Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)

TL;DR

This paper introduces a spatio-temporal CNN model that effectively captures brain network patterns from 4D fMRI data, enabling accurate and generalizable identification of the Default Mode Network across diverse datasets and tasks.

Contribution

The paper presents a novel ST-CNN architecture that jointly learns spatial and temporal features for brain network identification, demonstrating superior performance and generalizability.

Findings

ST-CNN accurately identifies DMN from various datasets.

Joint learning of spatial and temporal features improves performance.

Model generalizes well across different cognitive tasks.

Abstract

Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis. Inspired by the recent success in applying deep learning for functional brain decoding and encoding, in this work we propose a spatio-temporal convolutional neural network (ST-CNN)to jointly learn the spatial and temporal patterns of targeted network from the training data and perform automatic, pin-pointing functional network identification. The proposed ST-CNN is evaluated by the task of identifying the Default Mode Network (DMN) from fMRI data. Results show that while the framework is only trained on one fMRI dataset,it has the sufficient generalizability to identify the DMN from different populations of data as well as different cognitive tasks. Further investigation into the results show that the superior performance of ST-CNN is driven by the jointly-learning scheme, which capture the intrinsic relationship between the spatial and temporal characteristic of DMN and ensures the accurate identification.