A Parameter-efficient Multi-subject Model for Predicting fMRI Activity

2023/08/04 by Connor Lane, Gregory Kiar, Lane, Connor +1
Medicine · Neuroscience · #Advanced MRI Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Functional Brain Connectivity Studies

paper · pdf · doi:10.48550/arxiv.2308.02351

openalex publication_date 2023/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This is the Algonauts 2023 submission report for team "BlobGPT". Our model consists of a multi-subject linear encoding head attached to a pretrained trunk model. The multi-subject head consists of three components: (1) a shared multi-layer feature projection, (2) shared plus subject-specific low-dimension linear transformations, and (3) a shared PCA fMRI embedding. In this report, we explain these components in more detail and present some experimental results. Our code is available at https://github.com/cmi-dair/algonauts23.

Related

• A shared neural encoding model for the prediction of subject-specific fMRI response
• A Multimodal Seq2Seq Transformer for Predicting Brain Responses to Naturalistic Stimuli
• Large Connectome Model: An fMRI Foundation Model of Brain Connectomes Empowered by Brain-Environment Interaction in Multitask Learning Landscape
• Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis
• MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding