Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice.

Brian R White,Akiva S Cohen,Daniel J Licht,Jonah A Padawer-Curry,Arjun G Yodh

doi:10.1364/boe.10.005952

Abstract

Resting-state functional connectivity analysis using optical neuroimaging holds the potential to be a powerful bridge between mouse models of disease and clinical neurologic monitoring. However, analysis techniques specific to optical methods are rudimentary, and algorithms from magnetic resonance imaging are not always applicable to optics. We have developed visual processing tools to increase data quality, improve brain segmentation, and average across sessions with better field-of-view. We demonstrate improved performance using resting-state optical intrinsic signal from normal mice. The proposed methods increase the amount of usable data from neuroimaging studies, improve image fidelity, and should be translatable to human optical neuroimaging systems.

Highlights

Optical functional neuroimaging holds promise to link mouse models of neurological disease to the insights about human neuroscience gained from functional magnetic resonance imaging
Resting-state functional connectivity analysis has been adapted for use with optical intrinsic signal (OIS) imaging [4,5] and for fluorescence imaging using voltage-sensitive dyes [6] and geneticallyencoded calcium indicators [7,8,9]
We have developed, demonstrated, and quantified novel visual processing methods to improve the analysis of mouse optical neuroimaging data

Summary

Introduction

Optical functional neuroimaging holds promise to link mouse models of neurological disease to the insights about human neuroscience gained from functional magnetic resonance imaging (fMRI). Resting-state functional connectivity analysis has been adapted for use with optical intrinsic signal (OIS) imaging [4,5] and for fluorescence imaging using voltage-sensitive dyes [6] and geneticallyencoded calcium indicators [7,8,9] These techniques, in turn, are stimulating development of new imaging biomarkers of neurologic disease in preclinical models [10,11,12,13]. One important limitation of this method is that it relies significantly on operator judgement which, we hypothesize, causes unintended variability in border selection This approach is limiting because it does not offer flexibility to remove individual pixels within the larger field-of-view, for example to mask regions of low signal-to-noise due to overlying venous sinuses or optical defects in the cranial window

Methods

Results

Discussion

Conclusion