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Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography

Okito Yamashita¹, Takeaki Shimokawa², Ryota Aisu³

¹Neural Information Analysis Laboratories, ATR, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan; Brain Functional Imaging Technologies Group, CiNet, 1-4 Yamadaoka, Suita City, Osaka 565-0871, Japan.

|May 7, 2016

Summary

This summary is machine-generated.

A new hierarchical Bayesian diffuse optical tomography (DOT) algorithm accurately reconstructs brain activity. This advanced DOT method shows improved spatial accuracy and fewer false positives compared to existing techniques.

Keywords:

Diffuse optical tomography Image reconstruction NIRS Scalp blood flow

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Area of Science:

Neuroimaging
Biomedical Engineering
Medical Physics

Background:

Diffuse optical tomography (DOT) enhances near-infrared spectroscopy (NIRS) spatial resolution.
A hierarchical Bayesian DOT algorithm was previously proposed for simultaneous scalp and cortical hemodynamic reconstruction.

Purpose of the Study:

To validate a hierarchical Bayesian DOT algorithm across multiple subjects and tasks.
To compare the algorithm's performance against functional magnetic resonance imaging (fMRI) and a gold-standard DOT method.

Main Methods:

High-density NIRS and fMRI data were collected from 12 subjects during graded motor tasks (hand movement, finger movement, no-movement).
The hierarchical Bayesian DOT algorithm and a gold-standard DOT method were used for image reconstruction.
Reconstruction accuracy was evaluated against fMRI blood-oxygenation-level-dependent (BOLD) signals.

Main Results:

The Bayesian DOT method achieved median localization errors of 6mm (hand task) and 8mm (finger task).
Spatial-pattern similarity was 0.6 for the hand task and 0.4 for the finger task.
The algorithm correctly identified no activity during the no-movement task and demonstrated fewer false positives than the gold-standard method.

Conclusions:

The hierarchical Bayesian DOT algorithm is valid for multi-subject, multi-task brain activity reconstruction.
The method offers improved spatial-pattern similarity and reduced false positives compared to the gold-standard DOT approach.