Development of a portable multi-channel broadband near infrared spectroscopy instrument to measure brain tissue oxygenation and metabolism during functional activation and seizures

Epilepsy is a common neurological disorder often developed during childhood, characterised by abnormal neuronal discharges. These spontaneous recurrent seizures can be associated with poor long-term neurological development. Near-infrared spectroscopy (NIRS) is a non-invasive tech- nique able to monitor cerebral concentration changes in oxygenated- (∆[HbO2]) and deoxygenated- (∆[HHb]) haemoglobin. However, current commercial NIRS systems use only a few wavelengths, limiting their use to haemodynamic monitoring. Broadband NIRS (bNIRS) systems use a larger number of wavelengths enabling changes in concentration of the oxidation state of cytochrome-c- oxidase (∆[oxCCO]) to be determined, a marker of cellular metabolism. This thesis describes the development and miniaturisation of an existing bNIRS system to monitor haemodynamic and metabolic changes in children with epilepsy. Using the latest technological advancements, the bulk and complexity of the system was reduced while increasing the number of measurement channels. Two miniature tungsten halogen light sources were utilised with time- multiplexing capabilities implemented (0.5Hz). Bifurcated optical fibre bundles (2.8mm diameter) connected to each light source and twelve detector fibre bundles (1mm diameter) arranged linearly into a ferrule (25mm diameter); modification of the interface between the detectors and lens-based spectrograph ensured compatibility with the increased detector number. Light was collimated to a diffraction grating with a wider 308nm bandwidth and the largest CCD image sensor available (1340x1300 array, 26.8x26mm) was integrated into the system. LabVIEW software was updated to enable simultaneous, real-time collection and display of intensity and concentration changes. Extensive testing of the system was performed; in-vivo testing in healthy adults using a Stroop task demonstrated a typical haemodynamic response with regional variation in metabolism. Si- multaneous bNIRS and electroencephalography data were collected from 12 children with epilepsy in the Neurology Unit. One patient case study is presented in detail, with temporal data from 17 seizures collected. A large decrease in metabolism was observed in the left posterior region, corresponding to a region of cortical malformation, suggesting an energetic deficiency in this re- gion. This indicates the potential for ∆[oxCCO] as an investigative marker in monitoring seizures, providing localised information about cellular oxygen utilisation

A Newcomer\u27s Guide to Functional Near Infrared Spectroscopy Experiments

This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs

A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go?

This mini-review is aimed at briefly summarizing the present status of functional near-infrared spectroscopy (fNIRS) and predicting where the technique should go in the next decade. This mini-review quotes 33 articles on the different fNIRS basics and technical developments and 44 reviews on the fNIRS applications published in the last eight years. The huge number of review articles about a wide spectrum of topics in the field of cognitive and social sciences, functional neuroimaging research, and medicine testifies to the maturity achieved by this non-invasive optical vascular-based functional neuroimaging technique. Today, fNIRS has started to be utilized on healthy subjects while moving freely in different naturalistic settings. Further instrumental developments are expected to be done in the near future to fully satisfy this latter important aspect. In addition, fNIRS procedures, including correction methods for the strong extracranial interferences, need to be standardized before using fNIRS as a clinical tool in individual patients. New research avenues such as interactive neurosciences, cortical activation modulated by different type of sport performance, and cortical activation during neurofeedback training are highlighted

Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health.Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After extracting the brain’s hemodynamic response from fNIRS signal, different linear and nonlinear features were extracted from the signal which are then used for stress levels classification both individually and in combination.Results: In this study, the maximum accuracy of 88.72% was achieved in classification between high and low stress levels, and 96.92% was obtained for the stress and rest states.Conclusion: Our results showed that using the proposed linear and nonlinear features it is possible to effectively classify stress levels from fNIRS signals recorded from only one site in the prefrontal cortex. Comparing to other methods, it is shown that the proposed algorithm outperforms other previously reported methods using the nonlinear features extracted from the fNIRS signal. These results clearly show the potential of fNIRS signal as a useful tool for early diagnosis and quantify stress

Validation of fNIRS System as a Technique to Monitor Cognitive Workload

CognitiveWorkload (CW) is a key factor in the human learning context. Knowing the optimal amount of CW is essential to maximise cognitive performance, emerging as an important variable in e-learning systems and Brain-Computer Interfaces (BCI) applications. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising avenue of brain discovery because of its easy setup and robust results. It is, in fact, along with Electroencephalography (EEG), an encouraging technique in the context of BCI. Brain- Computer Interfaces, by tracking the user’s cognitive state, are suitable for educational systems. Thus, this work sought to validate the fNIRS technique for monitoring different CW stages. For this purpose, we acquired the fNIRS and EEG signals when performing cognitive tasks, which included a progressive increase of difficulty and simulation of the learning process. We also used the breathing sensor and the participants’ facial expressions to assess their cognitive status. We found that both visual inspections of fNIRS signals and power spectral analysis of EEG bands are not sufficient for discriminating cognitive states, nor quantify CW. However, by applying machine learning (ML) algorithms, we were able to distinguish these states with mean accuracies of 79.8%, reaching a value of 100% in one specific case. Our findings provide evidence that fNIRS technique has the potential to monitor different levels of CW. Furthermore, our results suggest that this technique allied with the EEG and combined via ML algorithms is a promising tool to be used in the e-learning and BCI fields for its skill to discriminate and characterize cognitive states.O esforço cognitivo (CW) é um factor relevante no contexto da aprendizagem humana. Conhecer a quantidade óptima de CW é essencial para maximizar o desempenho cognitivo, surgindo como uma variável importante em sistemas de e-learning e aplicações de Interfaces Cérebro-Computador (BCI). A Espectroscopia Funcional de Infravermelho Próximo (fNIRS) emergiu como uma via de descoberta do cérebro devido à sua fácil configuração e resultados robustos. É, de facto, juntamente com a Electroencefalografia (EEG), uma técnica encorajadora no contexto de BCI. As interfaces cérebro-computador, ao monitorizar o estado cognitivo do utilizador, são adequadas para sistemas educativos. Assim, este trabalho procurou validar o sistema de fNIRS como uma técnica de monitorização de CW. Para este efeito, adquirimos os sinais fNIRS e EEG aquando da execução de tarefas cognitivas, que incluiram um aumento progressivo de dificuldade e simulação do processo de aprendizagem. Utilizámos, ainda, o sensor de respiração e as expressões faciais dos participantes para avaliar o seu estado cognitivo. Verificámos que tanto a inspeção visual dos sinais de fNIRS como a análise espectral dos sinais de EEG não são suficientes para discriminar estados cognitivos, nem para quantificar o CW. No entanto, aplicando algoritmos de machine learning (ML), fomos capazes de distinguir estes estados com exatidões médias de 79.8%, chegando a atingir o valor de 100% num caso específico. Os nossos resultados fornecem provas da prospecção da técnica fNIRS para supervisionar diferentes níveis de CW. Além disso, os nossos resultados sugerem que esta técnica aliada à de EEG e combinada via algoritmos ML é uma ferramenta promissora a ser utilizada nos campos do e-learning e de BCI, pela sua capacidade de discriminar e caracterizar estados cognitivos

Optical imaging and spectroscopy for the study of the human brain: status report.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions