Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis

TL;DR

This paper introduces the Multi-Objective Cognitive Model (MOCM), a unified approach for multi-subject fMRI analysis that improves stability and performance over traditional disjoint pipeline methods.

Contribution

It proposes a novel integrated multi-objective optimization framework for MVP analysis, enhancing stability and accuracy in decoding brain activity.

Findings

MOCM outperforms existing methods in stability and accuracy

The integrated approach yields superior results on multi-subject fMRI datasets

Empirical results demonstrate improved cognitive decoding performance

Abstract

In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces the Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques.