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Parameter clustering in Bayesian functional principal component analysis of neuroscientific data.

Nicolò Margaritella¹, Vanda Inácio¹, Ruth King¹

¹School of Mathematics, University of Edinburgh, Edinburgh, UK.

Statistics in Medicine

|October 11, 2020

Summary

We introduce parameter clustering functional principal component analysis (PCl-fPCA), a novel model for analyzing complex brain data. This method enhances understanding of spatiotemporal patterns in neuroscientific recordings.

Keywords:

Bayesian hierarchical models Dirichlet process clustering functional data analysis neuroscience spatiotemporal data

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Area of Science:

Neuroscience
Statistics
Data Science

Background:

Neuroscientific technology has generated complex spatiotemporal datasets.
Existing models may oversimplify intricate patterns in brain recordings.
Advanced analytical methods are needed for meaningful pattern identification.

Purpose of the Study:

To propose a novel model, parameter clustering functional principal component analysis (PCl-fPCA), for exploring complex spatiotemporal neuroscientific data.
To develop a computationally feasible approach for signal reconstruction and pattern discovery.
To enhance the understanding of brain time series data.

Main Methods:

Merging functional data analysis and Bayesian nonparametrics.
Utilizing a Dirichlet process Gaussian mixture model to cluster functional principal component scores within a Bayesian functional PCA framework.
Capturing spatial dependence and temporal interactions without prior spatial assumptions.

Main Results:

Demonstrated improved curve and correlation reconstruction in simulation studies compared to existing Bayesian and frequentist functional PCA models.
Successfully applied the PCl-fPCA method to functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) data.
Provided a rich exploration of spatiotemporal dependence in brain time series.

Conclusions:

PCl-fPCA offers a flexible and computationally feasible method for analyzing complex neuroscientific data.
The model enhances insight into spatiotemporal dynamics by clustering functional principal component scores.
This approach advances the analysis of fMRI and EEG data, revealing intricate patterns in brain activity.