  • Neuroscience(all)


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