Journal Article

Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset

Rastegarnia, Shima, Pinsard, Basile, St-Laurent, Marie, Bellec, Pierre, DuPre, Elizabeth

2023

Overview

•Brain decoding using a dense individual fMRI dataset (∼7 h fMRI data per subject).•Challenging benchmark: decoding 21 conditions from single fMRI brain volumes.•The best individual model reaches 58–67 % accuracy, close to state-of-the-art group decoding (76 %).•Individual decoding models generalized poorly to other subjects. Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-individual variations in functional brain organization challenge accurate decoding performed at the group level. In this paper, we tested whether accurate brain decoding models can be trained entirely at the individual level. We trained several classifiers on a dense individual functional magnetic resonance imaging (fMRI) dataset for which six participants completed the entire Human Connectome Project (HCP) task battery >13 times over ten separate fMRI sessions. We evaluated nine decoding methods, from Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to Graph Convolutional Neural Networks (GCN). All decoders were trained to classify single fMRI volumes into 21 experimental conditions simultaneously, using ∼7 h of fMRI data per participant. The best prediction accuracies were achieved with GCN and MLP models, whose performance (57–67 % accuracy) approached state-of-the-art accuracy (76 %) with models trained at the group level on >1 K hours of data from the original HCP sample. Our SVM model also performed very well (54–62 % accuracy). Feature importance maps derived from MLP —our best-performing model— revealed informative features in regions relevant to particular cognitive domains, notably in the motor cortex. We also observed that inter-subject classification achieved substantially lower accuracy than subject-specific models, indicating that our decoders learned individual-specific features. This work demonstrates that densely-sampled neuroimaging datasets can be used to train accurate brain decoding models at the individual level. We expect this work to become a useful benchmark for techniques that improve model generalization across multiple subjects and acquisition conditions. [Display omitted]

• Publisher: Elsevier Inc,Elsevier Limited,Elsevier
• Subject: Accuracy / Brain - diagnostic imaging / Brain architecture / Brain mapping / Brain research / Cognition & reasoning / Cognitive ability / Communication / Connectome - methods / Cortex (motor) / Datasets / Decoding cognitive states / Deep learning / Dense sampling / fMRI / Functional magnetic resonance imaging / Functional morphology / Humans / Individual brain models / Learning / Machine learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Neural networks / Neural Networks, Computer / Neuroimaging