The Use of a priori Information in ICA-Based Techniques for Real-Time fMRI: An Evaluation of Static/Dynamic and Spatial/Temporal Characteristics

Abstract

Real-time brain functional MRI (rt-fMRI) allows in vivo non-invasive monitoring of neural networks. The use of multivariate data-driven analysis methods such as independent component analysis (ICA) offers an attractive trade-off between data interpretability and information extraction, and can be used during both task-based and rest experiments. The purpose of this study was to assess the effectiveness of different ICA-based procedures to monitor in real-time a target IC defined from a functional localizer which also used ICA. Four novel methods were implemented to monitor ongoing brain activity in a sliding window approach. The methods differed in the ways in which a priori information, derived from ICA algorithms, was used to monitor a target independent component (IC). We implemented four different algorithms, all based on ICA. One Back-projection method used ICA to derive static spatial information from the functional localizer, off-line, which was then back-projected dynamically during the real-time acquisition. The other three methods used real-time ICA algorithms that dynamically exploited temporal, spatial, or spatial-temporal priors during the real-time acquisition. The methods were evaluated by simulating a rt-fMRI experiment that used real fMRI data. The performance of each method was characterized by the spatial and/or temporal correlation with the target IC component monitored, computation time, and intrinsic stochastic variability of the algorithms. In this study the Back-projection method, which could monitor more than one IC of interest, outperformed the other methods. These results are consistent with a functional task that gives stable target ICs over time. The dynamic adaptation possibilities offered by the other ICA methods proposed may offer better performance than the Back-projection in conditions where the functional activation shows higher spatial and/or temporal variability.

High-Speed Real-Time Resting-State fMRI Using Multi-Slab Echo-Volumar Imaging

“…In contrast, data driven approaches, such as ICA, in single subjects may require considerable user interaction to interpret resulting maps and time courses. Semi-automated data sorting routines for ICA are under development, but actual real-time applications have not yet been demonstrated (Soldati et al, 2013a,b). A model-based approach such as seed-based correlation analysis that uses prior knowledge is advantageous compared to ICA for detecting small signal changes.…”

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository

“…Recent advances in real-time fMRI (rt-fMRI) [16, 17, 18] have made it possible to provide participant-specific feedback about neural networks. These advances permit the addition of instructions to modulate given neural networks as well as the assessment of an individuals ability to follow the instructions or modulate the specific network.…”

The Real-time fMRI Neurofeedback Based Stratification of Default Network Regulation Neuroimaging Data Repository

“…While psychological functions related to the DMN can be targeted, the DMN itself is somewhat more difficult to target, as the functions it instantiates are presumably multiply determined (i.e., invoking specific aspects of the DMN and/or other networks) and varied (i.e., different in nature and possibly kind) [15]. Recent advances in real-time fMRI (rt-fMRI) [16,17,18] have made it possible to provide participant-specific feedback about neural networks. These advances permit the addition of instructions to modulate given neural networks as well as the assessment of an individual's ability to follow the instructions or modulate the specific network.…”