scholarly journals Longitudinal infant fNIRS channel-space analyses are robust to variability parameters at the group-level: an image reconstruction investigation

NeuroImage ◽  
2021 ◽  
pp. 118068
Author(s):  
Liam H. Collins-Jones ◽  
Robert J. Cooper ◽  
Chiara Bulgarelli ◽  
Anna Blasi ◽  
Laura Katus ◽  
...  
2021 ◽  
Author(s):  
Samuel H. Forbes ◽  
Sobanawartiny Wijeakumar ◽  
Adam T. Eggebrecht ◽  
Vincent A. Magnotta ◽  
John P. Spencer

AbstractAimWe demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space.ApproachWe synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis.ResultsThis pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data.ConclusionsThe pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.SignificanceImage reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present a novel integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy.


Author(s):  
R. A. Crowther

The reconstruction of a three-dimensional image of a specimen from a set of electron micrographs reduces, under certain assumptions about the imaging process in the microscope, to the mathematical problem of reconstructing a density distribution from a set of its plane projections.In the absence of noise we can formulate a purely geometrical criterion, which, for a general object, fixes the resolution attainable from a given finite number of views in terms of the size of the object. For simplicity we take the ideal case of projections collected by a series of m equally spaced tilts about a single axis.


Author(s):  
Santosh Bhattacharyya

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.


2018 ◽  
Vol 103 (7) ◽  
pp. 703-723 ◽  
Author(s):  
Barbara Nevicka ◽  
Annelies E. M. Van Vianen ◽  
Annebel H. B. De Hoogh ◽  
Bart C. M. Voorn
Keyword(s):  

2013 ◽  
Author(s):  
Yueng-Hsiang Huang ◽  
Dov Zohar ◽  
Michelle Robertson ◽  
Jin Lee ◽  
Jenn Rineer ◽  
...  

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