Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach

TL;DR

This paper introduces a data-driven fMRI parcellation method using ICA and PLS, reducing biases of traditional GLM-based approaches and improving accuracy in identifying brain regions.

Contribution

The authors develop a novel ICA and PLS-based spectral clustering method for fMRI parcellation, eliminating the need for prior HRF and task-related assumptions.

Findings

Improved parcellation accuracy over GLM-based methods

Effective on both single and multi-subject datasets

Preliminary results show reduced intra-parcel variance

Abstract

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM)and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.