Twenty years of functional near-infrared spectroscopy: introduction for the special issue

Mechanically switchable solid inhomogeneous phantom for performance tests in diffuse imaging and spectroscopy

A mechanically switchable solid inhomogeneous phantom simulating localized absorption changes was developed and characterized. The homogeneous host phantom was made of epoxy resin with black toner and titanium dioxide particles added as absorbing and scattering components, respectively. A cylindrical rod, movable along a hole in the block and made of the same material, has a black polyvinyl chloride cylinder embedded in its center. By varying the volume and position of the black inclusion, absorption perturbations can be generated over a large range of magnitudes. The phantom has been characterized by various time-domain diffuse optics instruments in terms of absorption and scattering spectra, transmittance images, and reflectance contrast. Addressing a major application of the phantom for performance characterization for functional near-infrared spectroscopy of the brain, the contrast was measured in reflectance mode while black cylinders of volumes from ≈20  mm3 to ≈270  mm3 were moved in lateral and depth directions, respectively. The new type of solid inhomogeneous phantom is expected to become a useful tool for routine quality check of clinical instruments or implementation of industrial standards provided an experimental characterization of the phantom is performed in advance.

Lawrence K. Assessment of the best flow model to characterize diffuse correlation spectroscopy data acquired directly on the brain

Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique capable of monitoring tissue perfusion. The normalized temporal intensity autocorrelation function generated by DCS is typically characterized by assuming that the movement of erythrocytes can be modeled as a Brownian diffusion-like process instead of by the expected random flow model. Recently, a hybrid model, referred to as the hydrodynamic diffusion model, was proposed, which combines the random and Brownian flow models. The purpose of this study was to investigate the best model to describe autocorrelation functions acquired directly on the brain in order to avoid confounding effects of extracerebral tissues. Data were acquired from 11 pigs during normocapnia and hypocapnia, and flow changes were verified by computed tomography perfusion (CTP). The hydrodynamic diffusion model was found to provide the best fit to the autocorrelation functions; however, no significant difference for relative flow changes measured by the Brownian and hydrodynamic diffusion models was observed.

Optimal estimation reconstruction of the optical properties of a two-layered tissue phantom from time-resolved single-distance measurements

In this work, we have tested the optimal estimation (OE) algorithm for the reconstruction of the optical properties of a two-layered liquid tissue phantom from time-resolved single-distance measurements. The OE allows a priori information, in particular on the range of variation of fit parameters, to be included. The purpose of the present investigations was to compare the performance of OE with the Levenberg–Marquardt method for a geometry and real experimental conditions typically used to reconstruct the optical properties of biological tissues such as muscle and brain. The absorption coefficient of the layers was varied in a range of values typical for biological tissues. The reconstructions performed demonstrate the substantial improvements achievable with the OE provided a priori information is available. We note the extreme reliability, robustness, and accuracy of the retrieved absorption coefficient of the second layer obtained with the OE that was found for up to six fit parameters, with an error in the retrieved values of less than 10%. A priori information on fit parameters and fixed forward model parameters clearly improves robustness and accuracy of the inversion procedure.

Passive BCI based on drowsiness detection: an fNIRS study

We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI). The passive brain signals for the drowsy state are acquired from the prefrontal and dorsolateral prefrontal cortex. The experiment is performed on 13 healthy subjects using a driving simulator, and their brain activity is recorded using a continuous-wave fNIRS system. Linear discriminant analysis (LDA) is employed for training and testing, using the data from the prefrontal, left- and right-dorsolateral prefrontal regions. For classification, eight features are tested: mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak, in 0~5, 0~10, and 0~15 second time windows, respectively. The results show that the best performance for classification is achieved using mean oxyhemoglobin, the signal peak, and the sum of peaks as features. The average accuracies in the right dorsolateral prefrontal cortex (83.1, 83.4 and 84.9% in the 0~5, 0~10 and 0~15 second time windows, respectively) show that the proposed method has an effective utility for detection of drowsiness for a passive BCI.

Correlates of Near-Infrared Spectroscopy Brain-Computer Interface Accuracy in a Multi-Class Personalization Framework

Brain–computer interfaces (BCIs) provide individuals with a means of interacting with a computer using only neural activity. To date, the majority of near-infrared spectroscopy (NIRS) BCIs have used prescribed tasks to achieve binary control. The goals of this study were to evaluate the possibility of using a personalized approach to establish control of a two-, three-, four-, and five-class NIRS–BCI, and to explore how various user characteristics correlate to accuracy. Ten able-bodied participants were recruited for five data collection sessions. Participants performed six mental tasks and a personalized approach was used to select each individual’s best discriminating subset of tasks. The average offline cross-validation accuracies achieved were 78, 61, 47, and 37% for the two-, three-, four-, and five-class problems, respectively. Most notably, all participants exceeded an accuracy of 70% for the two-class problem, and two participants exceeded an accuracy of 70% for the three-class problem. Additionally, accuracy was found to be strongly positively correlated (Pearson’s) with perceived ease of session (ρ = 0.653), ease of concentration (ρ = 0.634), and enjoyment (ρ = 0.550), but strongly negatively correlated with verbal IQ (ρ = −0.749).

Validating a new methodology for optical probe design and image registration in fNIRS studies

Functional near-infrared spectroscopy (fNIRS) is an imaging technique that relies on the principle of shining near-infrared light through tissue to detect changes in hemodynamic activation. An important methodological issue encountered is the creation of optimized probe geometry for fNIRS recordings. Here, across three experiments, we describe and validate a processing pipeline designed to create an optimized, yet scalable probe geometry based on selected regions of interest (ROIs) from the functional magnetic resonance imaging (fMRI) literature. In experiment 1, we created a probe geometry optimized to record changes in activation from target ROIs important for visual working memory. Positions of the sources and detectors of the probe geometry on an adult head were digitized using a motion sensor and projected onto a generic adult atlas and a segmented head obtained from the subject's MRI scan. In experiment 2, the same probe geometry was scaled down to fit a child's head and later digitized and projected onto the generic adult atlas and a segmented volume obtained from the child's MRI scan. Using visualization tools and by quantifying the amount of intersection between target ROIs and channels, we show that out of 21 ROIs, 17 and 19 ROIs intersected with fNIRS channels from the adult and child probe geometries, respectively. Further, both the adult atlas and adult subject-specific MRI approaches yielded similar results and can be used interchangeably. However, results suggest that segmented heads obtained from MRI scans be used for registering children's data. Finally, in experiment 3, we further validated our processing pipeline by creating a different probe geometry designed to record from target ROIs involved in language and motor processing.

Systems Neuroengineering: Understanding and Interacting with the Brain

In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering-to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.

Optical imaging of the prefrontal activity in joint attention experience

Functional near-infrared spectroscopy (fNIRS) was used to measure the prefrontal activity in joint attention experience. 16 healthy adults participated in the experiment in which 42 optical channels were fixed over the anterior prefrontal cortex (aPFC), dorsolateral prefrontal cortex (DLPFC), inferior frontal gyrus (IFG) and a small anterior portion of the superior temporal gyrus (STG). Video stimuli were used to engender joint or non-joint attention experience in observers. Cortical hemodynamic response and functional connectivity were measured and averaged across all subjects for each stimulus condition. Our data showed the activation in joint attention located in the aPFC and DLPFC bilaterally, but dominantly in the left hemisphere. This observation, together with the previous findings on infants and children, provides a clear developmental scenario on the prefrontal activation associated with joint attention process. In the case of non-joint attention condition, only a small region of the right DLPFC was activated. Functional connectivity was observed to be enhanced, but differently in joint and non-joint attention condition.

fMRI Validation of fNIRS Measurements During a Naturalistic Task

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson’s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning.

Neuronal or hemodynamic? Grappling with the functional MRI signal

Magnetic resonance imaging (MRI) and functional MRI (fMRI) continue to advance because creative physicists, engineers, neuroscientists, clinicians, and physiologists find new ways for extracting more information from the signal. Innovations in pulse sequence design, paradigm design, and processing methods have advanced the field and firmly established fMRI as a cornerstone for understanding the human brain. In this article, the field of fMRI is described through consideration of the central problem of separating hemodynamic from neuronal information. Discussed here are examples of how pulse sequences, activation paradigms, and processing methods are integrated such that novel, high-quality information can be obtained. Examples include the extraction of information such as activation onset latency, metabolic rate, neuronal adaptation, vascular patency, vessel diameter, vigilance, and subvoxel activation. Experimental measures include time series latency, hemodynamic shape, MR phase, multivoxel patterns, ratios of activation-related R2* to R2, metabolic rate changes, fluctuation correlations and frequencies, changes in fluctuation correlations and frequencies over time, resting correlation states, echo time dependence, and more.

Effect of motion artifacts and their correction on near-infrared spectroscopy oscillation data: a study in healthy subjects and stroke patients

Functional near-infrared spectroscopy is prone to contamination by motion artifacts (MAs). Motion correction algorithms have previously been proposed and their respective performance compared for evoked rain activation studies. We study instead the effect of MAs on "oscillation" data which is at the basis of functional connectivity and autoregulation studies. We use as our metric of interest the interhemispheric correlation (IHC), the correlation coefficient between symmetrical time series of oxyhemoglobin oscillations. We show that increased motion content results in a decreased IHC. Using a set of motion-free data on which we add real MAs, we find that the best motion correction approach consists of discarding the segments of MAs following a careful approach to minimize the contamination due to band-pass filtering of data from "bad" segments spreading into adjacent "good" segments. Finally, we compare the IHC in a stroke group and in a healthy group that we artificially contaminated with the MA content of the stroke group, in order to avoid the confounding effect of increased motion incidence in the stroke patients. After motion correction, the IHC remains lower in the stroke group in the frequency band around 0.1 and 0.04 Hz, suggesting a physiological origin for the difference. We emphasize the importance of considering MAs as a confounding factor in oscillation-based functional near-infrared spectroscopy studies.

Unemotional on all counts: Evidence of reduced affective responses in individuals with high callous-unemotional traits across emotion systems and valences

The current study aimed to identify atypical neurophysiological activity associated with deficient affective processing in individuals with high callous-unemotional traits (CU). Fifty-six participants (M age = 20.52; 46% male) divided in two groups, differentiated on levels of CU traits, were invited to participate in the experimental phase of the study. Medial prefrontal cortex activity, measured with functional Near-Infrared Spectroscopy, and facial electro-myography activity were recorded during videos depicting violent, comedy and neutral scenes. Individuals high on CU traits showed similar medial prefrontal cortex oxygenated hemoglobin (HbO(2)) activity to positive and negative films, while the pre-frontal cortical responses of low CU individuals were more pronounced to positive than negative materials. High CU participants also showed reduced facial electromyography at the corrugator muscle in response to violent films, which was not differentiated from their responses to comedy films. These findings suggest that individuals high on CU traits show reduced but not absent (i.e., flat) affect to emotional material. Deficits in processing positive and negative valent material, measured with different neuro-physiological modalities, might be essential to understand CU traits.

Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial

Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that is used to noninvasively measure cerebral hemoglobin concentration changes induced by brain activation. Using structural guidance in fNIRS research enhances interpretation of results and facilitates making comparisons between studies. AtlasViewer is an open-source software package we have developed that incorporates multiple spatial registration tools to enable structural guidance in the interpretation of fNIRS studies. We introduce the reader to the layout of the AtlasViewer graphical user interface, the folder structure, and user files required in the creation of fNIRS probes containing sources and detectors registered to desired locations on the head, evaluating probe fabrication error and intersubject probe placement variability, and different procedures for estimating measurement sensitivity to different brain regions as well as image reconstruction performance. Further, we detail how AtlasViewer provides a generic head atlas for guiding interpretation of fNIRS results, but also permits users to provide subject-specific head anatomies to interpret their results. We anticipate that AtlasViewer will be a valuable tool in improving the anatomical interpretation of fNIRS studies.

A synchrony-dependent influence of sounds on activity in visual cortex measured using functional near-infrared spectroscopy (fNIRS)

Evidence from human neuroimaging and animal electrophysiological studies suggests that signals from different sensory modalities interact early in cortical processing, including in primary sensory cortices. The present study aimed to test whether functional near-infrared spectroscopy (fNIRS), an emerging, non-invasive neuroimaging technique, is capable of measuring such multisensory interactions. Specifically, we tested for a modulatory influence of sounds on activity in visual cortex, while varying the temporal synchrony between trains of transient auditory and visual events. Related fMRI studies have consistently reported enhanced activation in response to synchronous compared to asynchronous audiovisual stimulation. Unexpectedly, we found that synchronous sounds significantly reduced the fNIRS response from visual cortex, compared both to asynchronous sounds and to a visual-only baseline. It is possible that this suppressive effect of synchronous sounds reflects the use of an efficacious visual stimulus, chosen for consistency with previous fNIRS studies. Discrepant results may also be explained by differences between studies in how attention was deployed to the auditory and visual modalities. The presence and relative timing of sounds did not significantly affect performance in a simultaneously conducted behavioral task, although the data were suggestive of a positive relationship between the strength of the fNIRS response from visual cortex and the accuracy of visual target detection. Overall, the present findings indicate that fNIRS is capable of measuring multisensory cortical interactions. In multisensory research, fNIRS can offer complementary information to the more established neuroimaging modalities, and may prove advantageous for testing in naturalistic environments and with infant and clinical populations.

Specificity of hemodynamic brain responses to painful stimuli: a functional near-infrared spectroscopy study

Assessing pain in individuals not able to communicate (e.g. infants, under surgery, or following stroke) is difficult due to the lack of non-verbal objective measures of pain. Near-infrared spectroscopy (NIRS) being a portable, non-invasive and inexpensive method of monitoring cerebral hemodynamic activity has the potential to provide such a measure. Here we used functional NIRS to evaluate brain activation to an innocuous and a noxious electrical stimulus on healthy human subjects (n = 11). For both innocuous and noxious stimuli, we observed a signal change in the primary somatosensory cortex contralateral to the stimulus. The painful and non-painful stimuli can be differentiated based on their signal size and profile. We also observed that repetitive noxious stimuli resulted in adaptation of the signal. Furthermore, the signal was distinguishable from a skin sympathetic response to pain that tended to mask it. Our results support the notion that functional NIRS has a potential utility as an objective measure of pain.

A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data

Movements are a major source of artifacts in functional Near-Infrared Spectroscopy (fNIRS). Several algorithms have been developed for motion artifact correction of fNIRS data, including Principal Component Analysis (PCA), targeted Principal Component Analysis (tPCA), Spline Interpolation (SI), and Wavelet Filtering (WF). WF is based on removing wavelets with coefficients deemed to be outliers based on their standardized scores, and it has proven to be effective on both synthetized and real data. However, when the SNR is high, it can lead to a reduction of signal amplitude. This may occur because standardized scores inherently adapt to the noise level, independently of the shape of the distribution of the wavelet coefficients. Higher-order moments of the wavelet coefficient distribution may provide a more diagnostic index of wavelet distribution abnormality than its variance. Here we introduce a new procedure that relies on eliminating wavelets that contribute to generate a large fourth-moment (i.e., kurtosis) of the coefficient distribution to define "outliers" wavelets (kurtosis-based Wavelet Filtering, kbWF). We tested kbWF by comparing it with other existing procedures, using simulated functional hemodynamic responses added to real resting-state fNIRS recordings. These simulations show that kbWF is highly effective in eliminating transient noise, yielding results with higher SNR than other existing methods over a wide range of signal and noise amplitudes. This is because: (1) the procedure is iterative; and (2) kurtosis is more diagnostic than variance in identifying outliers. However, kbWF does not eliminate slow components of artifacts whose duration is comparable to the total recording time.

fNIRS in the developmental sciences

With the introduction of functional near-infrared spectroscopy (fNIRS) into the experimental setting, developmental scientists have, for the first time, the capacity to investigate the functional activation of the infant brain in awake, engaged participants. The advantages of fNIRS clearly outweigh the limitations, and a description of how this technology is implemented in infant populations is provided. Most fNIRS research falls into one of three content domains: object processing, processing of biologically and socially relevant information, and language development. Within these domains, there are ongoing debates about the origins and development of human knowledge, making early neuroimaging particularly advantageous. The use of fNIRS has allowed investigators to begin to identify the localization of early object, social, and linguistic knowledge in the immature brain and the ways in which this changes with time and experience. In addition, there is a small but growing body of research that provides insight into the neural mechanisms that support and facilitate learning during the first year of life. At the same time, as with any emerging field, there are limitations to the conclusions that can be drawn on the basis of current findings. We offer suggestions as to how to optimize the use of this technology to answer questions of theoretical and practical importance to developmental scientists.

Comparison of procedures for co-registering scalp-recording locations to anatomical magnetic resonance images

Functional brain imaging techniques require accurate co-registration to anatomical images to precisely identify the areas being activated. Many of them, including diffuse optical imaging, rely on scalp-placed recording sensors. Fiducial alignment is an effective and rapid method for co-registering scalp sensors onto anatomy, but is quite sensitive to placement errors. Surface Euclidean distance minimization using the Levenberq-Marquart algorithm (LMA) has been shown to be very accurate when based on good initial guesses, such as precise fiducial alignment, but its accuracy drops substantially with fiducial placement errors. Here we compared fiducial and LMA co-registration methods to a new procedure, the iterative closest point-to-plane (ICP2P) method, using simulated and real data. An advantage of ICP2P is that it eliminates the need to identify fiducials and is, therefore, entirely automatic. We show that, typically, ICP2P is as accurate as fiducial-based LMA, but is less sensitive to initial placement errors. However, ICP2P is more sensitive to spatially correlated noise in the description of the head surface. Hence, the best technique for co-registration depends on the type of data available to describe the scalp and the surface defined by the recording sensors. Under optimal conditions, co-registration error using surface-fitting procedures can be reduced to ~ 3 mm.

Concurrent fNIRS-fMRI measurement to validate a method for separating deep and shallow fNIRS signals by using multidistance optodes

It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level-dependent signals was analyzed. A three-way analysis of variance ([Formula: see text]) indicated that the main effect of fNIRS signal depth on the correlation is significant [[Formula: see text], [Formula: see text]]. This result indicates that the MD-ICA method successfully separates fNIRS signals into spatially deep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method.

Hemodynamic correlates of cognition in human infants

Over the past 20 years, the field of cognitive neuroscience has relied heavily on hemodynamic measures of blood oxygenation in local regions of the brain to make inferences about underlying cognitive processes. These same functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) techniques have recently been adapted for use with human infants. We review the advantages and disadvantages of these two neuroimaging methods for studies of infant cognition, with a particular emphasis on their technical limitations and the linking hypotheses that are used to draw conclusions from correlational data. In addition to summarizing key findings in several domains of infant cognition, we highlight the prospects of improving the quality of fNIRS data from infants to address in a more sophisticated way how cognitive development is mediated by changes in underlying neural mechanisms.

Performance assessment of time-domain optical brain imagers, part 2: nEUROPt protocol

The nEUROPt protocol is one of two new protocols developed within the European project nEUROPt to characterize the performances of time-domain systems for optical imaging of the brain. It was applied in joint measurement campaigns to compare the various instruments and to assess the impact of technical improvements. This protocol addresses the characteristic of optical brain imaging to detect, localize, and quantify absorption changes in the brain. It was implemented with two types of inhomogeneous liquid phantoms based on Intralipid and India ink with well-defined optical properties. First, small black inclusions were used to mimic localized changes of the absorption coefficient. The position of the inclusions was varied in depth and lateral direction to investigate contrast and spatial resolution. Second, two-layered liquid phantoms with variable absorption coefficients were employed to study the quantification of layer-wide changes and, in particular, to determine depth selectivity, i.e., the ratio of sensitivities for deep and superficial absorption changes. We introduce the tests of the nEUROPt protocol and present examples of results obtained with different instruments and methods of data analysis. This protocol could be a useful step toward performance tests for future standards in diffuse optical imaging.

A micro-Raman study of live, single red blood cells (RBCs) treated with AgNO3 nanoparticles

Silver nanoparticles (Ag NPs) are known to exhibit broad antimicrobial activity. However, such activity continues to raise concerns in the context of the interaction of such NPs with biomolecules. In a physiological environment NPs interact with individual biological cells either by penetrating through the cell membrane or by adhering to the membrane. We have explored the interaction of Ag NPs with single optically-trapped, live erythrocytes (red blood cells, RBCs) using Raman Tweezers spectroscopy. Our experiments reveal that Ag NPs induce modifications within an RBC that appear to be irreversible. In particular we are able to identify that the heme conformation in an RBC transforms from the usual R-state (oxy-state) to the T-state (deoxy-state). We rationalize our observations by proposing a model for the nanoparticle cytotoxicity pathway when the NP size is larger than the membrane pore size. We propose that the interaction of Ag NPs with the cell surface induces damage brought about by alteration of intracellular pH caused by the blockage of the cell membrane transport.

Mapping cortical haemodynamics during neonatal seizures using diffuse optical tomography: a case study

Seizures in the newborn brain represent a major challenge to neonatal medicine. Neonatal seizures are poorly classified, under-diagnosed, difficult to treat and are associated with poor neurodevelopmental outcome. Video-EEG is the current gold-standard approach for seizure detection and monitoring. Interpreting neonatal EEG requires expertise and the impact of seizures on the developing brain remains poorly understood. In this case study we present the first ever images of the haemodynamic impact of seizures on the human infant brain, obtained using simultaneous diffuse optical tomography (DOT) and video-EEG with whole-scalp coverage. Seven discrete periods of ictal electrographic activity were observed during a 60 minute recording of an infant with hypoxic-ischaemic encephalopathy. The resulting DOT images show a remarkably consistent, high-amplitude, biphasic pattern of changes in cortical blood volume and oxygenation in response to each electrographic event. While there is spatial variation across the cortex, the dominant haemodynamic response to seizure activity consists of an initial increase in cortical blood volume prior to a large and extended decrease typically lasting several minutes. This case study demonstrates the wealth of physiologically and clinically relevant information that DOT-EEG techniques can yield. The consistency and scale of the haemodynamic responses observed here also suggest that DOT-EEG has the potential to provide improved detection of neonatal seizures.

Sensitivity of near-infrared spectroscopy and diffuse correlation spectroscopy to brain hemodynamics: simulations and experimental findings during hypercapnia

Near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) are two diffuse optical technologies for brain imaging that are sensitive to changes in hemoglobin concentrations and blood flow, respectively. Measurements for both modalities are acquired on the scalp, and therefore hemodynamic processes in the extracerebral vasculature confound the interpretation of cortical hemodynamic signals. The sensitivity of NIRS to the brain versus the extracerebral tissue and the contrast-to-noise ratio (CNR) of NIRS to cerebral hemodynamic responses have been well characterized, but the same has not been evaluated for DCS. This is important to assess in order to understand their relative capabilities in measuring cerebral physiological changes. We present Monte Carlo simulations on a head model that demonstrate that the relative brain-to-scalp sensitivity is about three times higher for DCS (0.3 at 3 cm) than for NIRS (0.1 at 3 cm). However, because DCS has higher levels of noise due to photon-counting detection, the CNR is similar for both modalities in response to a physiologically realistic simulation of brain activation. Even so, we also observed higher CNR of the hemodynamic response during graded hypercapnia in adult subjects with DCS than with NIRS.

Mapping distributed brain function and networks with diffuse optical tomography

Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson's disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging.

Reduced interhemispheric functional connectivity of children with autism spectrum disorder: evidence from functional near infrared spectroscopy studies

Autism spectrum disorder (ASD) is a neuro-developmental disorder, which has been associated with atypical neural synchronization. In this study, functional near infrared spectroscopy (fNIRS) was used to study the differences in functional connectivity in bilateral inferior frontal cortices (IFC) and bilateral temporal cortices (TC) between ASD and typically developing (TD) children between 8 and 11 years of age. As the first report of fNIRS study on the resting state functional connectivity (RSFC) in children with ASD, ten children with ASD and ten TD children were recruited in this study for 8 minute resting state measurement. Compared to TD children, children with ASD showed reduced interhemispheric connectivity in TC. Children with ASD also showed significantly lower local connectivity in bilateral temporal cortices. In contrast to TD children, children with ASD did not show typical patterns of symmetry in functional connectivity in temporal cortex. These results support the feasibility of using the fNIRS method to assess atypical functional connectivity of cortical responses of ASD and its potential application in diagnosis.