PHOEBE: a method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy

Links between socioeconomic disadvantage, neural function, and working memory in early childhood

Children reared in socioeconomically disadvantaged environments are at risk for academic, cognitive, and behavioral problems. Mounting evidence suggests that childhood adversities, encountered at disproportionate rates in contexts of socioeconomic risk, shape the developing brain in ways that explain disparities. Circuitries that subserve neurocognitive functions related to memory, attention, and cognitive control are especially affected. However, most work showing altered neural function has focused on middle childhood and adolescence. Understanding alterations in brain development during foundational points in early childhood is a key next step. To address this gap, we examined functional near-infrared-spectroscopy-based neural activation during a working memory (WM) task in young children aged 4-7 years (N = 30) who varied in socioeconomic risk exposure. Children who experienced greater disadvantage (lower income to needs ratio and lower Hollingshead index) exhibited lower activation in the lateral prefrontal cortex than children who experienced less to no disadvantage. Variability in prefrontal cortex activation, but not behavioral performance on the WM task, was associated with worse executive functioning in children as reported by parents. These findings add to existing evidence that exposure to early adversity, such as socioeconomic risk, may lead to foundational changes in the developing brain, which increases risk for disparities in functioning across multiple cognitive and social domains.

Functional Connectivity Within the Fronto-Parietal Network Predicts Complex Task Performance: A fNIRS Study

Performance in complex tasks is essential for many high risk operators. The achievement of such tasks is supported by high-level cognitive functions arguably involving functional activity and connectivity in a large ensemble of brain areas that form the fronto-parietal network. Here we aimed at determining whether the functional connectivity at rest within this network could predict performance in a complex task: the Space Fortress video game. Functional Near Infrared Spectroscopy (fNIRS) data from 32 participants were recorded during a Resting-State period, the completion of a simple version of Space Fortress (monotask) and the original version (multitask). The intrinsic functional connectivity within the fronto-parietal network (i.e., during the Resting-State) was a significant predictor of performance at Space Fortress multitask but not at its monotask version. The same pattern was observed for the functional connectivity during the task. Our overall results suggest that Resting-State functional connectivity within the fronto-parietal network could be used as an intrinsic brain marker for performance prediction of a complex task achievement, but not for simple task performance.

Neuroprotection of the Perinatal Brain by Early Information of Cerebral Oxygenation and Perfusion Patterns

Abnormal patterns of cerebral perfusion/oxygenation are associated with neuronal damage. In preterm neonates, hypoxemia, hypo-/hypercapnia and lack of cerebral autoregulation are related to peri-intraventricular hemorrhages and white matter injury. Reperfusion damage after perinatal hypoxic ischemia in term neonates seems related with cerebral hyperoxygenation. Since biological tissue is transparent for near infrared (NIR) light, NIR-spectroscopy (NIRS) is a noninvasive bedside tool to monitor brain oxygenation and perfusion. This review focuses on early assessment and guiding abnormal cerebral oxygenation/perfusion patterns to possibly reduce brain injury. In term infants, early patterns of brain oxygenation helps to decide whether or not therapy (hypothermia) and add-on therapies should be considered. Further NIRS-related technical advances such as the use of (functional) NIRS allowing simultaneous estimation and integrating of heart rate, respiration rate and monitoring cerebral autoregulation will be discussed.

Longitudinal infant fNIRS channel-space analyses are robust to variability parameters at the group-level: An image reconstruction investigation

•

First investigation of validity of longitudinal infant channel-space fNIRS analysis.

•

Novel image reconstruction analysis conducted.

•

Variability in array position is dominant factor driving different inferences.

•

Channel-space fNIRS analyses robust to implicit assumptions at group-level.

•

Hope to encourage more widespread use of image reconstruction in infant analyses.

The first 1000 days from conception to two-years of age are a critical period in brain development, and there is an increasing drive for developing technologies to help advance our understanding of neurodevelopmental processes during this time. Functional near-infrared spectroscopy (fNIRS) has enabled longitudinal infant brain function to be studied in a multitude of settings. Conventional fNIRS analyses tend to occur in the channel-space, where data from equivalent channels across individuals are combined, which implicitly assumes that head size and source-detector positions (i.e. array position) on the scalp are constant across individuals. The validity of such assumptions in longitudinal infant fNIRS analyses, where head growth is most rapid, has not previously been investigated. We employed an image reconstruction approach to analyse fNIRS data collected from a longitudinal cohort of infants in The Gambia aged 5- to 12-months. This enabled us to investigate the effect of variability in both head size and array position on the anatomical and statistical inferences drawn from the data at both the group- and the individual-level. We also sought to investigate the impact of group size on inferences drawn from the data. We found that variability in array position was the driving factor between differing inferences drawn from the data at both the individual- and group-level, but its effect was weakened as group size increased towards the full cohort size (N = 53 at 5-months, N = 40 at 8-months and N = 45 at 12-months). We conclude that, at the group sizes in our dataset, group-level channel-space analysis of longitudinal infant fNIRS data is robust to assumptions about head size and array position given the variability in these parameters in our dataset. These findings support a more widespread use of image reconstruction techniques in longitudinal infant fNIRS studies.

Wavelength censoring for spectroscopy in optical functional neuroimaging

Optical neuromonitoring provides insight into neurovascular physiology and brain structure and function. These methods rely on spectroscopy to relate light absorption changes to variation of concentrations of physiologic chromophores such as oxy- and deoxyhemoglobin. In clinical or preclinical practice, data quality can vary significantly across wavelengths. In such situations, standard spectroscopic methods may perform poorly, resulting in data loss and limiting field-of-view. To address this issue, and thereby improve the robustness of optical neuromonitoring, we develop, in this manuscript, novel methods to perform spectroscopy even when data quality exhibits wavelength-dependent spatial variation. We sought to understand the impact of spatial, wavelength-based censoring on the physiologic accuracy and utility of hemoglobin spectroscopy. The principles of our analysis are quite general, but to make the methodology tangible we focused on optical intrinsic signal imaging of resting-state functional connectivity in mice. Starting with spectroscopy using four sources, all possible subset spectroscopy matrices were assessed theoretically, using simulated data, and using experimental data. These results were compared against the use of the full spectroscopy matrix to determine which subsets yielded robust results. Our results demonstrated that accurate calculation of changes in hemoglobin concentrations and the resulting functional connectivity network maps was possible even with censoring of some wavelengths. Additionally, we found that the use of changes in total hemoglobin (rather than oxy- or deoxyhemoglobin) yielded results more robust to experimental noise and allowed for the preservation of more data. This new and rigorous image processing method should improve the fidelity of clinical and preclinical functional neuroimaging studies.

Feasibility of combining functional near-infrared spectroscopy with electroencephalography to identify chronic stroke responders to cerebellar transcranial direct current stimulation-a computational modeling and portable neuroimaging methodological study

Feasibility of portable neuroimaging of cerebellar transcranial direct current stimulation (ctDCS) effects on the cerebral cortex has not been investigated vis-à-vis cerebellar lobular electric field strength. We studied functional near-infrared spectroscopy (fNIRS) in conjunction with electroencephalography (EEG) to measure changes in the brain activation at the prefrontal cortex (PFC) and the sensorimotor cortex (SMC) following ctDCS as well as virtual reality-based balance training (VBaT) before and after ctDCS treatment in 12 hemiparetic chronic stroke survivors. We performed general linear modeling (GLM) that putatively associated the lobular electric field strength with the changes in the fNIRS-EEG measures at the ipsilesional and contra-lesional PFC and SMC. Here, fNIRS-EEG measures were found in the latent space from canonical correlation analysis (CCA) between the changes in total hemoglobin (tHb) concentrations (0.01-0.07Hz and 0.07-0.13Hz bands) and log10-transformed EEG bandpower within 1-45 Hz where significant (Wilks' lambda>0.95) canonical correlations were found only for the 0.07-0.13-Hz band. Also, the first principal component (97.5% variance accounted for) of the mean lobular electric field strength was a good predictor of the latent variables of oxy-hemoglobin (O2Hb) concentrations and log10-transformed EEG bandpower. GLM also provided insights into non-responders to ctDCS who also performed poorly in the VBaT due to ideomotor apraxia. Future studies should investigate fNIRS-EEG joint-imaging in a larger cohort to identify non-responders based on GLM fitting to the fNIRS-EEG data.

Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning

Chronic tinnitus is a debilitating condition which affects 10-20% of adults and can severely impact their quality of life. Currently there is no objective measure of tinnitus that can be used clinically. Clinical assessment of the condition uses subjective feedback from individuals which is not always reliable. We investigated the sensitivity of functional near-infrared spectroscopy (fNIRS) to differentiate individuals with and without tinnitus and to identify fNIRS features associated with subjective ratings of tinnitus severity. We recorded fNIRS signals in the resting state and in response to auditory or visual stimuli from 25 individuals with chronic tinnitus and 21 controls matched for age and hearing loss. Severity of tinnitus was rated using the Tinnitus Handicap Inventory and subjective ratings of tinnitus loudness and annoyance were measured on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state measures of connectivity between temporal regions and frontal and occipital regions were significantly higher in patients with tinnitus compared to controls. In the tinnitus group, temporal-occipital connectivity showed a significant increase with subject ratings of loudness. Also in this group, both visual and auditory evoked responses were significantly reduced in the visual and auditory regions of interest respectively. Naïve Bayes classifiers were able to classify patients with tinnitus from controls with an accuracy of 78.3%. An accuracy of 87.32% was achieved using Neural Networks to differentiate patients with slight/ mild versus moderate/ severe tinnitus. Our findings show the feasibility of using fNIRS and machine learning to develop an objective measure of tinnitus. Such a measure would greatly benefit clinicians and patients by providing a tool to objectively assess new treatments and patients' treatment progress.

Signal quality index: an algorithm for quantitative assessment of functional near infrared spectroscopy signal quality

We propose the signal quality index (SQI) algorithm as a novel tool for quantitatively assessing the functional near infrared spectroscopy (fNIRS) signal quality in a numeric scale from 1 (very low quality) to 5 (very high quality). The algorithm comprises two preprocessing steps followed by three consecutive rating stages. The results on a dataset annotated by independent fNIRS experts showed SQI performed significantly better (p<0.05) than PHOEBE (placing headgear optodes efficiently before experimentation) and SCI (scalp coupling index), two existing algorithms, in both quantitatively rating and binary classifying the fNIRS signal quality. Employment of the proposed algorithm to estimate the signal quality before processing the fNIRS signals increases certainty in the interpretations.

Functional Near-Infrared Spectroscopy to Assess Central Pain Responses in a Nonpharmacologic Treatment Trial of Osteoarthritis

BACKGROUND AND PURPOSE

Knee osteoarthritis (OA) is a common source of pain in older adults. Although OA-induced pain can be relieved with analgesics and anti-inflammatory drugs, the current opioid epidemic is fostering the exploration of nonpharmacologic strategies for pain mitigation. Amongs these, transcranial direct current stimulation (tDCS) and mindfulness-based meditation (MBM) hold potential for pain-relief efficacy due to their neuromodulatory effects of the central nervous system, which is known to play a fundamental role in pain perception and processing.

METHODS

In this double-blind study, we used functional near-infrared spectroscopy (fNIRS) to investigate the effects of tDCS combined with MBM on underlying pain processing mechanisms at the central nervous level in older adults with knee OA. Nineteen subjects were randomly assigned to two groups undergoing a 10-day active tDCS and MBM regimen and a sham tDCS and MBM regimen, respectively.

RESULTS

Our results showed that the neuromodulatory intervention significantly relieved pain only in the group receiving active treatment. We also found that only the active treatment group showed a significant increase in oxyhemoglobin activation of the superior motor and somatosensory cortices colocated to the placement of the tDCS anodal electrode. To our knowledge, this is the first study in which the combined effect of tDCS and MBM is investigated using fNIRS.

CONCLUSION

In conclusion, fNIRS can be effectively used to investigate neural mechanisms of pain at the cortical level in association with nonpharmacological, self-administered treatments.

A consensus guide to using functional near-infrared spectroscopy in posture and gait research

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) is increasingly used in the field of posture and gait to investigate patterns of cortical brain activation while people move freely. fNIRS methods, analysis and reporting of data vary greatly across studies which in turn can limit the replication of research, interpretation of findings and comparison across works.

RESEARCH QUESTION AND METHODS

Considering these issues, we propose a set of practical recommendations for the conduct and reporting of fNIRS studies in posture and gait, acknowledging specific challenges related to clinical groups with posture and gait disorders.

RESULTS

Our paper is organized around three main sections: 1) hardware set up and study protocols, 2) artefact removal and data processing and, 3) outcome measures, validity and reliability; it is supplemented with a detailed checklist.

SIGNIFICANCE

This paper was written by a core group of members of the International Society for Posture and Gait Research and posture and gait researchers, all experienced in fNIRS research, with the intent of assisting the research community to lead innovative and impactful fNIRS studies in the field of posture and gait, whilst ensuring standardization of research.

Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task

Background

Experimental designs using surrogate gait-like movements, such as in functional magnetic resonance imaging (MRI), cannot fully capture the cortical activation associated with overground gait. Overground gait in a robotic exoskeleton may be an ideal tool to generate controlled sensorimotor stimulation of gait conditions like ‘active’ (i.e. user moves with the device) and ‘passive’ (i.e. user is moved by the device) gait. To truly understand these neural mechanisms, functional near-infrared spectroscopy (fNIRS) would yield greater ecological validity. Thus, the aim of this experiment was to use fNIRS to delineate brain activation differences between ‘Active’ and ‘Passive’ overground gait in a robotic exoskeleton.

Methods

Fourteen healthy adults performed 10 walking trials in a robotic exoskeleton for Passive and Active conditions, with fNIRS over bilateral frontal and parietal lobes, and electromyography (EMG) over bilateral thigh muscles. Digitization of optode locations and individual T1 MRI scans were used to demarcate the brain regions fNIRS recorded from.

Results

Increased oxyhemoglobin in the right frontal cortex was found for Passive compared with Active conditions. For deoxyhemoglobin, increased activation during Passive was found in the left frontal cortex and bilateral parietal cortices compared with Active; one channel in the left parietal cortex decreased during Active when compared with Passive. Normalized EMG mean amplitude was higher in the Active compared with Passive conditions for all four muscles (p ≤ 0.044), confirming participants produced the conditions asked of them.

Conclusions

The parietal cortex is active during passive robotic exoskeleton gait, a novel finding as research to date has not recorded posterior to the primary somatosensory cortex. Increased activation of the parietal cortex may be related to the planning of limb coordination while maintaining postural control. Future neurorehabilitation research could use fNIRS to examine whether exoskeletal gait training can increase gait-related brain activation with individuals unable to walk independently.

Tracking differential activation of primary and supplementary motor cortex across timing tasks: An fNIRS validation study

Functional near-infrared spectroscopy (fNIRS) provides an alternative to functional magnetic resonance imaging (fMRI) for assessing changes in cortical hemodynamics. To establish the utility of fNIRS for measuring differential recruitment of the motor network during the production of timing-based actions, we measured cortical hemodynamic responses in 10 healthy adults while they performed two versions of a finger-tapping task. The task, used in an earlier fMRI study (Jantzen et al., 2004), was designed to track the neural basis of different timing behaviors. Participants paced their tapping to a metronomic tone, then continued tapping at the established pace without the tone. Initial tapping was either synchronous or syncopated relative to the tone. This produced a 2 × 2 design: synchronous or syncopated tapping and pacing the tapping with or continuing without a tone. Accuracy of the timing of tapping was tracked while cortical hemodynamics were monitored using fNIRS. Hemodynamic responses were computed by canonical statistical analysis across trials in each of the four conditions. Task-induced brain activation resulted in significant increases in oxygenated hemoglobin concentration (oxy-Hb) in a broad region in and around the motor cortex. Overall, syncopated tapping was harder behaviorally and produced more cortical activation than synchronous tapping. Thus, we observed significant changes in oxy-Hb in direct relation to the complexity of the task.

Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies

Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic "brain" responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.

Using anatomically defined regions-of-interest to adjust for head-size and probe alignment in functional near-infrared spectroscopy

Significance: Functional near-infrared spectroscopy (fNIRS) uses surface-placed light sources and detectors to record underlying changes in the brain due to fluctuations in hemoglobin levels and oxygenation. Since these measurements are recorded from the surface of the scalp, the mapping from underlying regions-of-interest (ROIs) in the brain space to the fNIRS channel space measurements depends on the registration of the sensors, the anatomy of the head/brain, and the sensitivity of these diffuse measurements through the tissue. However, small displacements in the probe position can change the distribution of recorded brain activity across the fNIRS measurements. Aim: We propose an approach using either individual or atlas-based brain-space anatomical information to define ROI-based statistical hypotheses to test the null involvement of specific regions, which allows us to test the analogous ROI across subjects while adjusting for fNIRS probe placement and sensitivity differences due to head size variations without a localizer task. Approach: We use the optical forward model to project the underlying brain-space ROI into a tapered contrast vector, which defines the relative weighting of the fNIRS channels contributing to the ROI and allows us to test the null hypothesis of no brain activity in this region during a functional task. We demonstrate this method through simulation and compare the sensitivity-specificity of this approach to other conventional methods. Results: We examine the performance of this method in the scenario where head size and probe registration are both an accurately known parameters and where this is subject to unknown experimental errors. This method is compared with the performance of the conventional method using 364 different simulation parameter combinations. Conclusion: The proposed method is always recommended in ROI-based analysis, since it significantly improves the analysis performance without a localizer task, wherever the fNIRS probe registration is known or unknown.

Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovascular Coupling

Portable neuroimaging technologies can be employed for long-term monitoring of neurophysiological and neuropathological states. Functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are highly suited for such a purpose. Their multimodal integration allows the evaluation of hemodynamic and electrical brain activity together with neurovascular coupling. An innovative fNIRS-EEG system is here presented. The system integrated a novel continuous-wave fNIRS component and a modified commercial EEG device. fNIRS probing relied on fiberless technology based on light emitting diodes and silicon photomultipliers (SiPMs). SiPMs are sensitive semiconductor detectors, whose large detection area maximizes photon harvesting from the scalp and overcomes limitations of fiberless technology. To optimize the signal-to-noise ratio and avoid fNIRS-EEG interference, a digital lock-in was implemented for fNIRS signal acquisition. A benchtop characterization of the fNIRS component showed its high performances with a noise equivalent power below 1 pW. Moreover, the fNIRS-EEG device was tested in vivo during tasks stimulating visual, motor and pre-frontal cortices. Finally, the capabilities to perform ecological recordings were assessed in clinical settings on one Alzheimer's Disease patient during long-lasting cognitive tests. The system can pave the way to portable technologies for accurate evaluation of multimodal brain activity, allowing their extensive employment in ecological environments and clinical practice.

Resting-State NIRS-EEG in Unresponsive Patients with Acute Brain Injury: A Proof-of-Concept Study

BACKGROUND

Neurovascular-based imaging techniques such as functional MRI (fMRI) may reveal signs of consciousness in clinically unresponsive patients but are often subject to logistical challenges in the intensive care unit (ICU). Near-infrared spectroscopy (NIRS) is another neurovascular imaging technique but low cost, can be performed serially at the bedside, and may be combined with electroencephalography (EEG), which are important advantages compared to fMRI. Combined NIRS-EEG, however, has never been evaluated for the assessment of neurovascular coupling and consciousness in acute brain injury.

METHODS

We explored resting-state oscillations in eight-channel NIRS oxyhemoglobin and eight-channel EEG band-power signals to assess neurovascular coupling, the prerequisite for neurovascular-based imaging detection of consciousness, in patients with acute brain injury in the ICU (n = 9). Conscious neurological patients from step-down units and wards served as controls (n = 14). Unsupervised adaptive mixture-independent component analysis (AMICA) was used to correlate NIRS-EEG data with levels of consciousness and clinical outcome.

RESULTS

Neurovascular coupling between NIRS oxyhemoglobin (0.07-0.13 Hz) and EEG band-power (1-12 Hz) signals at frontal areas was sensitive and prognostic to changing consciousness levels. AMICA revealed a mixture of five models from EEG data, with the relative probabilities of these models reflecting levels of consciousness over multiple days, although the accuracy was less than 85%. However, when combined with two channels of bilateral frontal neurovascular coupling, weighted k-nearest neighbor classification of AMICA probabilities distinguished unresponsive patients from conscious controls with > 90% accuracy (positive predictive value 93%, false discovery rate 7%) and, additionally, identified patients who subsequently failed to recover consciousness with > 99% accuracy.

DISCUSSION

We suggest that NIRS-EEG for monitoring of acute brain injury in the ICU is worthy of further exploration. Normalization of neurovascular coupling may herald recovery of consciousness after acute brain injury.

Brain-Based Binary Communication Using Spatiotemporal Features of fNIRS Responses

“Locked-in” patients lose their ability to communicate naturally due to motor system dysfunction. Brain-computer interfacing offers a solution for their inability to communicate by enabling motor-independent communication. Straightforward and convenient in-session communication is essential in clinical environments. The present study introduces a functional near-infrared spectroscopy (fNIRS)-based binary communication paradigm that requires limited preparation time and merely nine optodes. Eighteen healthy participants performed two mental imagery tasks, mental drawing and spatial navigation, to answer yes/no questions during one of two auditorily cued time windows. Each of the six questions was answered five times, resulting in five trials per answer. This communication paradigm thus combines both spatial (two different mental imagery tasks, here mental drawing for “yes” and spatial navigation for “no”) and temporal (distinct time windows for encoding a “yes” and “no” answer) fNIRS signal features for information encoding. Participants’ answers were decoded in simulated real-time using general linear model analysis. Joint analysis of all five encoding trials resulted in an average accuracy of 66.67 and 58.33% using the oxygenated (HbO) and deoxygenated (HbR) hemoglobin signal respectively. For half of the participants, an accuracy of 83.33% or higher was reached using either the HbO signal or the HbR signal. For four participants, effective communication with 100% accuracy was achieved using either the HbO or HbR signal. An explorative analysis investigated the differentiability of the two mental tasks based solely on spatial fNIRS signal features. Using multivariate pattern analysis (MVPA) group single-trial accuracies of 58.33% (using 20 training trials per task) and 60.56% (using 40 training trials per task) could be obtained. Combining the five trials per run using a majority voting approach heightened these MVPA accuracies to 62.04 and 75%. Additionally, an fNIRS suitability questionnaire capturing participants’ physical features was administered to explore its predictive value for evaluating general data quality. Obtained questionnaire scores correlated significantly (r = -0.499) with the signal-to-noise of the raw light intensities. While more work is needed to further increase decoding accuracy, this study shows the potential of answer encoding using spatiotemporal fNIRS signal features or spatial fNIRS signal features only.

Longitudinal effect of transcranial direct current stimulation on knee osteoarthritis patients measured by functional infrared spectroscopy: a pilot study

Significance: Knee osteoarthritis (OA) is a common joint disease causing chronic pain and functional alterations (stiffness and swelling) in the elderly population. OA is currently treated pharmacologically with analgesics, although neuromodulation via transcranial direct current stimulation (tDCS) has recently generated a growing interest as a safe side-effect free treatment alternative or a complement to medications for chronic pain conditions. Although a number of studies have shown that tDCS has a beneficial effect on behavioral measures of pain, the mechanistic action of neuromodulation on pain sensitivity and coping at the central nervous system is not well understood. Aim: We aimed at observing longitudinal changes of cortical hemodynamics in older adults with knee OA associated with a two-week-long tDCS self-treatment protocol. Approach: Hemodynamics was measured bilaterally in the motor and somatosensory cortices with functional near-infrared spectroscopy (fNIRS) in response to thermal pain induced ipsilaterally to the knee primarily affected by OA. Results: We found that both oxyhemoglobin- and deoxyhemoglobin-related functional activations significantly increased during the course of the tDCS treatment, supporting the notion that tDCS yields an increased cortical excitability. Concurrently, clinical measures of pain decreased with tDCS treatment, hinting at a potential spatial dissociation between cortically mediated pain perception and suppression and the prevalence of neuromodulatory effects over cortical pain processing. Conclusions: fNIRS is a valid method for objectively tracking pain in an ambulatory setting and it could potentially be used to inform strategies for optimized tDCS treatment and to develop innovative tDCS protocols.

Wearable, Fiber-less, Multi-Channel System for Continuous Wave Functional Near Infrared Spectroscopy Based on Silicon Photomultipliers Detectors and Lock-In Amplification

Development and in-vivo validation of a Continuous Wave (CW) functional Near Infrared Spectroscopy (fNIRS) system is presented. The system is wearable, fiber-less, multi-channel (16×16, 256 channels) and expandable and it relies on silicon photomultipliers (SiPMs) for light detection. SiPMs are inexpensive, low voltage and resilient semiconductor light detectors, whose performances are analogous to photomultiplier tubes (PMTs). The advantage of SiPMs with respect to PMTs is that they allow direct contact with the scalp and avoidance of optical fibers. In fact, the coupling of SiPMs and light emitting diodes (LEDs) allows the transfer of the analog signals to and from the scalp through thin electric cables that greatly increase the system flexibility. Moreover, the optical probes, mechanically resembling electroencephalographic electrodes, are robust against motion artifacts. In order to increase the signal-to-noise-ratio (SNR) of the fNIRS acquisition and to decrease ambient noise contamination, a digital lock-in technique was implemented through LEDs modulation and SiPMs signal processing chain. In-vivo validation proved the system capabilities of detecting functional brain activity in the sensorimotor cortices. When compared to other state-of-the-art wearable fNIRS systems, the single photon sensitivity and dynamic range of SiPMs can exploit the long and variable interoptode distances needed for estimation of brain functional hemodynamics using CW-fNIRS.

Cortical Speech Processing in Postlingually Deaf Adult Cochlear Implant Users, as Revealed by Functional Near-Infrared Spectroscopy

An experiment was conducted to investigate the feasibility of using functional near-infrared spectroscopy (fNIRS) to image cortical activity in the language areas of cochlear implant (CI) users and to explore the association between the activity and their speech understanding ability. Using fNIRS, 15 experienced CI users and 14 normal-hearing participants were imaged while presented with either visual speech or auditory speech. Brain activation was measured from the prefrontal, temporal, and parietal lobe in both hemispheres, including the language-associated regions. In response to visual speech, the activation levels of CI users in an a priori region of interest (ROI)—the left superior temporal gyrus or sulcus—were negatively correlated with auditory speech understanding. This result suggests that increased cross-modal activity in the auditory cortex is predictive of poor auditory speech understanding. In another two ROIs, in which CI users showed significantly different mean activation levels in response to auditory speech compared with normal-hearing listeners, activation levels were significantly negatively correlated with CI users’ auditory speech understanding. These ROIs were located in the right anterior temporal lobe (including a portion of prefrontal lobe) and the left middle superior temporal lobe. In conclusion, fNIRS successfully revealed activation patterns in CI users associated with their auditory speech understanding.

Assessing hearing by measuring heartbeat: The effect of sound level

Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technique that measures changes in oxygenated and de-oxygenated hemoglobin concentration and can provide a measure of brain activity. In addition to neural activity, fNIRS signals contain components that can be used to extract physiological information such as cardiac measures. Previous studies have shown changes in cardiac activity in response to different sounds. This study investigated whether cardiac responses collected using fNIRS differ for different loudness of sounds. fNIRS data were collected from 28 normal hearing participants. Cardiac response measures evoked by broadband, amplitude-modulated sounds were extracted for four sound intensities ranging from near-threshold to comfortably loud levels (15, 40, 65 and 90 dB Sound Pressure Level (SPL)). Following onset of the noise stimulus, heart rate initially decreased for sounds of 15 and 40 dB SPL, reaching a significantly lower rate at 15 dB SPL. For sounds at 65 and 90 dB SPL, increases in heart rate were seen. To quantify the timing of significant changes, inter-beat intervals were assessed. For sounds at 40 dB SPL, an immediate significant change in the first two inter-beat intervals following sound onset was found. At other levels, the most significant change appeared later (beats 3 to 5 following sound onset). In conclusion, changes in heart rate were associated with the level of sound with a clear difference in response to near-threshold sounds compared to comfortably loud sounds. These findings may be used alone or in conjunction with other measures such as fNIRS brain activity for evaluation of hearing ability.

Functional near-infrared spectroscopy as a tool for assessing speech and spoken language processing in pediatric and adult cochlear implant users

Much of what is known about the course of auditory learning in following cochlear implantation is based on behavioral indicators that users are able to perceive sound. Both prelingually deafened children and postlingually deafened adults who receive cochlear implants display highly variable speech and language processing outcomes, although the basis for this is poorly understood. To date, measuring neural activity within the auditory cortex of implant recipients of all ages has been challenging, primarily because the use of traditional neuroimaging techniques is limited by the implant itself. Functional near-infrared spectroscopy (fNIRS) is an imaging technology that works with implant users of all ages because it is non-invasive, compatible with implant devices, and not subject to electrical artifacts. Thus, fNIRS can provide insight into processing factors that contribute to variations in spoken language outcomes in implant users, both children and adults. There are important considerations to be made when using fNIRS, particularly with children, to maximize the signal-to-noise ratio and to best identify and interpret cortical responses. This review considers these issues, recent data, and future directions for using fNIRS as a tool to understand spoken language processing in children and adults who hear through a cochlear implant.

Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise⁻Cognition Science: A Systematic, Methodology-Focused Review

For cognitive processes to function well, it is essential that the brain is optimally supplied with oxygen and blood. In recent years, evidence has emerged suggesting that cerebral oxygenation and hemodynamics can be modified with physical activity. To better understand the relationship between cerebral oxygenation/hemodynamics, physical activity, and cognition, the application of state-of-the art neuroimaging tools is essential. Functional near-infrared spectroscopy (fNIRS) is such a neuroimaging tool especially suitable to investigate the effects of physical activity/exercises on cerebral oxygenation and hemodynamics due to its capability to quantify changes in the concentration of oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) non-invasively in the human brain. However, currently there is no clear standardized procedure regarding the application, data processing, and data analysis of fNIRS, and there is a large heterogeneity regarding how fNIRS is applied in the field of exercise⁻cognition science. Therefore, this review aims to summarize the current methodological knowledge about fNIRS application in studies measuring the cortical hemodynamic responses during cognitive testing (i) prior and after different physical activities interventions, and (ii) in cross-sectional studies accounting for the physical fitness level of their participants. Based on the review of the methodology of 35 as relevant considered publications, we outline recommendations for future fNIRS studies in the field of exercise⁻cognition science.

Automated Processing of fNIRS Data-A Visual Guide to the Pitfalls and Consequences

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.