Brain response pattern identification of fMRI data using a particle swarm optimization-based approach

Brain Informatics
Xinpei MaW Art Chaovalitwongse

Abstract

Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby's dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compa...Continue Reading

References

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Citations

Aug 11, 2018·Neuroinformatics·Muhammad Yousefnezhad, Daoqiang Zhang
Feb 23, 2020·Computational Intelligence and Neuroscience·Alejandro VelozCarla Taramasco
Jul 31, 2020·Data Mining and Knowledge Discovery·Wei ZhangJr-Shin Li

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Methods Mentioned

BETA
feature extraction

Software Mentioned

qquad
SVM
MVPA
HHPSO
scikit
learn
SPM
PSO

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