A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity

TL;DR

This paper introduces a multi-task deep learning framework that uses dynamic functional connectivity from resting-state fMRI to accurately localize eloquent cortex areas in brain tumor patients, aiding preoperative planning.

Contribution

It presents a novel multi-task deep learning approach that leverages graph-based features and LSTM attention to improve localization accuracy and handle missing data in brain tumor patients.

Findings

Achieves higher localization accuracy than conventional methods

Successfully identifies bilateral language areas from unilateral training data

Handles missing patient data effectively

Abstract

We present a novel deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex in brain tumor patients. Our method leverages convolutional layers to extract graph-based features from the dynamic connectivity matrices and a long-short term memory (LSTM) attention network to weight the relevant time points during classification. The final stage of our model employs multi-task learning to identify different eloquent subsystems. Our unique training strategy finds a shared representation between the cognitive networks of interest, which enables us to handle missing patient data. We evaluate our method on resting-state fMRI data from 56 brain tumor patients while using task fMRI activations as surrogate ground-truth labels for training and testing. Our model achieves higher localization accuracies than conventional deep learning approaches and can identify bilateral language areas even when trained on left-hemisphere lateralized cases. Hence, our method may ultimately be useful for preoperative mapping in tumor patients.