Hierarchical Bayesian regularization of reconstructions for diffuse optical tomography using multiple priors

Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex

Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT. We show that the joint reconstruction using both EEG and DOT clearly resolves the two stimuli temporally, and the spatial confinement is drastically improved in comparison to reconstruction using EEG alone.

Brain space image reconstruction of functional near-infrared spectroscopy using a Bayesian adaptive fused sparse overlapping group lasso model

Significance

Functional near-infrared spectroscopy (fNIRS) is a noninvasive technology that uses low levels of nonionizing light in the range of red and near-infrared to record changes in the optical absorption and scattering of the underlying tissue that can be used to infer blood flow and oxygen changes during brain activity. The challenges and difficulties of reconstructing spatial images of hemoglobin changes from fNIRS data are mainly caused by the illposed nature of the optical inverse model.

Aim

We describe a Bayesian approach combining several lasso-based regularizations to apply anatomy-prior information to solving the inverse model.

Approach

We built a Bayesian hierarchical model to solve the Bayesian adaptive fused sparse overlapping group lasso (Ba-FSOGL) model. The method is evaluated and validated using simulation and experimental datasets.

Results

We apply this approach to the simulation and experimental datasets to reconstruct a known brain activity. The reconstructed images and statistical plots are shown.

Conclusion

We discuss the adaptation of this method to fNIRS data and demonstrate that this approach provides accurate image reconstruction with a low false-positive rate, through numerical simulations and application to experimental data collected during motor and sensory tasks.

Diffuse Optical Tomography Using fNIRS Signals Measured from the Skull Surface of the Macaque Monkey

Diffuse optical tomography (DOT), as a functional near-infrared spectroscopy (fNIRS) technique, can estimate three-dimensional (3D) images of the functional hemodynamic response in brain volume from measured optical signals. In this study, we applied DOT algorithms to the fNIRS data recorded from the surface of macaque monkeys’ skulls when the animals performed food retrieval tasks using either the left- or right-hand under head-free conditions. The hemodynamic response images, reconstructed by DOT with a high sampling rate and fine voxel size, demonstrated significant activations at the upper limb regions of the primary motor area in the central sulcus and premotor, and parietal areas contralateral to the hands used in the tasks. The results were also reliable in terms of consistency across different recording dates. Time-series analyses of each brain area revealed preceding activity of premotor area to primary motor area consistent with previous physiological studies. Therefore, the fNIRS–DOT protocol demonstrated in this study provides reliable 3D functional brain images over a period of days under head-free conditions for region-of-interest–based time-series analysis.

Evaluation of a personalized functional near infra-red optical tomography workflow using maximum entropy on the mean

In the present study, we proposed and evaluated a workflow of personalized near infra-red optical tomography (NIROT) using functional near-infrared spectroscopy (fNIRS) for spatiotemporal imaging of cortical hemodynamic fluctuations. The proposed workflow from fNIRS data acquisition to local 3D reconstruction consists of: (a) the personalized optimal montage maximizing fNIRS channel sensitivity to a predefined targeted brain region; (b) the optimized fNIRS data acquisition involving installation of optodes and digitalization of their positions using a neuronavigation system; and (c) the 3D local reconstruction using maximum entropy on the mean (MEM) to accurately estimate the location and spatial extent of fNIRS hemodynamic fluctuations along the cortical surface. The workflow was evaluated on finger-tapping fNIRS data acquired from 10 healthy subjects for whom we estimated the reconstructed NIROT spatiotemporal images and compared with functional magnetic resonance imaging (fMRI) results from the same individuals. Using the fMRI activation maps as our reference, we quantitatively compared the performance of two NIROT approaches, the MEM framework and the conventional minimum norm estimation (MNE) method. Quantitative comparisons were performed at both single subject and group-level. Overall, our results suggested that MEM provided better spatial accuracy than MNE, while both methods offered similar temporal accuracy when reconstructing oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentration changes evoked by finger-tapping. Our proposed complete workflow was made available in the brainstorm fNIRS processing plugin-NIRSTORM, thus providing the opportunity for other researchers to further apply it to other tasks and on larger populations.

Deconvolution of hemodynamic responses along the cortical surface using personalized functional near infrared spectroscopy

In functional near infrared spectroscopy (fNIRS), deconvolution analysis of oxy and deoxy-hemoglobin concentration changes allows estimating specific hemodynamic response functions (HRF) elicited by neuronal activity, taking advantage of the fNIRS excellent temporal resolution. Diffuse optical tomography (DOT) is also becoming the new standard reconstruction procedure as it is more accurate than the modified Beer Lambert law approach at the sensor level. The objective of this study was to assess the relevance of HRF deconvolution after DOT constrained along the cortical surface. We used local personalized fNIRS montages which consists in optimizing the position of fNIRS optodes to ensure maximal sensitivity to subject specific target brain regions. We carefully evaluated the accuracy of deconvolution when applied after DOT, using realistic simulations involving several HRF models at different signal to noise ratio (SNR) levels and on real data related to motor and visual tasks in healthy subjects and from spontaneous pathological activity in one patient with epilepsy. We demonstrated that DOT followed by deconvolution was able to accurately recover a large variability of HRFs over a large range of SNRs. We found good performances of deconvolution analysis for SNR levels usually encountered in our applications and we were able to reconstruct accurately the temporal dynamics of HRFs in real conditions.

Enhanced spatiotemporal resolution imaging of neuronal activity using joint electroencephalography and diffuse optical tomography

Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.

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.

Optimal positioning of optodes on the scalp for personalized functional near-infrared spectroscopy investigations

BACKGROUND

Application of functional Near InfraRed Spectroscopy (fNIRS) in neurology is still limited as a good optical coupling and optimized optode coverage of specific brain regions remains challenging, notably for prolonged monitoring.

METHODS

We propose to evaluate a new procedure allowing accurate investigation of specific brain regions. The procedure consists in: (i) A priori maximization of spatial sensitivity of fNIRS measurements targeting specific brain regions, while reducing the number of applied optodes in order to decrease installation time and improve subject comfort. (ii) Utilization of a 3D neuronavigation device and usage of collodion to glue optodes on the scalp, ensuring good optical contact for prolonged investigations. (iii) Local reconstruction of the hemodynamic activity along the cortical surface using inverse modelling.

RESULTS

Using realistic simulations, we demonstrated that maps derived from optimal montage acquisitions showed, after reconstruction, spatial resolution only slightly lower to that of ultra high density montages while significantly reducing the number of optodes. The optimal montages provided overall good quantitative accuracy especially at the peak of the spatially reconstructed map. We also evaluated real motor responses in two healthy subjects and obtained reproducible motor responses over different sessions.

COMPARISON WITH EXISTING METHODS

We are among the first to propose a mathematical optimization strategy, allowing high sensitivity measurements.

CONCLUSIONS

Our results support that using personalized optimal montages should allow to conduct accurate fNIRS studies in clinical settings and realistic lifestyle conditions.

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

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

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

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique, which uses light to measure changes in cerebral blood oxygenation through sensors placed on the surface of the scalp. We recorded concurrent fNIRS with magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) in order to investigate the group-level correspondence of these measures with source-localized fNIRS estimates. Healthy participants took part in both a concurrent fNIRS-MEG and fNIRS-fMRI neuroimaging session during two somatosensory stimulation tasks, a blocked design median nerve localizer and parametric pulsed-pair median nerve stimulation using interpulse intervals from 100 to 500 ms. We found the spatial correlation for estimated activation patterns from the somatosensory task was [Formula: see text], 0.57, and [Formula: see text] and the amplitude correlation was [Formula: see text], 0.52, and [Formula: see text] for fMRI-MEG, fMRI-fNIRS oxy-hemoglobin, and fMRI-fNIRS deoxy-hemoglobin signals, respectively. Taken together, these results show good correspondence among the fMRI, fNIRS, and MEG with the great majority of the difference across modalities being driven by lower sensitivity for deeper brain sources in MEG and fNIRS. These results provide an important validation of source-localized fNIRS in the context of concurrent multimodal imaging for future studies of the relationship between physiological effects in the human brain.

Overview of diffuse optical tomography and its clinical applications

Near-infrared diffuse optical tomography (DOT), one of the most sophisticated optical imaging techniques for observations through biological tissue, allows 3-D quantitative imaging of optical properties, which include functional and anatomical information. With DOT, it is expected to be possible to overcome the limitations of conventional near-infrared spectroscopy (NIRS) as well as offering the potential for diagnostic optical imaging. However, DOT has been under development for more than 30 years, and the difficulties in development are attributed to the fact that light is strongly scattered and that diffusive photons are used for the image reconstruction. The DOT algorithm is based on the techniques of inverse problems. The radiative transfer equation accurately describes photon propagation in biological tissue, while, because of its high computation load, the diffusion equation (DE) is often used as the forward model. However, the DE is invalid in low-scattering and/or highly absorbing regions and in the vicinity of light sources. The inverse problem is inherently ill-posed and highly undetermined. Here, we first summarize NIRS and then describe various approaches in the efforts to develop accurate and efficient DOT algorithms and present some examples of clinical applications. Finally, we discuss the future prospects of DOT.

Functional Near-Infrared Spectroscopy Evidence for Development of Prefrontal Engagement in Working Memory in Early Through Middle Childhood

The neural underpinnings of working memory are hypothesized to develop incrementally across preschool and early school age, coinciding with the rapid maturation of executive function occurring during this period. This study investigates the development of prefrontal cortex function between the ages of 3 and 7. Children (n = 68) participated in a novel spatial working memory task while their middle and lateral prefrontal cortex (LPFC) was monitored using functional near infrared spectroscopy (fNIRS). We found increased activation of the LPFC when comparing working memory to rest. Greater LPFC increase was noted for longer compared with shorter delay periods. Increase in LPFC activation, accuracy, and response speed were positively correlated with child age, suggesting that developmental changes in prefrontal function might underlie effective development of executive function in this age range.

Hemodynamic Response to Interictal Epileptiform Discharges Addressed by Personalized EEG-fNIRS Recordings

Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional Near InfraRed Spectroscopy (fNIRS) recordings.

Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF) and cluster-permutation EEG-fMRI analyses were performed for comparison purposes.

Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (seven patients), followed by oxy-hemoglobin decreases (six patients). HR was lateralized in six patients and lasted from 8.5 to 30 s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result). The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30 s.

Conclusions: (i) EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; (ii) cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function (iii) the HR is often bilateral and lasts up to 30 s.

Combining energy and Laplacian regularization to accurately retrieve the depth of brain activity of diffuse optical tomographic data

Diffuse optical tomography (DOT) provides data about brain function using surface recordings. Despite recent advancements, an unbiased method for estimating the depth of absorption changes and for providing an accurate three-dimensional (3-D) reconstruction remains elusive. DOT involves solving an ill-posed inverse problem, requiring additional criteria for finding unique solutions. The most commonly used criterion is energy minimization (energy constraint). However, as measurements are taken from only one side of the medium (the scalp) and sensitivity is greater at shallow depths, the energy constraint leads to solutions that tend to be small and superficial. To correct for this bias, we combine the energy constraint with another criterion, minimization of spatial derivatives (Laplacian constraint, also used in low resolution electromagnetic tomography, LORETA). Used in isolation, the Laplacian constraint leads to solutions that tend to be large and deep. Using simulated, phantom, and actual brain activation data, we show that combining these two criteria results in accurate (error <2 mm) absorption depth estimates, while maintaining a two-point spatial resolution of <24 mm up to a depth of 30 mm. This indicates that accurate 3-D reconstruction of brain activity up to 30 mm from the scalp can be obtained with DOT.

Brain activation during neurocognitive testing using functional near-infrared spectroscopy in patients following concussion compared to healthy controls

There is no accepted clinical imaging modality for concussion, and current imaging modalities including fMRI, DTI, and PET are expensive and inaccessible to most clinics/patients. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, portable, and low-cost imaging modality that can measure brain activity. The purpose of this study was to compare brain activity as measured by fNIRS in concussed and age-matched controls during the performance of cognitive tasks from a computerized neurocognitive test battery. Participants included nine currently symptomatic patients aged 18-45 years with a recent (15-45 days) sport-related concussion and five age-matched healthy controls. The participants completed a computerized neurocognitive test battery while wearing the fNIRS unit. Our results demonstrated reduced brain activation in the concussed subject group during word memory, (spatial) design memory, digit-symbol substitution (symbol match), and working memory (X's and O's) tasks. Behavioral performance (percent-correct and reaction time respectively) was lower for concussed participants on the word memory, design memory, and symbol match tasks than controls. The results of this preliminary study suggest that fNIRS could be a useful, portable assessment tool to assess reduced brain activation and augment current approaches to assessment and management of patients following concussion.

A Unified Sparse Recovery and Inference Framework for Functional Diffuse Optical Tomography Using Random Effect Model

Diffuse optical tomography (DOT) is a non-invasive imaging technique to reconstruct optical properties of biological tissues using near-infrared light, and it has been successfully used to measure functional brain activities via changes in cerebral blood volume and cerebral blood oxygenation. However, DOT presents a severely ill-posed inverse problem, so various types of regularization should be incorporated to overcome low spatial resolution and lack of depth sensitivity. Another limitation of the conventional DOT reconstruction methods is that an inference step is separately performed after the reconstruction, so complicated interaction between reconstruction and regularization is difficult to analyze. To overcome these technical difficulties, we propose a unified sparse recovery framework using a random effect model whose termination criterion is determined by the statistical inference. Both numerical and experimental results confirm that the proposed method outperforms the conventional approaches.

A 4D neonatal head model for diffuse optical imaging of pre-term to term infants

Diffuse optical tomography is most accurate when an individual's MRI data can be used as a spatial prior for image reconstruction and for visualization of the resulting images of changes in oxy- and deoxy-hemoglobin concentration. As this necessitates an MRI scan to be performed for each study, which undermines many of the advantages of diffuse optical methods, the use of registered atlases to model the individual's anatomy is becoming commonplace. Infant studies require carefully age-matched atlases because of the rapid growth and maturation of the infant brain. In this paper, we present a 4D neonatal head model which, for each week from 29 to 44 weeks post-menstrual age, includes: 1) a multi-layered tissue mask which identifies extra-cerebral layers, cerebrospinal fluid, gray matter, white matter, cerebellum and brainstem, 2) a high-density tetrahedral head mesh, 3) surface meshes for the scalp, gray-matter and white matter layers and 4) cranial landmarks and 10-5 locations on the scalp surface. This package, freely available online at www.ucl.ac.uk/medphys/research/4dneonatalmodel can be applied by users of near-infrared spectroscopy and diffuse optical tomography to optimize probe locations, optimize image reconstruction, register data to cortical locations and ultimately improve the accuracy and interpretation of diffuse optical techniques in newborn populations.

fNIRS evidence of prefrontal regulation of frustration in early childhood

The experience of frustration is common in early childhood, yet some children seem to possess a lower tolerance for frustration than others. Characterizing the biological mechanisms underlying a wide range of frustration tolerance observed in early childhood may inform maladaptive behavior and psychopathology that is associated with this construct. The goal of this study was to measure prefrontal correlates of frustration in 3-5-year-old children, who are not readily adaptable for typical neuroimaging approaches, using functional near infrared spectroscopy (fNIRS). fNIRS of frontal regions were measured as frustration was induced in children through a computer game where a desired and expected prize was "stolen" by an animated dog. A fNIRS general linear model (GLM) was used to quantify the correlation of brain regions with the task and identify areas that were statistically different between the winning and frustrating test conditions. A second-level voxel-based ANOVA analysis was then used to correlate the amplitude of each individual's brain activation with measure of parent-reported frustration. Experimental results indicated increased activity in the middle prefrontal cortex during winning of a desired prize, while lateral prefrontal cortex activity increased during frustration. Further, activity increase in lateral prefrontal cortex during frustration correlated positively with parent-reported frustration tolerance. These findings point to the role of the lateral prefrontal cortex as a potential region supporting the regulation of emotion during frustration.

Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS)

Functional near-infrared spectroscopy (fNIRS) is a non-invasive and portable neuroimaging technique. The method uses non-ionizing laser light in the range of red to near-infrared to detect changes in cerebral blood oxygenation. In this study, we used fNIRS to investigate cortical hemodynamic changes in the temporo-parietal and frontal regions during caloric vestibular stimulation. Caloric stimulation has previously been investigated using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), which serves as a validation of the fNIRS imaging modality toward the measurement of vestibular related brain regions. To date, only a single study has used fNIRS during caloric irrigations, which observed blood volume changes in the temporal-parietal area in healthy younger subjects. In this current study, fNIRS was used to measure cortical vestibular activation in 10 right-handed younger subjects (5 male and 5 female, age 25+/-6 years) and 10 right-handed older subjects (6 male and 4 female, age 74+/-5 years). We investigated both warm (44 °C) and cool (30 °C) unilateral caloric vestibular stimulation. Consistent with previous reports, we found that warm (44 °C) caloric irrigation caused a bilateral activation. In addition, we found that cool (30 °C) caloric irrigation caused contralateral activation of the temporo-parietal area. This study is the first to investigate age effects of the caloric stimulation on brain activity. We found that the older subjects had stronger bilateral effects than the younger subjects. Our results confirm previous fMRI and PET studies that showed cortical activation during caloric vestibular irrigation is dependent on side of irrigation, and temperature of irrigation. Furthermore, our results demonstrate that fNIRS is a viable technique in measuring cortical effects during vestibular tasks.

Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging method that uses light to record regional changes in cerebral blood flow in the cortex during activation. fNIRS uses portable wearable sensors to allow measurements of brain activation during tasking. In this study, fNIRS was used to investigate how the brain processes information from multiple sensory modalities during dynamic posturography. Fifteen healthy volunteers (9M/6F; ages 28+/-9 yrs) participated in the posturography study while undergoing fNIRS brain imaging. Four standard conditions from the sensory organization test (SOT) were performed and a bilateral fNIRS probe was used to examine the cortical brain responses from the frontal, temporal, and parietal brain regions. We found that there was bilateral activation in the temporal-parietal areas (superior temporal gyrus, STG, and supramarginal gyrus, SMG) when both vision and proprioceptive information were degraded; forcing reliance on primarily vestibular information in the control of balance. This is consistent with previous reports of the role of these regions in vestibular control and demonstrates the potential utility of fNIRS in the study of cortical control of vestibular function during standing balance tasks.

Measurement of brain activation during an upright stepping reaction task using functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technology that uses light to measure changes in cortical hemoglobin concentrations. FNIRS measurements are recorded through fiber optic cables, which allow the participant to wear the fNIRS sensors while standing upright. Thus, fNIRS technology is well suited to study cortical brain activity during upright balance, stepping, and gait tasks. In this study, fNIRS was used to measure changes in brain activation from the frontal, motor, and premotor brain regions during an upright step task that required subjects to step laterally in response to visual cues that required executive function control. We hypothesized that cognitive processing during complex stepping cues would elicit brain activation of the frontal cortex in areas involved in cognition. Our results show increased prefrontal activation associated with the processing of the stepping cues. Moreover, these results demonstrate the potential to use fNIRS to investigate cognitive processing during cognitively demanding balance and gait studies.

Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography

High-density diffuse optical tomography (HD-DOT) is an emerging technique for visualizing the internal state of biological tissues. The large number of overlapping measurement channels due to the use of high-density probe arrays permits the reconstruction of the internal optical properties, even with a reflectance-only measurement. However, accurate three-dimensional reconstruction is still a challenging problem. First, the exponentially decaying sensitivity causes a systematic depth-localization error. Second, the nature of diffusive light makes the image blurred. In this paper, we propose a three-dimensional reconstruction method that overcomes these two problems by introducing sensitivity-normalized regularization and sparsity into the hierarchical Bayesian method. Phantom experiments were performed to validate the proposed method under three conditions of probe interval: 26 mm, 18.4 mm, and 13 mm. We found that two absorbers with distances shorter than the probe interval could be discriminated under the high-density conditions of 18.4-mm and 13-mm intervals. This discrimination ability was possible even if the depths of the two absorbers were different from each other. These results show the high spatial resolution of the proposed method in both depth and horizontal directions.

Changes in diffusion path length with old age in diffuse optical tomography

Diffuse, optical near infrared imaging is increasingly being used in various neurocognitive contexts where changes in optical signals are interpreted through activation maps. Statistical population comparison of different age or clinical groups rely on the relative homogeneous distribution of measurements across subjects in order to infer changes in brain function. In the context of an increasing use of diffuse optical imaging with older adult populations, changes in tissue properties and anatomy with age adds additional confounds. Few studies investigated these changes with age. Duncan et al. measured the so-called diffusion path length factor (DPF) in a large population but did not explore beyond the age of 51 after which physiological and anatomical changes are expected to occur [Pediatr. Res. 39(5), 889-894 (1996)]. With increasing interest in studying the geriatric population with optical imaging, we studied changes in tissue properties in young and old subjects using both magnetic resonance imaging (MRI)-guided Monte-Carlo simulations and time-domain diffuse optical imaging. Our results, measured in the frontal cortex, show changes in DPF that are smaller than previously measured by Duncan et al. in a younger population. The origin of these changes are studied using simulations and experimental measures.

Source localization approach for functional DOT using MUSIC and FDR control

In this paper, we formulate diffuse optical tomography (DOT) problems as a source localization problem and propose a MUltiple SIgnal Classification (MUSIC) algorithm for functional brain imaging application. By providing MUSIC spectra for major chromophores such as oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR), we are able to investigate the spatial distribution of brain activities. Moreover, the false discovery rate (FDR) algorithm can be applied to control the family-wise error in the MUSIC spectra. The minimum distance between the center of mass in DOT and the Montreal Neurological Institute (MNI) coordinates of target regions in experiments was between approximately 6 and 18 mm, and the displacement of the center of mass in DOT and fMRI ranged between 12 and 28 mm, which demonstrate the legitimacy of the DOT-based imaging. The proposed brain mapping method revealed its potential as an alternative algorithm to monitor the brain activation.

Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system

Functional near-infrared spectroscopy (fNIRS) is a portable, non-invasive, brain imaging technology that uses low levels of non-ionizing light to record changes in cerebral blood flow in the brain through optical sensors placed on the surface of the scalp. These signals are recorded via flexible fiber optic cables, which allow neuroimaging experiments to be conducted on participants while performing tasks such as standing or walking. FNIRS has the potential to provide new insights into the evolution of brain activation during ambulatory motor learning tasks and standing tasks to probe balance and vestibular function. In this study, a 32 channel fNIRS system was used to record blood flow changes in the frontal, motor, sensory, and temporal cortices during active balancing associated with playing a video game simulating downhill skiing (Nintendo Wii™; Wii-fit™). Using fNIRS, we found activation of superior temporal gyrus, which was modulated by the difficulty of the balance task. This region had been previously implicated in vestibular function from other animal and human studies.

Bedside optical imaging of occipital resting-state functional connectivity in neonates

Resting-state networks derived from temporal correlations of spontaneous hemodynamic fluctuations have been extensively used to elucidate the functional organization of the brain in adults and infants. We have previously developed functional connectivity diffuse optical tomography methods in adults, and we now apply these techniques to study functional connectivity in newborn infants at the bedside. We present functional connectivity maps in the occipital cortices obtained from healthy term-born infants and premature infants, including one infant with an occipital stroke. Our results suggest that functional connectivity diffuse optical tomography has potential as a valuable clinical tool for the early detection of functional deficits and for providing prognostic information on future development.

A random-effects model for group-level analysis of diffuse optical brain imaging

Diffuse optical imaging is a non-invasive technique for measuring changes in blood oxygenation in the brain. This technique is based on the temporally and spatially resolved recording of optical absorption in tissue within the near-infrared range of light. Optical imaging can be used to study functional brain activity similar to functional MRI. However, group level comparisons of brain activity from diffuse optical data are difficult due to registration of optical sensors between subjects. In addition, optical signals are sensitive to inter-subject differences in cranial anatomy and the specific arrangement of optical sensors relative to the underlying functional region. These factors can give rise to partial volume errors and loss of sensitivity and therefore must be accounted for in combining data from multiple subjects. In this work, we describe an image reconstruction approach using a parametric Bayesian model that simultaneously reconstructs group-level images of brain activity in the context of a random-effects analysis. Using this model, we demonstrate that localization accuracy and the statistical effects size of group-level reconstructions can be improved when compared to individualized reconstructions. In this model, we use the Restricted Maximum Likelihood (ReML) method to optimize a Bayesian random-effects model.