Exploring the combined use of electrical and hemodynamic brain activity to investigate brain function

This thesis explored the relationship between electrical and metabolic aspects of brain functioning in health and disease, measured with QEEG and NIRS, in order to evaluate its clinical potential. First the limitations of NIRS were investigated, depicting its susceptibility to different types of motion artefacts and the inability of the CBSI-method to remove them from resting state data. Furthermore, the quality of the NIRS signals was poor in a significant portion of the investigated sample, reducing clinical potential. Different analysis methods were used to explore both EEG and NIRS, and their coupling in an eyes open eyes closed paradigm in healthy participants. It could be reproduced that during eyes closed blocks less HbO2 (p = 0.000), more Hbb (p = 0.008), and more alpha activity (p = 0.000) was present compared to eyes open blocks. Furthermore, dynamic cross correlation analysis reproduced a positive correlation between alpha and Hbb (r: 0.457 and 0.337) and a negative correlation between alpha and HbO2 (r: -0.380 and -0.366) with a delayed hemodynamic response (7 to 8s). This was only possible when removing all questionable and physiological illogical data, suggesting that an 8s hemodynamic delay might not be the golden standard. Also the inability of the cross correlation to take non-linear relationships into account may distort outcomes. Therefore, In chapter 5 non-linear aspects of the relationship were evaluated by introducing the measure of relative cross mutual information. A newly suggested approach and the most valuable contribution of the thesis since it broadens knowledge in the fields of EEG, NIRS and general time series analysis. Data of two stroke patients then showed differences from the healthy group between the coupling of EEG and NIRS. The differences in long range temporal correlations (p= 0.000 for both cases), entropy (p< 0.040 and p =0.000), and relative cross mutual information (p < 0.003 and p < 0.013) provide the proof of principle that these measures may have clinical utility. Even though more research is necessary before widespread clinical use becomes possible

Biomedical Signal Analysis of the Brain and Systemic Physiology

Near-infrared spectroscopy (NIRS) is a non-invasive and easy-to-use diagnostic technique that enables real-time tissue oxygenation measurements applied in various contexts and for different purposes. Continuous monitoring with NIRS of brain oxygenation, for example, in neonatal intensive care units (NICUs), is essential to prevent lifelong disabilities in newborns. Moreover, NIRS can be applied to observe brain activity associated with hemodynamic changes in blood flow due to neurovascular coupling. In the latter case, NIRS contributes to studying cognitive processes allowing to conduct experiments in natural and socially interactive contexts of everyday life. However, it is essential to measure systemic physiology and NIRS signals concurrently. The combination of brain and body signals enables to build sophisticated systems that, for example, reduce the false alarms that occur in NICUs. Furthermore, since fNIRS signals are influenced by systemic physiology, it is essential to understand how the latter impacts brain signals in functional studies. There is an interesting brain body coupling that has rarely been investigated yet. To take full advantage of these brain and body data, the aim of this thesis was to develop novel approaches to analyze these biosignals to extract the information and identify new patterns, to solve different research or clinical questions. For this the development of new methodological approaches and sophisticated data analysis is necessary, because often the identification of these patterns is challenging or not possible with traditional methods. In such cases, automatic machine learning (ML) techniques are beneficial. The first contribution of this work was to assess the known systemic physiology augmented (f)NIRS approach for clinical use and in everyday life. Based on physiological and NIRS signals of preterm infants, an ML-based classification system has been realized, able to reduce the false alarms in NICUs by providing a high sensitivity rate. In addition, the SPA-fNIRS approach was further applied in adults during a breathing task. The second contribution of this work was the advancement of the classical fNIRS hyperscanning method by adding systemic physiology measures. For this, new biosignal analyses in the time-frequency domain have been developed and tested in a simple nonverbal synchrony task between pairs of subjects. Furthermore, based on SPA-fNIRS hyperscanning data, another ML-based system was created, which is able distinguish familiar and unfamiliar pairs with high accuracy. This approach enables to determine the strength of social bonds in a wide range of social interaction contexts. In conclusion, we were the first group to perform a SPA-fNIRS hyperscanning study capturing changes in cerebral oxygenation and hemodynamics as well as systemic physiology in two subjects simultaneously. We applied new biosignals analysis methods enabling new insights into the study of social interactions. This work opens the door to many future inter-subjects fNIRS studies with the benefit of assessing the brain-to-brain, the brain-to-body, and body-to-body coupling between pairs of subjects

Biophysical modeling of hemodynamic-based neuroimaging techniques

Thesis (Ph. D. in Medical Engineering and Medical Physics)--Harvard-MIT Program in Health Sciences and Technology, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 163-182).Two different hemodynamic-based neuroimaging techniques were studied in this work. Near-Infrared Spectroscopy (NIRS) is a promising technique to measure cerebral hemodynamics in a clinical setting due to its potential for continuous monitoring. However, the presence of strong systemic interference in the signal significantly limits our ability to recover the hemodynamic response without averaging tens of trials. Developing a new methodology to clean the NIRS signal from systemic interference and isolate the cortical signal would therefore significantly increase our ability to recover the hemodynamic response opening the door for clinical NIRS studies such as epilepsy. Toward this goal, a new method based on multi-distance measurements and state-space modeling was developed and further optimized to remove systemic physiological oscillations contaminating the NIRS signal. Furthermore, the cortical and pial contributions to the NIRS signal were quantified using a new multimodal regression analysis. Functional Magnetic Resonance Imaging (fMRI) based on the Blood Oxygenation Level Dependent (BOLD) response has become the method of choice for exploring brain function, and yet the physiological basis of this technique is still poorly understood. Despite the effort, a detailed and validated model relating the signal measured to the physiological changes occurring in the cortical tissue is still lacking. Modeling the BOLD signal is challenging because of the difficulty to take into account the complex morphology of the cortical microvasculature, the distribution of oxygen in those microvessels and its dynamics during neuronal activation. Here, we overcome this difficulty by performing Monte Carlo simulations over real microvascular networks and oxygen distributions measured in vivo on rodents, at rest and during forepaw stimulation, using two-photon microscopy. Our model reveals for the first time the specific contribution of individual vascular compartment to the BOLD signal, for different field strengths and different cortical orientations. Our model makes a new prediction: the amplitude of the BOLD signal produced by a given physiological change during neuronal activation depends on the spatial orientation of the cortical region in the MRI scanner. This occurs because veins are preferentially oriented either perpendicular or parallel to the cortical surface in the gray matter.by Louis Gagnon.Ph.D.in Medical Engineering and Medical Physic

Study of the Hemodynamic Response to Interictal Epileptiform Discharges in Human Epilepsy Using Functional Near Infrared Spectroscopy

RÉSUMÉ L'imagerie spectroscopique proche infrarouge fonctionnelle (ISPIf) s'est imposée comme technique d’imagerie neuronale prometteuse. Cette dernière permet une surveillance non invasive de l'évolution chronique de l'activité hémodynamique corticale. Durant la dernière décennie, ISPIf combiné avec l'électroencéphalographie (EEG) a été appliqué dans le contexte de l'épilepsie humaine, et a permi d’explorer le lien entre l’activité neurale et hémodynamique. Cependant, la plupart des travaux antérieurs sont uniquement axés sur l'étude des crises d'épilepsie qui sont aléatoires et se produisent rarement pendant un test de l’EEG-ISPIf. Cette thèse cherche à évaluer la capacité de l'EEG-ISPIf à observer les changements hémodynamiques associés aux décharges épileptiformes intercritiques (DEIs), et à déterminer si ces DEIs peuvent également être utilisés pour extraire de l'information additionnelle servant à la localisation du site d’un foyer épileptique. En se basant sur des données multimodales EEG-ISPIf recueillies sur un grand échantillon de patients (40), combiné à l'utilisation d'un modèle linéaire généralisé (MLG), une première étude a permis la quantification préliminaire de la sensibilité et la spécificité de la technique en utilisant la détection des zones cérébrales activées par des DEIs pour la localisation de la région du foyer épileptique. Dans un sous-groupe de 29 patients atteints au niveau de la région néocorticale, lorsque mesuré durant des évènements de DEIs, des diminutions de la concentration d’hémoglobine désoxygénée (HbR) (chez 62% des sujets) et des augmentations de la concentration de l’hémoglobine oxygénée (HbO) (chez 38% des sujets) ont été observées. De plus, cette variation en HbR et HbO était significativement plus forte dans la région du foyer épileptique (qui donc pourrait conduire à une localisation du foyer épileptique) dans 28% / 21% des patients. Ces estimations modestes de la sensibilité et de la spécificité suggèrent que l'utilisation d'une fonction de réponse hémodynamique (FRH) canonique n’est pas optimale dans l’analyse des DEIs par MLG classique. Par conséquent, une seconde approche a été explorée dans le cadre d’une deuxième étude par modélisation des variations spécifiques à chaque patient dans la construction de la réponse hémodynamique associée aux DEIs. Un terme quadratique a également été ajouté au modèle pour tenir compte de la non-linéarité de la réponse associée à une fréquence plus élevée d’évènements lors de l'enregistrement. Ces nouveaux modèles ont d'abord été validés numériquement par simulations, avant d’être appliqués à l'analyse de données de cinq patients sélectionnés. Lorsque comparée à la FRH canonique, l'utilisation de la FRH spécifique au patient dans l'analyse MLG a non seulement amélioré considérablement les scores statistiques et les étendues spatiales des----------ABSTRACT Functional near-infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging technique as it allows non-invasive and long-term monitoring of cortical hemodynamics. For the last decades, fNIRS combined with electroencephalography (EEG) has been applied in the context of human epilepsy, and has yielded good results. However, most previous work only focused on the study of epileptic seizures which are random and seldom occur during EEG-fNIRS testing. This thesis sought to evaluate the potential of EEG-fNIRS in observing the hemodynamic changes associated with interictal epileptiform discharges (IEDs), and to determine whether these IEDs can also be used to extract useful information in the localization of the epileptic focus site. Based on the EEG-fNIRS data collected from a relatively large number of patients (40) and using a standard general linear model (GLM) approach, the first study of this thesis provided preliminary estimates of the sensitivity and the specificity of EEG-fNIRS in detecting brain areas activated by IEDs and in localizing the epileptic focus region. In the 29 patients with neocortical epilepsies, significant deoxygenated hemoglobin (HbR) concentration decreases and oxygenated hemoglobin (HbO) concentration increases corresponding to IEDs were observed in 62% and 38% of patients respectively. This HbR/HbO response was most significant in the epileptic focus region among all the activations, and thus could lead to successful identification of the epileptic focus site in 28%/21% of the patients. These modest estimates of the sensitivity and the specificity suggested that using a standard GLM with a canonical hemodynamic response function (HRF) might not be the optimal method in the analysis of IEDs. Therefore, the second study of this thesis made a first attempt to model the patient-specific variations in the shape of the hemodynamic response to IEDs. A quadratic term was also added to the model to account for the nonlinearity in the response when frequent IEDs were present in the recording. The new models were first validated through carefully designed simulations, and were then applied in the data analysis of five selected patients. Compared with the canonical HRF, including patient-specific HRFs in the GLM analysis not only significantly improved the statistical scores and the spatial extents of existing activations, but also was able to detect new brain regions activated by IEDs on all of the five patients. These improvements in activation detection also helped obtain more accurate focus localization results in some cases

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

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