Figure 1.
Flowchart of three FRIEND processing pipelines for neurofeedback.
(1) BOLD level real-time display from pre-defined ROIs; (2) Real-time functional connectivity neurofeedback based on the correlation between the signals from different ROIs; (3) Support Vector Machine based neurofeedback, defined on the basis of projected values onto the discriminative hyperplane.
Figure 2.
Typical parameters for a study session in FRIEND (anatomical and functional volumes of reference, number of volumes, and statistical thresholds, among others).
Additional parameters (e.g., % of higher voxels for GLM feature selection, inclusion of motion parameter variables in the GLM model, FWHM values) can be modified by editing an input text file.
Figure 3.
FRIEND’s control window, including: the main menu (A), training and feedback buttons (B), current experimental condition (C), rotation in radians (D), translation in mm (E) and root mean square error from motion parameters (F).
User-defined neurofeedback stimuli to be presented to participants (a thermometer in this case) are displayed when the feedback option is selected (G). For single ROI processing, time-course, mean signal within specified ROIs, signal change and condition blocks will be shown (H). In the case of sliding-window ROI correlation analysis, a similar graph shows the level of correlation, sliding window size and upper and lower bounds of correlation targets (I). During the SVM classification sessions, the interface shows the classification phase, the current scan and the model-based cumulative classification accuracy (J).
Figure 4.
Illustration of how neurofeedback stimuli are defined based on the calculated projections on the SVM discriminant hyperplane.
The black and white circles are observations of two different types of stimuli (e.g., positive and negative emotional condition). The basic concept is that after training a two-class linear SVM, a discriminant hyperplane is defined (in light blue). Next, each new fMRI volume is projected on this hyperplane (decision function) and a score is attributed, reflecting the relative distance from the classification boundary (intersection with separating hyperplane). This score is then categorized in order to determine which visual image will be displayed to the participant as a feedback.
Figure 5.
Example of feedback figures displayed in motor imagery (left) and emotional (right) neurofeedback protocols.
FRIEND provides default neurofeedback figures (thermometer and rings), but user-defined ones may be used instead. The displayed words (GO/STOP and positive/negative) are cues for the specific task to be performed by participants.
Figure 6.
Real-time brain activation mapping, depicting the ratio [(average BOLD signal of the ROI during the three last scans) – (average BOLD signal of the ROI during the previous baseline condition)] / (average BOLD signal of the ROI during the previous baseline condition) for each voxel on the participant’s native space using an arbitrary image threshold.
Figure 7.
Weight map from the SVM classification of a participant of an emotional memory neurofeedback session.
The weights are SVM coefficients determining the discriminant hyperplane, which depicts the relevance of each voxel for the classification between positive and negative conditions. Blue/red colors refer to the sign of these coefficients (negative/positive, respectively). FRIEND saves a NIfTI file containing these maps, which can be viewed using any MRI visualization software. This is only an illustrative map with an arbitrary threshold and slice selection.
Figure 8.
Correlation curves from the second session from all three subjects.
Green lines represent head motion (RMS threshold). Gray columns represent subtraction blocks, while emotional blocks are represented in blue (indignation) and red (guilt) columns. The initial five volumes, which are discarded from the correlation calculus, are shown in black. The correlation was computed using a sliding window of 10 volumes.