Robust activation detection methods for real-time and offline fMRI analysis. (June 2017)

• Record Type: Journal Article
• Authors: Oguz, Kaya; Cinsdikici, Muhammed G.; Gonul, Ali Saffet
• Is Part Of: Computer methods and programs in biomedicine. Volume 144(2017)
• Page Start: 1
• Page End: 11
• Publication Date: 2017-06
• Subjects: fMRI -- Activation estimation -- Robust regression -- Instantaneous activation -- Real-time fMRI; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.03.015
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: New approaches to fMRI activation detection are proposed. Instantaneous methods enable activation estimation to stimuli at specific instants. New metric based on regression analysis is proposed that can be used in estimation. Methods are tested with two synthetic data sets and real fMRI data. The analysis is done with ROC curves, and PSNR values and yield successful results. Abstract: We propose two contributions with novel approaches to fMRI activation analysis. The first is to apply confidence intervals to locate activations in real-time, and second is a new metric based on robust regression of fMRI signals. These contributions are implemented in our four proposed methods; Instantaneous Activation Method (IAM), Instantaneous Activation Method with Past Blocks (IAMP) for real-time analysis, Task Robust Regression Distance Method (TRRD) for the new metric with robust regression and Instantaneous Robust Regression Distance Method (IRRD) for both contributions. For comparison, a statistical offline method called Task Activation Method (TAM) and a correlation analysis method are also implemented. The methods are initially evaluated with synthetic data generated using two different approaches; first using varying hemodynamic response function signals to simulate a wide range of stimuli responses, along with a Gaussian white noise, and second using no activity state data of a real fMRI experiment, which removes the need to generate noise. The methods are also tested with real fMRI experiments and compared with the results obtained by the widely used SPM tool. The results show that instantaneous methods reveal activations that are lost statistically in an offline analysis. They also reveal further improvements by robust fitting application, which minimizes the outlier effect. TRRD has an area under the ROC curve of 0, 7127 for very noisy synthetic images, is reaching up to 0, 9608 as the noise decreases, while the instantaneous score is in the range of 0, 6124 to 0, 8019 in the same noise levels.