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.
Studying the temporal and spectral characteristics of brain function under spontaneous activity has been recently receiving great interest in the field of neuroscience. By combining wavelet coherence and multivariate permutation test, this paper presents a new method for investigating changes in functional connectivity under spontaneous activity. The proposed method does not impose any prior assumption about the frequency bands that are involved in the activity, nor on the distribution of the data. The proposed method is applied on data obtained from widefield transcranial calcium imaging of GCaMP6 transgenic mice. Results on how function connectivity corresponding to two forms of spontaneous activity differ across frequency and space are presented.
We investigate the problem related to the averaging procedure in functional near-infrared spectroscopy (fNIRS) brain imaging studies. Typically, to reduce noise and to empower the signal strength associated with task-induced activities, recorded signals (e.g., in response to repeated stimuli or from a group of individuals) are averaged through a point-by-point conventional averaging technique. However, due to the existence of variable latencies in recorded activities, the use of the conventional averaging technique can lead to inaccuracies and loss of information in the averaged signal, which may result in inaccurate conclusions about the functionality of the brain. To improve the averaging accuracy in the presence of variable latencies, we present an averaging framework that employs dynamic time warping (DTW) to account for the temporal variation in the alignment of fNIRS signals to be averaged. As a proof of concept, we focus on the problem of localizing task-induced active brain regions. The framework is extensively tested on experimental data (obtained from both block design and event-related design experiments) as well as on simulated data. In all cases, it is shown that the DTW-based averaging technique outperforms the conventional-based averaging technique in estimating the location of task-induced active regions in the brain, suggesting that such advanced averaging methods should be employed in fNIRS brain imaging studies.
Modeling behavior of broadband (30 to 1000 MHz) frequency modulated near-infrared (NIR) photons through a phantom is the basis for accurate extraction of optical absorption and scattering parameters of biological turbid media. Photon dynamics in a phantom are predicted using both analytical and numerical simulation and are related to the measured insertion loss (IL) and insertion phase (IP) for a given geometry based on phantom optical parameters. Accuracy of the extracted optical parameters using finite element method (FEM) simulation is compared to baseline analytical calculations from the diffusion equation (DE) for homogenous brain phantoms. NIR spectroscopy is performed using custom-designed, broadband, free-space optical transmitter (Tx) and receiver (Rx) modules that are developed for photon migration at wavelengths of 680, 780, and 820 nm. Differential detection between two optical Rx locations separated by 0.3 cm is employed to eliminate systemic artifacts associated with interfaces of the optical Tx and Rx with the phantoms. Optical parameter extraction is achieved for four solid phantom samples using the least-square-error method in MATLAB (for DE) and COMSOL (for FEM) simulation by fitting data to measured results over broadband and narrowband frequency modulation. Confidence in numerical modeling of the photonic behavior using FEM has been established here by comparing the transmission mode’s experimental results with the predictions made by DE and FEM for known commercial solid brain phantoms.
Fiber based functional near infra-red (fNIR) spectroscopy has been considered as a cost effective imaging modality. To
achieve a better spatial resolution and greater accuracy in extraction of the optical parameters (i.e., μa and μ's), broadband
frequency modulated systems covering multi-octave frequencies of 10-1000MHz is considered. A helmet mounted
broadband free space fNIR system is considered as significant improvement over bulky commercial fiber fNIR
realizations that are inherently uncomfortable and dispersive for broadband operation. Accurate measurements of
amplitude and phase of the frequency modulated NIR signals (670nm, 795nm, and 850nm) is reported here using free
space optical transmitters and receivers realized in a small size and low cost modules. The tri-wavelength optical
transmitter is based on vertical cavity semiconductor lasers (VCSEL), whereas the sensitive optical receiver is based on
either PIN or APD photodiodes combined with transimpedance amplifiers. This paper also has considered brain
phantoms to perform optical parameter extraction experiments using broadband modulated light for separations of up to
5cm. Analytical models for predicting forward (transmittance) and backward (reflectance) scattering of modulated
photons in diffused media has been modeled using Diffusion Equation (DE). The robustness of the DE modeling and
parameter extraction algorithm was studied by experimental verification of multi-layer diffused media phantoms. In
particular, comparison between analytical and experimental models for narrow band and broadband has been performed
to analyze the advantages of our broadband fNIR system.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.