Functional Connectome Fingerprinting Using Convolutional and Dictionary Learning

TL;DR

This paper introduces a novel framework combining convolutional autoencoders and sparse dictionary learning to improve individual brain fingerprinting accuracy using fMRI data, demonstrating significant performance gains.

Contribution

It presents a new method that integrates deep autoencoders with sparse coding to enhance the identification of individual-specific brain connectivity patterns.

Findings

Achieved a 10% improvement over baseline models on HCP data.

Demonstrated the effectiveness of combining deep learning with sparse coding.

Showed potential for scalable personalized neuroscience applications.

Abstract

Advances in data analysis and machine learning have revolutionized the study of brain signatures using fMRI, enabling non-invasive exploration of cognition and behavior through individual neural patterns. Functional connectivity (FC), which quantifies statistical relationships between brain regions, has emerged as a key metric for studying individual variability and developing biomarkers for personalized medicine in neurological and psychiatric disorders. The concept of subject fingerprinting, introduced by Finn et al. (2015), leverages neural connectivity variability to identify individuals based on their unique patterns. While traditional FC methods perform well on small datasets, machine learning techniques are more effective with larger datasets, isolating individual-specific features and maximizing inter-subject differences. In this study, we propose a framework combining convolutional autoencoders and sparse dictionary learning to enhance fingerprint accuracy. Autoencoders capture shared connectivity patterns while isolating subject-specific features in residual FC matrices, which are analyzed using sparse coding to identify distinctive features. Tested on the Human Connectome Project dataset, this approach achieved a 10% improvement over baseline group-averaged FC models. Our results highlight the potential of integrating deep learning and sparse coding techniques for scalable and robust functional connectome fingerprinting, advancing personalized neuroscience applications and biomarker development.