Deep Labeling of fMRI Brain Networks
Ammar Ahmed Pallikonda Latheef Sejal Ghate Zhipeng Hui Alberto Santamaria-Pang Ivan Tarapov Haris I Sair Craig K Jones
Resting State Networks (RSNs) of the brain extracted from Resting State
functional Magnetic Resonance Imaging (RS-fMRI) are used in the pre-surgical
planning to guide the neurosurgeon. This is difficult, though, as expert
knowledge is required to label each of the RSNs. There is a lack of efficient
and standardized methods to be used in clinical workflows. Additionally, these
methods need to be generalizable since the method needs to work well regardless
of the acquisition technique. We propose an accurate, fast, and lightweight
deep learning approach to label RSNs. Group Independent Component Analysis
(ICA) was used to extract large scale functional connectivity patterns in the
cohort and dual regression was used to back project them on individual subject
RSNs. We compare a Multi-Layer Perceptron (MLP) based method with 2D and 3D
Convolutional Neural Networks (CNNs) and find that the MLP is faster and more
accurate. The MLP method performs as good or better than other works despite
its compact size. We prove the generalizability of our method by showing that
the MLP performs at 100% accuracy in the holdout dataset and 98.3% accuracy in
three other sites' fMRI acquisitions.