Special Issue "Algorithms for Functional Near-Infrared Spectroscopy, Cerebral Oximetry and Near-Infrared Imaging"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 March 2018)

Special Issue Editors

Guest Editor
Dr. Felix Scholkmann

Biomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Switzerland
Website | E-Mail
Interests: biophotonics and neurophotonics (functional near-infrared spectroscopy, cerebral oximetry, photobiology); biomedical signal-processing; integrative human physiology; neuroscience; chronobiology; biophysics; evolutionary and functional medicine; astrophysics
Guest Editor
Dr. Alexander Caicedo

ESAT-STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, 3001 Leuven
Website | E-Mail
Interests: biomedical signal-processing; cerebral oximetry; neurophysiology in the newborns; machine learning; linear algebra; neuroscience

Special Issue Information

Dear Colleagues,

The open access journal Algorithms is planning a Special Issue entitled “Algorithms for Functional
Near-Infrared Spectroscopy, Cerebral Oximetry and Near-Infrared Imaging”.

Near-infrared spectroscopy (NIRS) is increasingly being used for optical brain imaging (functional near-infrared spectroscopy, fNIRS), monitoring the absolute oxygenation of human brain tissue (cerebral oximetry, CO), and imaging of tissue perfusion and oxygenation (near-infrared imaging, NIRI).

In the last couple of years, several novel signal processing algorithms and methods for fNIRS, CO and NIRI have been developed. This trend is set to continue as the quality (e.g., accuracy, precision, robustness against artifacts, user-friendliness) of measurement techniques requires further improvement. Impulses will also come from the need to apply adequate signal-processing algorithms and methods to the recorded signals in order to extract and quantify physiologically relevant information. Advances in signal processing and data analysis of fNIRS, CO and NIRI signals will help to increase the usability of these techniques in basic research (human neuroscience, human physiology) as well as medical applications (e.g., patient monitoring, diagnosis and treatment control).

The Special Issue is intended as a platform for exchanging and proposing new algorithms, methods and ideas related to signal processing and data analysis of fNIRS, CO and NIRI measurements. We look forward to a great deal of interesting input from the research community.

The following is a (non-exhaustive) list of topics of interest but we also welcome new ideas:  

  • Detection of evoked and/or resting-state brain activity
  • Decomposition of the signals into physiological meaningful components (e.g., components from the intracerebral and extracerebral tissue, components related to brain activity or systemic physiology, etc.)
  • Removal of confounding component (scalp blood flow, changes in systemic physiology) from the measured signals
  • New NIRS algorithms to determine absolute chromophore concentrations or relative changes in tissue
  • New parameters that quantify changes in hemodynamics and oxygenation
  • Measurement artifacts detection and removal
  • New techniques and signal processing pipelines for fNIRS, OX and NIRI applications

Dr. Felix Scholkmann
Dr. Alexander Caicedo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Functional near-infrared spectroscopy (fNIRS)
  • Cerebral oximetry (CO)
  • Near-infrared imaging (NIRI)
  • Biomedical signal-processing
  • Algorithms to quantify tissue hemodynamics and oxygenation
  • Artifact detection and removal
  • Time-frequency analysis
  • Spectroscopy
  • Signal decomposition
  • Regression and adaptive filtering

Published Papers (3 papers)

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Research

Open AccessArticle The NIRS Brain AnalyzIR Toolbox
Algorithms 2018, 11(5), 73; https://doi.org/10.3390/a11050073
Received: 30 March 2018 / Revised: 5 May 2018 / Accepted: 12 May 2018 / Published: 16 May 2018
Cited by 4 | PDF Full-text (3182 KB) | HTML Full-text | XML Full-text
Abstract
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650–900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state
[...] Read more.
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650–900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics. Full article
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Open AccessArticle Estimating Functional Connectivity Symmetry between Oxy- and Deoxy-Haemoglobin: Implications for fNIRS Connectivity Analysis
Algorithms 2018, 11(5), 70; https://doi.org/10.3390/a11050070
Received: 30 March 2018 / Revised: 3 May 2018 / Accepted: 9 May 2018 / Published: 11 May 2018
PDF Full-text (14836 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO2) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO2 and HHb is not regularly reported, or
[...] Read more.
Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO2) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO2 and HHb is not regularly reported, or worse, assumed to be similar. Here we describe a methodology that allows the estimation of the symmetry between the functional connectivity (FC) networks of HbO2 and HHb and propose a differential symmetry index (DSI) indicative of the in-common physiological information. Our hypothesis is that the symmetry between FC networks associated with HbO2 and HHb is above what should be expected from random networks. FC analysis was done in fNIRS data collected from six freely-moving healthy volunteers over 16 locations on the prefrontal cortex during a real-world task in an out-of-the-lab environment. In addition, systemic data including breathing rate (BR) and heart rate (HR) were also synchronously collected and used within the FC analysis. FC networks for HbO2 and HHb were established independently using a Bayesian networks analysis. The DSI between both haemoglobin (Hb) networks with and without systemic influence was calculated. The relationship between the symmetry of HbO2 and HHb networks, including the segregational and integrational characteristics of the networks (modularity and global efficiency respectively) were further described. Consideration of systemic information increases the path lengths of the connectivity networks by 3%. Sparse networks exhibited higher asymmetry than dense networks. Importantly, our experimental connectivity networks symmetry between HbO2 and HHb departs from random (t-test: t(509) = 26.39, p < 0.0001). The DSI distribution suggests a threshold of 0.2 to decide whether both HbO2 and HHb FC networks ought to be studied. For sparse FC networks, analysis of both haemoglobin species is strongly recommended. Our DSI can provide a quantifiable guideline for deciding whether to proceed with single or both Hb networks in FC analysis. Full article
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Open AccessArticle Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
Algorithms 2018, 11(5), 67; https://doi.org/10.3390/a11050067
Received: 6 April 2018 / Revised: 1 May 2018 / Accepted: 2 May 2018 / Published: 8 May 2018
Cited by 3 | PDF Full-text (24989 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to
[...] Read more.
With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site. Full article
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