Sabrina Brigadoi

Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data

Motion artifacts are a significant source of noise in many functional near-infrared spectroscopy (fNIRS) experiments. Despite this, there is no well-established method for their removal. Instead, functional trials of fNIRS data containing a motion artifact are often rejected completely. However, in most experimental circumstances the number of trials is limited, and multiple motion artifacts are common, particularly in challenging populations. Many methods have been proposed recently to correct for motion artifacts, including principle component analysis, spline interpolation, Kalman filtering, wavelet filtering and correlation-based signal improvement. The performance of different techniques has been often compared in simulations, but only rarely has it been assessed on real functional data. Here, we compare the performance of these motion correction techniques on real functional data acquired during a cognitive task, which required the participant to speak aloud, leading to a low-frequency, low-amplitude motion artifact that is correlated with the hemodynamic response. To compare the efficacy of these methods, objective metrics related to the physiology of the hemodynamic response have been derived. Our results show that it is always better to correct for motion artifacts than reject trials, and that wavelet filtering is the most effective approach to correcting this type of artifact, reducing the area under the curve where the artifact is present in 93% of the cases. Our results therefore support previous studies that have shown wavelet filtering to be the most promising and powerful technique for the correction of motion artifacts in fNIRS data. The analyses performed here can serve as a guide for others to objectively test the impact of different motion correction algorithms and therefore select the most appropriate for the analysis of their own fNIRS experiment.

How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy.

Abstract. In recent years, it has been demonstrated that using functional near-infrared spectroscopy (fNIRS) channels with short separations to explicitly sample extra-cerebral tissues can provide a significant improvement in the accuracy and reliability of fNIRS measurements. The aim of these short-separation channels is to measure the same superficial hemodynamics observed by standard fNIRS channels while also being insensitive to the brain. We use Monte Carlo simulations of photon transport in anatomically informed multilayer models to determine the optimum source–detector distance for short-separation channels in adult and newborn populations. We present a look-up plot that provides (for an acceptable value of short-separation channel brain sensitivity relative to standard channel brain sensitivity) the optimum short-separation distance. Though values vary across the scalp, when the acceptable ratio of the short-separation channel brain sensitivity to standard channel brain sensitivity is set at 5%, the optimum short-separation distance is 8.4 mm in the typical adult and 2.15 mm in the term-age infant.

A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements

Functional near-infrared spectroscopy (fNIRS) uses near-infrared light to measure cortical concentration changes in oxygenated (HbO) and deoxygenated hemoglobin (HbR) held to be correlated with cognitive activity. Providing a parametric depiction of such changes in the classic form of stimulus-evoked hemodynamic responses (HRs) can be attained with this technique only by solving two problems. One problem concerns the separation of informative optical signal from structurally analogous noise generated by a variety of spurious sources, such as heart beat, respiration, and vasomotor waves. Another problem pertains to the inherent variability of HRs, which is notoriously contingent on the type of experiment, brain region monitored, and human phenotype. A novel method was devised in the present context to solve both problems based on a two-step algorithm combining the treatment of noise-only data extrapolated from a reference-channel and a Bayesian filter applied on a per-trial basis. The present method was compared to two current methods based on conventional averaging, namely, a typical averaging method and an averaging method implementing the use of a reference-channel. The result of the comparison, carried out both on artificial and real data, revealed a sensitive accuracy improvement in HR estimation using the present method relative to each of the other methods.

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

Diffuse optical tomography is most accurate when an individuals 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 individuals 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.

Unleashing the future potential of functional near-infrared spectroscopy in brain sciences

The wondrous innovations bound to the introduction of functional near-infrared spectroscopy in cognitive neuroscience are characterized by a multifaceted nature, ranging from technological improvements to sophisticated signal processing methods; the outstanding progress enabled scientists to investigate a variety of hard-to-test clinical populations and to successfully employ optical imaging in fields that were almost unimaginable twenty years ago. Here we illustrate how the emerging use of fNIRS methodologies might represent a drawing power in a variety of challenging experimental and medical contexts; we expect in the near future a wide increase of the use of wireless fNIRS, especially in children and in particular clinical populations, as well as a striking progress of fNIRS-BCI and hybrid BCI systems for neurofeedback and neurorehabilitation. These emerging trends might dramatically foster the future potential of fNIRS in brain sciences, provided that they are properly supported by a significant progress in signal processing and cognitive neuroscience.

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.

Prefrontal Cortex Activation Upon a Demanding Virtual Hand-Controlled Task: A New Frontier for Neuroergonomics

Functional near-infrared spectroscopy (fNIRS) is a non-invasive vascular-based functional neuroimaging technology that can assess, simultaneously from multiple cortical areas, concentration changes in oxygenated-deoxygenated hemoglobin at the level of the cortical microcirculation blood vessels. fNIRS, with its high degree of ecological validity and its very limited requirement of physical constraints to subjects, could represent a valid tool for monitoring cortical responses in the research field of neuroergonomics. In virtual reality (VR) real situations can be replicated with greater control than those obtainable in the real world. Therefore, VR is the ideal setting where studies about neuroergonomics applications can be performed. The aim of the present study was to investigate, by a 20-channel fNIRS system, the dorsolateral/ventrolateral prefrontal cortex (DLPFC/VLPFC) in subjects while performing a demanding VR hand-controlled task (HCT). Considering the complexity of the HCT, its execution should require the attentional resources allocation and the integration of different executive functions. The HCT simulates the interaction with a real, remotely-driven, system operating in a critical environment. The hand movements were captured by a high spatial and temporal resolution 3-dimensional (3D) hand-sensing device, the LEAP motion controller, a gesture-based control interface that could be used in VR for tele-operated applications. Fifteen University students were asked to guide, with their right hand/forearm, a virtual ball (VB) over a virtual route (VROU) reproducing a 42 m narrow road including some critical points. The subjects tried to travel as long as possible without making VB fall. The distance traveled by the guided VB was 70.2 ± 37.2 m. The less skilled subjects failed several times in guiding the VB over the VROU. Nevertheless, a bilateral VLPFC activation, in response to the HCT execution, was observed in all the subjects. No correlation was found between the distance traveled by the guided VB and the corresponding cortical activation. These results confirm the suitability of fNIRS technology to objectively evaluate cortical hemodynamic changes occurring in VR environments. Future studies could give a contribution to a better understanding of the cognitive mechanisms underlying human performance either in expert or non-expert operators during the simulation of different demanding/fatiguing activities.

A novel semi-immersive virtual reality visuo-motor task activates ventrolateral prefrontal cortex: a functional near-infrared spectroscopy study.

OBJECTIVE In the last few years, the interest in applying virtual reality systems for neurorehabilitation is increasing. Their compatibility with neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), allows for the investigation of brain reorganization with multimodal stimulation and real-time control of the changes occurring in brain activity. The present study was aimed at testing a novel semi-immersive visuo-motor task (VMT), which has the features of being adopted in the field of neurorehabilitation of the upper limb motor function. APPROACH A virtual environment was simulated through a three-dimensional hand-sensing device (the LEAP Motion Controller), and the concomitant VMT-related prefrontal cortex (PFC) response was monitored non-invasively by fNIRS. Upon the VMT, performed at three different levels of difficulty, it was hypothesized that the PFC would be activated with an expected greater level of activation in the ventrolateral PFC (VLPFC), given its involvement in the motor action planning and in the allocation of the attentional resources to generate goals from current contexts. Twenty-one subjects were asked to move their right hand/forearm with the purpose of guiding a virtual sphere over a virtual path. A twenty-channel fNIRS system was employed for measuring changes in PFC oxygenated-deoxygenated hemoglobin (O2Hb/HHb, respectively). MAIN RESULTS A VLPFC O2Hb increase and a concomitant HHb decrease were observed during the VMT performance, without any difference in relation to the task difficulty. SIGNIFICANCE The present study has revealed a particular involvement of the VLPFC in the execution of the novel proposed semi-immersive VMT adoptable in the neurorehabilitation field.

A methodology to improve estimation of stimulus-evoked hemodynamic response from fNIRS measurements

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging method used to investigate functional activity of the cerebral cortex evoked by cognitive, visual, auditory and motor tasks, detecting regional changes of oxy- and deoxy-hemoglobin concentration. Accurate estimation of the stimulus-evoked hemodynamic response (HR) from fNIRS signals in order to quantitatively investigate cognitive functions requires to cope with several noise components. Some of them appear as random disturbances (typically tackled through averaging techniques), while others are due to physiological sources, such as heart beat, respiration, vasomotor waves, and are particularly challenging to be dealt with because they lie in the same frequency band of HR. In this work we present a new two-steps methodology for the HR estimation from fNIRS data. The first step is a pre-processing stage where physiological trends in fNIRS data are reduced by exploiting a mathematical model identified from the signal of a reference channel. In the second step, the pre-processed data of the other channels are filtered with a recently presented non-parametric Bayesian approach (Scarpa et al., Optics Express, 2010). The presented method for HR estimation is compared with widely used methods: conventional averaging, band-pass filtering and principal component analysis (PCA). Results on simulated data reveal the ability of the proposed method to improve the accuracy of the estimates of the functional hemodynamic response, as well as the estimate of peak amplitude and latency. Encouraging preliminary results in a representative real data set showing an improvement of contrast to noise ratio are also reported.

A virtual ball task driven by forearm movements for neuro-rehabilitation

The present study was aimed at describing a semi-immersive virtual reality environment, driven by a 3D hand sensing device (LEAP Motion Controller), to define a virtual task based on a virtual ball moving on a virtual path. The prefrontal cortex haemodynamic responses during the execution of this demanding task were evaluated by a 16-channel functional near-infrared spectroscopy (fNIRS) system. A bilateral ventrolateral prefrontal cortex activation was found during the virtual task. Although the proposed task has not been yet applied in the neuro-rehabilitation field, it has the potential to be adopted in the upper limb functional assessment and rehabilitation treatment.