Correspondence of Electroencephalography and Near-Infrared Spectroscopy Sensitivities to the Cerebral Cortex using a High-Density Layout

This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R=0.46±0.08 . Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R=0.43±0.07 . Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization

Concurrent fNIRS and EEG for brain function investigation: A systematic, methodology-focused review

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research

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

MEG Source Imaging and Group Analysis Using VBMEG

Variational Bayesian Multimodal EncephaloGraphy (VBMEG) is a MATLAB toolbox that estimates distributed source currents from magnetoencephalography (MEG)/electroencephalography (EEG) data by integrating functional MRI (fMRI) (https://vbmeg.atr.jp/). VBMEG also estimates whole-brain connectome dynamics using anatomical connectivity derived from a diffusion MRI (dMRI). In this paper, we introduce the VBMEG toolbox and demonstrate its usefulness. By collaborating with VBMEG's tutorial page (https://vbmeg.atr.jp/docs/v2/static/vbmeg2_tutorial_neuromag.html), we show its full pipeline using an open dataset recorded by Wakeman and Henson (2015). We import the MEG data and preprocess them to estimate the source currents. From the estimated source currents, we perform a group analysis and examine the differences of current amplitudes between conditions by controlling the false discovery rate (FDR), which yields results consistent with previous studies. We highlight VBMEG's characteristics by comparing these results with those obtained by other source imaging methods: weighted minimum norm estimate (wMNE), dynamic statistical parametric mapping (dSPM), and linearly constrained minimum variance (LCMV) beamformer. We also estimate source currents from the EEG data and the whole-brain connectome dynamics from the MEG data and dMRI. The observed results indicate the reliability, characteristics, and usefulness of VBMEG

Preoperative brain imaging using functional near-infrared spectroscopy helps predict cochlear implant outcome in deaf adults

Currently it is not possible to accurately predict how well a deaf individual will be able to understand speech when hearing is (re)introduced via a cochlear implant. Differences in brain organisation following deafness are thought to contribute to variability in speech understanding with a cochlear implant and may offer unique insights that could help to more reliably predict outcomes. An emerging optical neuroimaging technique, functional near-infrared spectroscopy (fNIRS), was used to determine whether a preoperative measure of brain activation could explain variability in CI outcomes and offer additional prognostic value above that provided by known clinical characteristics. Cross-modal activation to visual speech was measured in bilateral superior temporal cortex of profoundly deaf adults before cochlear implantation. Behavioural measures of auditory speech understanding were obtained in the same individuals following six months of cochlear-implant use. The results showed that stronger preoperative cross-modal activation of auditory brain regions by visual speech was predictive of poorer auditory speech understanding after implantation. Further investigation suggested that this relationship may have been driven primarily by group differences between pre- and post-lingually deaf individuals. Nonetheless, preoperative cortical imaging provided additional prognostic value above that of influential clinical characteristics, including the age-at-onset and duration of auditory deprivation, suggesting that objectively assessing the physiological status of the brain using fNIRS imaging preoperatively may support more accurate prediction of individual CI outcomes. Whilst activation of auditory brain regions by visual speech prior to implantation was related to the CI user’s clinical history of deafness, activation to visual speech did not relate to the future ability of these brain regions to respond to auditory speech stimulation with a CI. Greater preoperative activation of left superior temporal cortex by visual speech was associated with enhanced speechreading abilities, suggesting that visual-speech processing may help to maintain left temporal-lobe specialisation for language processing during periods of profound deafness

Empirical comparison of deep learning models for fNIRS pain decoding

Introduction: Pain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain.Methods: In this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features.Results: The results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models.Discussion: Overall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Leveraging Artificial Intelligence to Improve EEG-fNIRS Data Analysis

La spectroscopie proche infrarouge fonctionnelle (fNIRS) est apparue comme une technique de neuroimagerie qui permet une surveillance non invasive et à long terme de l'hémodynamique corticale. Les technologies de neuroimagerie multimodale en milieu clinique permettent d'étudier les maladies neurologiques aiguës et chroniques. Dans ce travail, nous nous concentrons sur l'épilepsie - un trouble chronique du système nerveux central affectant près de 50 millions de personnes dans le monde entier prédisposant les individus affectés à des crises récurrentes. Les crises sont des aberrations transitoires de l'activité électrique du cerveau qui conduisent à des symptômes physiques perturbateurs tels que des changements aigus ou chroniques des compétences cognitives, des hallucinations sensorielles ou des convulsions de tout le corps. Environ un tiers des patients épileptiques sont récalcitrants au traitement pharmacologique et ces crises intraitables présentent un risque grave de blessure et diminuent la qualité de vie globale. Dans ce travail, nous étudions 1. l'utilité des informations hémodynamiques dérivées des signaux fNIRS dans une tâche de détection des crises et les avantages qu'elles procurent dans un environnement multimodal par rapport aux signaux électroencéphalographiques (EEG) seuls, et 2. la capacité des signaux neuronaux, dérivé de l'EEG, pour prédire l'hémodynamique dans le cerveau afin de mieux comprendre le cerveau épileptique. Sur la base de données rétrospectives EEG-fNIRS recueillies auprès de 40 patients épileptiques et utilisant de nouveaux modèles d'apprentissage en profondeur, la première étude de cette thèse suggère que les signaux fNIRS offrent une sensibilité et une spécificité accrues pour la détection des crises par rapport à l'EEG seul. La validation du modèle a été effectuée à l'aide de l'ensemble de données CHBMIT open source documenté et bien référencé avant d'utiliser notre ensemble de données EEG-fNIRS multimodal interne. Les résultats de cette étude ont démontré que fNIRS améliore la détection des crises par rapport à l'EEG seul et ont motivé les expériences ultérieures qui ont déterminé la capacité prédictive d'un modèle d'apprentissage approfondi développé en interne pour décoder les signaux d'état de repos hémodynamique à partir du spectre complet et d'une bande de fréquences neuronale codée spécifique signaux d'état de repos (signaux sans crise). Ces résultats suggèrent qu'un autoencodeur multimodal peut apprendre des relations multimodales pour prédire les signaux d'état de repos. Les résultats suggèrent en outre que des gammes de fréquences EEG plus élevées prédisent l'hémodynamique avec une erreur de reconstruction plus faible par rapport aux gammes de fréquences EEG plus basses. De plus, les connexions fonctionnelles montrent des modèles spatiaux similaires entre l'état de repos expérimental et les prédictions fNIRS du modèle. Cela démontre pour la première fois que l'auto-encodage intermodal à partir de signaux neuronaux peut prédire l'hémodynamique cérébrale dans une certaine mesure. Les résultats de cette thèse avancent le potentiel de l'utilisation d'EEG-fNIRS pour des tâches cliniques pratiques (détection des crises, prédiction hémodynamique) ainsi que l'examen des relations fondamentales présentes dans le cerveau à l'aide de modèles d'apprentissage profond. S'il y a une augmentation du nombre d'ensembles de données disponibles à l'avenir, ces modèles pourraient être en mesure de généraliser les prédictions qui pourraient éventuellement conduire à la technologie EEG-fNIRS à être utilisée régulièrement comme un outil clinique viable dans une grande variété de troubles neuropathologiques.----------ABSTRACT Functional near-infrared spectroscopy (fNIRS) has emerged as a neuroimaging technique that allows for non-invasive and long-term monitoring of cortical hemodynamics. Multimodal neuroimaging technologies in clinical settings allow for the investigation of acute and chronic neurological diseases. In this work, we focus on epilepsy—a chronic disorder of the central nervous system affecting almost 50 million people world-wide predisposing affected individuals to recurrent seizures. Seizures are transient aberrations in the brain's electrical activity that lead to disruptive physical symptoms such as acute or chronic changes in cognitive skills, sensory hallucinations, or whole-body convulsions. Approximately a third of epileptic patients are recalcitrant to pharmacological treatment and these intractable seizures pose a serious risk for injury and decrease overall quality of life. In this work, we study 1) the utility of hemodynamic information derived from fNIRS signals in a seizure detection task and the benefit they provide in a multimodal setting as compared to electroencephalographic (EEG) signals alone, and 2) the ability of neural signals, derived from EEG, to predict hemodynamics in the brain in an effort to better understand the epileptic brain. Based on retrospective EEG-fNIRS data collected from 40 epileptic patients and utilizing novel deep learning models, the first study in this thesis suggests that fNIRS signals offer increased sensitivity and specificity metrics for seizure detection when compared to EEG alone. Model validation was performed using the documented open source and well referenced CHBMIT dataset before using our in-house multimodal EEG-fNIRS dataset. The results from this study demonstrated that fNIRS improves seizure detection as compared to EEG alone and motivated the subsequent experiments which determined the predictive capacity of an in-house developed deep learning model to decode hemodynamic resting state signals from full spectrum and specific frequency band encoded neural resting state signals (seizure free signals). These results suggest that a multimodal autoencoder can learn multimodal relations to predict resting state signals. Findings further suggested that higher EEG frequency ranges predict hemodynamics with lower reconstruction error in comparison to lower EEG frequency ranges. Furthermore, functional connections show similar spatial patterns between experimental resting state and model fNIRS predictions. This demonstrates for the first time that intermodal autoencoding from neural signals can predict cerebral hemodynamics to a certain extent. The results of this thesis advance the potential of using EEG-fNIRS for practical clinical tasks (seizure detection, hemodynamic prediction) as well as examining fundamental relationships present in the brain using deep learning models. If there is an increase in the number of datasets available in the future, these models may be able to generalize predictions which would possibly lead to EEG-fNIRS technology to be routinely used as a viable clinical tool in a wide variety of neuropathological disorders

Cerebral language networks and neuropsychological profile in children with frontotemporal lobe epilepsy : a multimodal neuroimaging and neuropsychological approach

Thèse de doctorat présentée en vue de l'obtention du doctorat en psychologie (Ph.D).L'enfance et l'adolescence sont des périodes uniques de la vie où les changements neuronaux favorisent l'établissement de réseaux cérébraux matures et le développement des capacités intellectuelles. Le langage est un domaine cognitif qui est, non seulement essentiel pour la communication interhumaine, mais qui contribue également au développement de nombreuse capacités et prédit de manière significative la réussite académique. Les régions cérébrales frontotemporales sont des régions clés du réseau langagier du cerveau. Il a été démontré que les neuropathologies telles que l'épilepsie des lobes frontal et temporal (ELF et ELT) interfèrent avec le développement des réseaux cérébraux du langage et provoquent des circuits cérébraux aberrants. Les patrons exacts de réorganisation des réseaux cérébraux fonctionnels ne sont toutefois, pas entièrement compris et l'association avec le profil neuropsychologique reste spéculative. Par conséquent, l'objectif principal de cette thèse est d'accroître la compréhension des altérations du réseau langagier et d'améliorer les connaissances de l'association de l'architecture du réseau et des capacités cognitives chez les enfants et les adolescents avec ELF ou ELT. La présente thèse est composée de trois articles scientifiques, les deux premiers présentant des travaux méthodologiques qui ont permis d'optimiser les méthodes appliquées dans le troisième article, l'étude empirique principale menée auprès d'enfants avec ELF et ELT. Le premier article présente le bilan neuropsychologique pédiatrique comme un outil important pour estimer les capacités cognitives et dresser un profil cognitif avec ses forces et ses faiblesses. Dans le deuxième article, l'analyse factorielle parallèle (PARAFAC) est présentée et validée comme une nouvelle technique employée pour corriger les artefacts de mouvement qui contaminent le signal hémodynamique évalué par la spectroscopie fonctionnelle proche infrarouge (fNIRS). Une meilleure qualité du signal permet une interprétation fiable de la réponse cérébrale en plis de déduire des métriques d'organisation du réseau cérébral. Le troisième article consiste en une étude empirique, où le traitement cérébral du langage, est comparé entre des enfants avec ELF et ELT, et des pairs neuroptypiques. Les schémas de connectivité fonctionnelle indiquent que le groupe de patients présente moins de connexions intra-hémisphériques dans l'hémisphère gauche et entre les hémisphères, et des connexions accrues dans l'hémisphère droit par rapport au groupe témoin. Les mesures de l'architecture du réseau révèlent en outre une efficacité de traitement local plus élevée dans l'hémisphère droit chez les enfants atteints de ELF et ELT par rapport aux enfants en bonne santé. L'architecture du réseau local de l'hémisphère gauche et la capacité intellectuelle globale dans le groupe de patients sont négativement liées, tandis que dans le groupe contrôle, aucune association de ce type n'est identifiable. Ces résultats suggèrent que la réorganisation du réseau de langage chez les enfants avec ELF ou ELT semble dans certains cas soutenir un meilleur résultat cognitif, soit lorsque l'efficacité du traitement local dans l'hémisphère gauche est diminuée. Au contraire, une plus grande efficacité de traitement local semble être une caractéristique d'un réseau de langage cérébral associé à de moins bonnes capacités cognitives. Les travaux de recherche de cette thèse de doctorat fournissent des lignes directrices pour l'utilisation de l'évaluation neuropsychologique pédiatrique, à la fois dans un contexte clinique et scientifique. L'introduction de PARAFAC pour corriger les artefacts de mouvement dans le signal fNIRS est un ajout important au pipeline de prétraitement qui permet d'augmenter la qualité du signal pour une analyse ultérieure. De futurs projets pourront s'appuyer sur cette validation initiale et étendre l'utilisation de PARAFAC pour les analyses du signal fNIRS. Sur cette base méthodologique solide, le travail empirique confirme l'incidence accrue de circuits cérébraux aberrants liés au traitement du langage chez les enfants atteints de ELF et de ELT, et soutient en outre l'efficacité du réseau local en tant que déterminant clé de l'impact de la plasticité cérébrale précoce sur les capacités cognitives. Afin de mieux comprendre les altérations du réseau en réponse aux neuropathologies et leur impact, des études avec des échantillons plus grands et de différents groupes d'âge, devraient étudier plus spécifiquement le rôle des facteurs cliniques (e.g., le type d'épilepsie, la latéralisation de l'épilepsie, le contrôle des crises, etc.) et aborder leurs influences sur le développement. À long terme, cela augmentera le pronostic des phénotypes cliniques chez les patients pédiatriques atteints de ELF et de ELT, et offrira des opportunités d'interventions précoces pour soutenir un développement typique.Childhood and adolescence are unique periods in life where neuronal changes support the establishment of mature brain networks and the development of intellectual capacities. Language is one cognitive domain that is not only an essential part of inter-human communication but also contributes to the development of other capacities and significantly influences academic achievement. Frontotemporal brain areas are key regions of the brain's language network. Neuropathologies such as frontal and temporal lobe epilepsies (FLE and TLE) have been shown to interfere with developing brain language networks and cause aberrant cerebral circuits. The exact patterns of functional brain network reorganization are not fully understood and the association with the neuropsychological profile remains speculative. Therefore, the main objective of this thesis was to increase comprehension of language network alterations and enhance the knowledge on the association of network topology and cognitive capacities in children and adolescents with FLE or TLE. This thesis consists of three scientific articles, with the first two presenting methodological work that allowed for the optimization of the methods applied in the third article, which is the main empirical study conducted on children with FLE and TLE. The first article presents the pediatric neuropsychological assessment as a valuable tool to estimate cognitive capacities and draw a cognitive profile with strengths and weaknesses. In the second article, parallel factor analysis (PARAFAC) is presented and validated as a novel technique to correct motion artifacts that contaminate the hemodynamic signal assessed with functional near-infrared spectroscopy (fNIRS). A better signal quality is the basis for a reliable interpretation of the cerebral response and derive metrics of brain network organization. The third article consists of an empirical study where cerebral language processing is compared between children with FLE and TLE, and neuroptypical peers. Patterns of functional connectivity indicate that the patient group demonstrates fewer intra-hemispheric connections in the left hemisphere and between hemispheres, and increased connections within the right hemisphere as compared to the control group. Metrics of network architecture further reveal a higher local processing efficiency within the right hemisphere in children with FLE and TLE compared to healthy peers. Local network architecture of the left hemisphere and the overall intellectual capacity in the patient group is negatively related, while in the control group no such association is identifiable. These findings suggest that language network reorganization in children with FLE or TLE in some cases seems to support a better cognitive outcome, namely when local processing efficiency in the left hemisphere is decreased. On the contrary, a higher local processing efficiency seems to be a characteristic of a brain language network that goes along with worse cognitive capacities. The research work of this doctoral thesis provides guidelines for the use of pediatric neuropsychological assessment both in a clinical and scientific context. The introduction of PARAFAC to correct motion artifact in the fNIRS signal is an important add-on to the preprocessing pipeline that allows to increase signal quality for subsequent analysis. Future projects will be able to build on this initial validation and extend PARAFAC's use for fNIRS analysis. On this solid methodological foundation, the empirical work confirms the increased incidence of aberrant brain circuits related to language processing in children with FLE and TLE, and further supports local network efficiency as a key determinant of the impact of early brain plasticity on cognitive capacities. In order to further understand network alterations in response to neuropathologies and their impact, studies with larger samples sizes and different age groups should further investigate the specific role of clinical factors (e.g., epilepsy type, epilepsy lateralization, seizure control, etc.) and address developmental influences. Ultimately, this will increase prognosis of clinical phenotypes in pediatric patients with FLE and TLE, and offer opportunities for early interventions to support a healthy development