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      On fractality of functional near-infrared spectroscopy signals: analysis and applications

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          Abstract.

          Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions.

          Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties.

          Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches.

          Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case.

          Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states.

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          Most cited references69

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          Approach to an irregular time series on the basis of the fractal theory

          T Higuchi (1988)
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            Long-range temporal correlations and scaling behavior in human brain oscillations.

            Klaus Linkenkaer-Hansen, Vadim V Nikouline, J Matias Palva … (2001)
            The human brain spontaneously generates neural oscillations with a large variability in frequency, amplitude, duration, and recurrence. Little, however, is known about the long-term spatiotemporal structure of the complex patterns of ongoing activity. A central unresolved issue is whether fluctuations in oscillatory activity reflect a memory of the dynamics of the system for more than a few seconds. We investigated the temporal correlations of network oscillations in the normal human brain at time scales ranging from a few seconds to several minutes. Ongoing activity during eyes-open and eyes-closed conditions was recorded with simultaneous magnetoencephalography and electroencephalography. Here we show that amplitude fluctuations of 10 and 20 Hz oscillations are correlated over thousands of oscillation cycles. Our analyses also indicated that these amplitude fluctuations obey power-law scaling behavior. The scaling exponents were highly invariant across subjects. We propose that the large variability, the long-range correlations, and the power-law scaling behavior of spontaneous oscillations find a unifying explanation within the theory of self-organized criticality, which offers a general mechanism for the emergence of correlations and complex dynamics in stochastic multiunit systems. The demonstrated scaling laws pose novel quantitative constraints on computational models of network oscillations. We argue that critical-state dynamics of spontaneous oscillations may lend neural networks capable of quick reorganization during processing demands.
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              fNIRS-based brain-computer interfaces: a review

              Noman Naseer, Keum-Shik Hong (2015)
              A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.
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                Author and article information

                Contributors
                Li Zhu:
                ORCID: https://orcid.org/0000-0003-0767-7471
                Sasan Haghani
                Laleh Najafizadeh:
                ORCID: https://orcid.org/0000-0002-6658-4112
                Journal
                Journal ID (nlm-ta): Neurophotonics
                Journal ID (iso-abbrev): Neurophotonics
                Journal ID (coden): NEUROW
                Journal ID (publisher-id): NPh
                Title: Neurophotonics
                Publisher: Society of Photo-Optical Instrumentation Engineers
                ISSN (Print): 2329-423X
                ISSN (Electronic): 2329-4248
                Publication date (Electronic): 29 April 2020
                Publication date (Print): April 2020
                Publication date PMC-release: 29 April 2020
                Volume: 7
                Issue: 2
                Electronic Location Identifier: 025001
                Affiliations
                [a ]Rutgers University , Integrated Systems and NeuroImaging Laboratory, Department of Electrical and Computer Engineering, Piscataway, New Jersey, United States
                [b ]University of The District of Columbia , Department of Electrical and Computer Engineering, Washington DC, United States
                Author notes
                [* ]Address all correspondence to Laleh Najafizadeh, E-mail: laleh.najafizadeh@ 123456rutgers.edu
                Author information
                https://orcid.org/0000-0003-0767-7471
                https://orcid.org/0000-0002-6658-4112
                Article
                Publisher-manuscript ID: NPh-19100RR Publisher ID: 19100RR
                DOI: 10.1117/1.NPh.7.2.025001
                PMC ID: 7189210
                SO-VID: e183a3bf-fee3-4c10-aaef-f95ad2f1623b
                Copyright © © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
                History
                Date received : 23 October 2019
                Date accepted : 13 April 2020
                Page count
                Figures: 6, Tables: 4, References: 79, Pages: 15
                Funding
                Funded by: Siemens Healthineers
                Funded by: National Science Foundation
                Award ID: 1533479
                Categories
                Subject: Research Papers
                Subject: Paper
                Custom metadata
                running-head Zhu et al.: On fractality of functional near-infrared spectroscopy signals…

                Keywords: functional near-infrared spectroscopy,optical brain imaging,visibility graph,fractal dimension,resting state,classification,brain computer interfaces
                Data availability:
                Keywords: functional near-infrared spectroscopy, optical brain imaging, visibility graph, fractal dimension, resting state, classification, brain computer interfaces

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