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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Dynamic linear model analysis of optical imaging data acquired from the human neocortex.

Michael Lavine¹, Michael M Haglund, Daryl W Hochman

¹Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, United States.

Journal of Neuroscience Methods

|June 7, 2011

Summary

A new Bayesian dynamic linear modeling approach effectively reduces noise in intrinsic optical signal imaging (ImIOS) data from human neocortical tissue. This method enhances the reliability of ImIOS for clinical and experimental studies, enabling quantitative analysis of brain activity.

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Area of Science:

Neuroscience
Biomedical Engineering
Optical Imaging

Background:

Neuronal activity alters light absorption and scattering in neocortical tissue.
Imaging of intrinsic optical signals (ImIOS) maps these activity-evoked optical changes.
Current ImIOS data from human studies is noisy, limiting its clinical and experimental utility.

Purpose of the Study:

To develop methods for noise artifact removal and statistical analysis of ImIOS data.
To improve the reliability and usefulness of ImIOS for human studies.
To enable quantitative analysis of intraoperative ImIOS data for neurosurgical mapping.

Main Methods:

A Bayesian, dynamic linear modeling (DLM) approach was developed.
The DLM incorporated cyclic components for heartbeat and respiration artifacts.
A local linear component modeled activity-evoked responses.
The model was tested on ImIOS data from six human subjects using 535nm or 660nm light.

Main Results:

The DLM successfully reduced noise artifacts in ImIOS data.
The model reliably preserved activity-evoked optical responses.
Quantitative analysis of intraoperative data during cortical stimulation demonstrated DLM's utility.

Conclusions:

The developed DLM approach effectively addresses noise issues in human ImIOS data.
This method enhances the reliability and quantitative analysis capabilities of ImIOS.
The DLM shows promise for intraoperative neurosurgical mapping and research applications.