Comparison of group-level, source localized activity for simultaneous functional near-infrared spectroscopy-magnetoencephalography and simultaneous fNIRS-fMRI during parametric median nerve stimulation

Huppert, Theodore J.; Barker, Jeff W.; Schmidt, Benjamin; Walls, Shawn A.; Ghuman, Avniel Singh

doi:10.1117/1.nph.4.1.015001

Cited by 41 publications

(52 citation statements)

References 47 publications

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“…Even for basic motor-sensory tasks, one of the challenges in comparing fMRI and fNIRS measurements lies in the inherent difficulty of reconstructing images of brain activation as detected by fNIRS (Huppert et al, 2017 ). As such, many investigations have been limited to patterns of temporal correlation.…”

Section: An Overview Of Combined Fmri-fnirs Studiesmentioning

confidence: 99%

Functional Magnetic Resonance Imaging and Functional Near-Infrared Spectroscopy: Insights from Combined Recording Studies

Scarapicchia

Brown

Mayo

et al. 2017

Front. Hum. Neurosci.

182

134

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Although blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a widely available, non-invasive technique that offers excellent spatial resolution, it remains limited by practical constraints imposed by the scanner environment. More recently, functional near infrared spectroscopy (fNIRS) has emerged as an alternative hemodynamic-based approach that possesses a number of strengths where fMRI is limited, most notably in portability and higher tolerance for motion. To date, fNIRS has shown promise in its ability to shed light on the functioning of the human brain in populations and contexts previously inaccessible to fMRI. Notable contributions include infant neuroimaging studies and studies examining full-body behaviors, such as exercise. However, much like fMRI, fNIRS has technical constraints that have limited its application to clinical settings, including a lower spatial resolution and limited depth of recording. Thus, by combining fMRI and fNIRS in such a way that the two methods complement each other, a multimodal imaging approach may allow for more complex research paradigms than is feasible with either technique alone. In light of these issues, the purpose of the current review is to: (1) provide an overview of fMRI and fNIRS and their associated strengths and limitations; (2) review existing combined fMRI-fNIRS recording studies; and (3) discuss how their combined use in future research practices may aid in advancing modern investigations of human brain function.

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Section: An Overview Of Combined Fmri-fnirs Studiesmentioning

confidence: 99%

Functional Magnetic Resonance Imaging and Functional Near-Infrared Spectroscopy: Insights from Combined Recording Studies

Scarapicchia

Brown

Mayo

et al. 2017

Front. Hum. Neurosci.

182

134

View full text Add to dashboard Cite

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“…In addition, a disadvantage of fNIRS is the low spatial resolution (5-10 mm) of the activation mapping of the cortical surface compared with those obtained from fMRI and PET. Research combining the use of fNIRS, fMRI, and MEG for source localization is currently ongoing [17]. These combination studies have advantages in temporal and spatial mapping of brain function.…”

Section: Introductionmentioning

confidence: 99%

Vector-Based Approach for the Detection of Initial Dips Using Functional Near-Infrared Spectroscopy

Kato¹

2019

Neuroimaging - Structure, Function and Mind

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Functional near-infrared spectroscopy (fNIRS) is a non-invasive method for the detection of local brain activity using changes in the local levels of oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb). Simultaneous measurement of the levels of oxyHb and deoxyHb is an advantage of fNIRS over other modalities. This review provides a historical description of the physiological problems involved in the accurate identification of local brain activity using fNIRS. The need for improved spatial and temporal identification of local brain activity is described in terms of the physiological challenges of task selection and placement of probes. In addition, this review discusses challenges with data analysis based on a single index, advantages of the simultaneous analysis of multiple indicators, and recently established composite indicators. The vector-based approach provides quantitative imaging of the phase and intensity contrast for oxygen exchange responses in a time series and may detect initial dips related to neuronal activity in the skull. The vector plane model consists of orthogonal vectors of oxyHb and deoxyHb. Initial dips are hemodynamic reactions of oxyHb and deoxyHb induced by increased oxygen consumption in the early tasks of approximately 2-3 seconds. The new analytical concept of fNIRS, able to effectively detect initial dips, may extend further the clinical and social applications of fNIRS.

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“…It is not uncommon to observe a several-fold difference in the signal-to-noise ratio in measurements between areas with little hair (e.g., the forehead) and those with hair or thicker bone structure (e.g., the occipital). The use of statistical models whose assumptions do not match these properties often results in unacceptable false-discovery and uncontrolled type-I errors.As our group has reviewed in several recent publications [9,10,[15][16][17][18], these noise features and unique statistical properties of fNIRS data need to be properly considered and will be briefly summarized in this publication in the context of a new fNIRS analysis toolbox.The primary rationale for the development of the AnalyzIR (pronounced "an-a-lyze-er") toolbox was to create a statistical analysis package to specifically address the properties of fNIRS data. This toolbox was designed to capture and preserve as much of this fNIRS-specific information and noise as possible through the entire analysis pipeline such that first-and higher-level statistical analysis methods could use this information in statistical models by utilizing covariance whitening, accounting for dependent noise terms, and using robust statistical methods.…”

mentioning

confidence: 99%

“…As our group has reviewed in several recent publications [9,10,[15][16][17][18], these noise features and unique statistical properties of fNIRS data need to be properly considered and will be briefly summarized in this publication in the context of a new fNIRS analysis toolbox.…”

mentioning

confidence: 99%

The NIRS Brain AnalyzIR Toolbox

et al. 2018

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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.Algorithms 2018, 11, 73 2 of 33 measurements are recorded from the scalp's surface, fNIRS is also more sensitive to contamination from superficial physiology in the skin, which poses unique challenges for data analysis [5,6].Over the last three decades, fNIRS has been used in a wide range of studies including pediatric populations (e.g., [7]), clinical studies (e.g., [8]), multimodal validations (e.g., [9]), and cognitive testing (e.g., [10]). However, as the fNIRS field has evolved and this technology has found more acceptance, the complexity of the scientific questions being asked of fNIRS data has dramatically increased. Group-level comparisons, longitudinal analysis, or complex comparisons between different task events are now status quo. In addition, fNIRS studies have been expanded to child and infant populations (reviewed in [7]), to allow a range of motion (including fNIRS studies of brain activity during gait or balance [11]), and more "real-world" experience (reviewed in [12]). However, these studies create challenges to analysis such as the need to deal with complex sources of motion and/or physiological noise artifacts. To date, the majority of fNIRS studies using tools and methods have either borrowed from other fields (primarily functional MRI) or have used modality-agnostic methods such as ordinary least-squares regression or methods coded in general programs such as statistical package for the social sciences (SPSS) [13] or statistical analysis system (SAS) [14]. However, in ge...

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Comparison of group-level, source localized activity for simultaneous functional near-infrared spectroscopy-magnetoencephalography and simultaneous fNIRS-fMRI during parametric median nerve stimulation

Cited by 41 publications

References 47 publications

Functional Magnetic Resonance Imaging and Functional Near-Infrared Spectroscopy: Insights from Combined Recording Studies

Functional Magnetic Resonance Imaging and Functional Near-Infrared Spectroscopy: Insights from Combined Recording Studies

Vector-Based Approach for the Detection of Initial Dips Using Functional Near-Infrared Spectroscopy

The NIRS Brain AnalyzIR Toolbox

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