A robust and adaptive decision-making algorithm for detecting brain networks using functional MRI within the spatial and frequency domain

As the interest in functional connectivity continues to increase among neuroimaging researchers there becomes a greater need to develop an objective method of network identification. The current paper offers a solution to this problem by developing a robust decision making algorithm that can extract a target neural network from an array of spatial maps. We used a probabilistic independent component analysis to generate spatial maps of the Default Mode Network (DMN); however, this adaptive pipeline can be applied to any network of interest. Different template matching algorithms including: Normalized Cross-Correlation, Sum of Squared Differences and Dice Coefficient, were applied to the spatial and frequency domains of the dataset to identify the components that shared the greatest similarity to our DMN template. After identifying components within the resting state, the decision making pipeline selected the components within each method that had the highest matching scores to our DMN template. The final decision of selecting the most prototypical DMN components was made by a comparison between methods. This resulted in a DMN mask that was generated by the components chosen by our decision-making algorithm. To evaluate the accuracy of the decision-maker, a cross-correlation between each final mask and the template was measured. Results indicated that the Normalized Cross Correlation method, using both the spatial and frequency domain, and the Dice Coefficient method, generated the optimal DMN mask. This demonstrates the utility of our algorithm in providing an objective method for network extraction.