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Related Experiment Video

Updated: Jun 23, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Modeling fMRI data generated by overlapping cognitive processes with unknown onsets using Hidden Process Models.

Rebecca A Hutchinson¹, Radu Stefan Niculescu, Timothy A Keller

¹Computer Science Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. rah@cs.cmu.edu

|May 22, 2009

Summary

This summary is machine-generated.

We introduce Hidden Process Models (HPMs) for analyzing functional magnetic resonance imaging (fMRI) data. This new method models unknown timing of mental processes, improving fMRI time series analysis.

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Area of Science:

Neuroimaging
Cognitive Neuroscience
Computational Neuroscience

Background:

Functional magnetic resonance imaging (fMRI) is a key tool for understanding brain activity.
Existing fMRI models often assume known stimulus-evoked mental processes.
Limitations exist in modeling processes with unknown or stimulus-independent timing.

Purpose of the Study:

To introduce Hidden Process Models (HPMs) for advanced fMRI time series analysis.
To provide a probabilistic framework for simultaneously learning process contributions and timing.
To enable model selection for differing numbers and types of underlying mental processes.

Main Methods:

Developed a principled, probabilistic framework for Hidden Process Models (HPMs).
Implemented learning and inference algorithms for HPMs.
Employed cross-validated data log-likelihood for model comparison.

Main Results:

Demonstrated the utility of HPMs on simulated and real fMRI data.
Successfully modeled overlapping mental processes with unknown timings.
Showcased HPMs' capability in evaluating competing process models.

Conclusions:

Hidden Process Models (HPMs) offer a robust framework for fMRI time series analysis.
HPMs advance the field by accommodating un-timed and stimulus-independent mental processes.
This method enhances the understanding of complex cognitive architectures from fMRI data.