Deep Labeling of fMRI Brain Networks

• URL: http://arxiv.org/abs/2305.03814v1
• Date: Fri, 5 May 2023 19:42:11 GMT
• Title: Deep Labeling of fMRI Brain Networks
• Authors: Ammar Ahmed Pallikonda Latheef (1), Sejal Ghate (2), Zhipeng Hui (1), Alberto Santamaria-Pang (3), Ivan Tarapov (3), Haris I Sair (4 and 5), and Craig K Jones (1, 4 and 5) ((1) Department of Computer Science, Johns Hopkins University, (2) Department of Biomedical Engineering, Johns Hopkins University, (3) Health AI, Microsoft, Redmond Washington, (4) Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, (5) Malone Center for Engineering in Healthcare, Johns Hopkins University)
• Abstract summary: 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. There is a lack of efficient and standardized methods to be used in clinical acquisitions. We propose an accurate, fast, and lightweight deep learning approach to label RSNs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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