The emergence of organic electronics - a technology that relies on carbon-based semiconductors to deliver devices with unique properties - represents one of the most dramatic developments of the past two decades. A rapidly emerging new direction in the field involves the interface with biology. The "soft" nature of organics offers better mechanical compatibility with tissue than traditional electronic materials, while their natural compatibility with mechanically flexible substrates suits the non-planar form factors often required for implants. More importantly, their ability to conduct ions in addition to electrons and holes opens up a new communication channel with biology. The coupling of electronics with living tissue holds the key to a variety of important life-enhancing technologies. One example is bioelectronic implants that record neural signals and/or electrically stimulate neurons. These devices offer unique opportunities to understand and treat conditions such as hearing and vision loss, epilepsy, brain degenerative diseases, and spinal cord injury.The engineering aspect of the work includes the development of a photolithographic process to integrate the conducting polymer poly(3,4-ethylenedioxythiophene: poly(styrene sulfonate) (PEDOT:PSS) with parylene C supports to make an active device. The technology is used to fabricate electrocorticography (ECoG) probes, high-speed transistors and wearable biosensors. The experimental work explores the fundamentals of communication at the interface between conducting polymers and the brain. It is shown that conducting polymers outperform conventional metallic electrodes for brain signals recording.Organic electrochemical transistors (OECTs) represent a step beyond conducting polymer electrodes. They consist of a conducting polymer channel in contact with an electrolyte. When a gate electrode excites an ionic current in the electrolyte, ions enter the polymer film and change its conductivity. Since a small amount of ions can effectively "block" the transistor channel, these devices offer significant amplification in ion-to-electron transduction. Using the developed technology a high-speed and high-density OECTs array is presented. The dense architecture of the array improves the resolution of the recording from neural networks and the transistors temporal response are 100 μs, significantly faster than the action potential. The experimental transistor responses are fit and modeled in order to optimize the gain of the transistor. Using the model, an OECT with two orders of magnitude higher normalized transconductance per channel width is fabricated as compared to Silicon-based field effect transistors. Furthermore, the OECTs are integrated to a highly conformable ECoG probe. This is the first time that a transistor is used to record brain activities in vivo. It shows a far superior signal-to-noise-ratio (SNR) compare to electrodes. The high SNR of the OECT recordings enables the observation of activities from the surface of the brain that only a perpetrating probe can record. Finally, the application of OECTs for biosensing is explored. The bulk of the currently available biosensors often require complex liquid handling, and thus suffer from problems associated with leakage and contamination. The use of an organic electrochemical transistor for detection of lactate by integration of a room temperature ionic liquid in a gel-format, as a solid-state electrolyte is demonstrated.

L'arithmétique est une des inventions majeures de l'humanité, mais il nous manque encore une compréhension globale de la façon dont le cerveau calcule les additions et soustractions. J'ai utilisé une nouvelle méthode comportementale basée sur un suivi de trajectoire capable de disséquer la succession des étapes de traitement impliquées dans les calculs arithmétiques. Les résultats sont compatibles avec un modèle de déplacement pas à pas sur une ligne numérique mentale, en commençant par l'opérande le plus grand et en ajoutant ou soustrayant de manière incrémentielle l'opérande le plus petit. Ensuite, j'ai analysé les signaux électrophysiologiques enregistrés à partir du cortex humain pendant que les sujets résolvaient des additions. L'activité globale dans le sillon intrapariétal augmentait au fur et à mesure que les opérandes grossissaient, prouvant son implication dans le calcul et la prise de décision. Étonnamment, les sites dans le gyrus temporal inférieur postérieur ont montré que l’activation initiale diminuait en fonction de la taille du problème, suggérant un engagement dans l'identification précoce de la difficulté de calcul. Enfin, j'ai enregistré des signaux de magnétoencéphalographie pendant que les sujets vérifiaient les additions et soustractions. En appliquant des techniques d'apprentissage automatique, j'ai étudié l'évolution temporelle des codes de représentation des opérandes et fourni une première image complète d'une cascade d'étapes de traitement en cours sous-jacentes au calcul arithmétique. Ainsi, cette dissertation fournit-elle plusieurs contributions sur la façon dont les concepts mathématiques élémentaires sont mis en œuvre dans le cerveau. / Arithmetic is one of the most important cultural inventions of humanity, however we still lack a comprehensive understanding of how the brain computes additions and subtractions. In the first study, I used a novel behavioral method based on trajectory tracking capable of dissecting the succession of processing stages involved in arithmetic computations. Results supported a model whereby single-digit arithmetic is computed by a stepwise displacement on a spatially organized mental number line, starting with the larger operand and incrementally adding or subtracting the smaller operand. In a second study, I analyzed electrophysiological signals recorded from the human cortex while subjects solved addition problems. I found that the overall activity in the intraparietal sulcus increased as the operands got larger, providing evidence for its involvement in arithmetic computation and decision-making. Surprisingly, sites within the posterior inferior temporal gyrus showed an initial burst of activity that decreased as a function of problem-size, suggesting an engagement in the early identification of the calculation difficulty. Lastly, I recorded magnetoencephalography signals while subjects verified additions and subtractions. By applying machine learning techniques, I investigated the temporal evolution of the representational codes of the operands and provided a first comprehensive picture of a cascade of unfolding processing stages underlying arithmetic calculation. Overall, this dissertation provides several contributions to our knowledge about how elementary mathematical concepts are implemented in the brain.

Pas de résumé en français seulement en anglais / The emergence of organic electronics – a technology that relies on carbon-based semiconductors to deliver devices with unique properties – represents one of the most dramatic developments of the past two decades. A rapidly emerging new direction in the field involves the interface with biology. The “soft” nature of organics offers better mechanical compatibility with tissue than traditional electronic materials, while their natural compatibility with mechanically flexible substrates suits the non-planar form factors often required for implants. More importantly, their ability to conduct ions in addition to electrons and holes opens up a new communication channel with biology. The coupling of electronics with living tissue holds the key to a variety of important life-enhancing technologies. One example is bioelectronic implants that record neural signals and/or electrically stimulate neurons. These devices offer unique opportunities to understand and treat conditions such as hearing and vision loss, epilepsy, brain degenerative diseases, and spinal cord injury.The engineering aspect of the work includes the development of a photolithographic process to integrate the conducting polymer poly(3,4-ethylenedioxythiophene: poly(styrene sulfonate) (PEDOT:PSS) with parylene C supports to make an active device. The technology is used to fabricate electrocorticography (ECoG) probes, high-speed transistors and wearable biosensors. The experimental work explores the fundamentals of communication at the interface between conducting polymers and the brain. It is shown that conducting polymers outperform conventional metallic electrodes for brain signals recording.Organic electrochemical transistors (OECTs) represent a step beyond conducting polymer electrodes. They consist of a conducting polymer channel in contact with an electrolyte. When a gate electrode excites an ionic current in the electrolyte, ions enter the polymer film and change its conductivity. Since a small amount of ions can effectively “block” the transistor channel, these devices offer significant amplification in ion-to-electron transduction. Using the developed technology a high-speed and high-density OECTs array is presented. The dense architecture of the array improves the resolution of the recording from neural networks and the transistors temporal response are 100 μs, significantly faster than the action potential. The experimental transistor responses are fit and modeled in order to optimize the gain of the transistor. Using the model, an OECT with two orders of magnitude higher normalized transconductance per channel width is fabricated as compared to Silicon-based field effect transistors. Furthermore, the OECTs are integrated to a highly conformable ECoG probe. This is the first time that a transistor is used to record brain activities in vivo. It shows a far superior signal-to-noise-ratio (SNR) compare to electrodes. The high SNR of the OECT recordings enables the observation of activities from the surface of the brain that only a perpetrating probe can record. Finally, the application of OECTs for biosensing is explored. The bulk of the currently available biosensors often require complex liquid handling, and thus suffer from problems associated with leakage and contamination. The use of an organic electrochemical transistor for detection of lactate by integration of a room temperature ionic liquid in a gel-format, as a solid-state electrolyte is demonstrated.

The research performed in this thesis aims to improve our understanding about one of the most malignant tumors of the human brain – glioma. From the early stages of my career I was confronted with the cruel reality of losing patients due to this devastating disease. The studies performed over the last four years involve extensive data analysis in different clinical and laboratory settings. The direct application of different analysis methods and tools in order to investigate the glioma infiltration delineation has potentially lead to direct applications of our results in the clinical setting. The overall approach of the study is based on three primary outcome measures, i.e., neuronal, electrophysiological and genetic/molecular features for distinguishing infiltrated and non-infiltrated zones within specifically peritumoral tissue (PT) and, more extensively, across the radiologically-defined boundaries of healthy, peritumoral and tumoral tissues. As such, we propose for the first time an objective demarcation and characterization of the PT and we detail how the genetic and epigenetic alterations within the tumoral and peritumoral area are linked with macroscopic functional MRI results. We also describe scale-free features (power law exponent) as well as distinct spectral features and reactivity to external stimulus in the tumoral and adjacent tissue of patients and provide novel insights in terms of glioma’s electrophysiology. The insights gained from these empirical studies further improve our understanding about the pathophysiology of this disease at micro- and macroscopic scales allowing us to envisage novel management methods for patients affected by glioma.

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL).

<p>    </p> <p>Over the last decade, measurement technology that records neural activity such as ECoG and Utah array has dramatically improved. These advancements have given researchers access to recordings from multiple neurons simultaneously. Efficient computational and statistical methods are required to analyze this data type successfully. The time-series model is one of the most common approaches for analyzing this data type. Unfortunately, even with all the advances made with time-series models, it is not always enough since these models often need massive amounts of data to achieve good results. This is especially true in the field of neuroscience, where the datasets are often limited, therefore imposing constraints on the type and complexity of the models we can use. Not only that, but the Signal-to- noise ratio tends to be lower than in other machine learning datasets. This paper will introduce different architectures and techniques to overcome constraints imposed by these small datasets. There are two major experiments that we will discuss. (1) We will strive to develop models for participants who lost the ability to speak by building upon the previous state-of-the-art model for decoding neural activity (ECoG data) into English text. (2) We will introduce two new models, RNNF and Neural RoBERTa. These new models impute missing neural data from neural recordings (Utah arrays) of monkeys performing kinematic tasks. These new models with the help of novel data augmentation techniques (dynamic masking) outperformed state-of-the-art models such as Neural Data Transformer (NDT) in the Neural Latents Benchmark competition. </p>

Une Interface Cerveau-Machine (ICM) est un système permettant de transformer l'activité neurale du cerveau en une commande d'effecteurs externes. Cette étude correspond à une étape vers une ICM totalement autonome fonctionnant dans un environnement naturel ce qui est d'une importance cruciale pour les futures applications cliniques d'une ICM. Pour représenter l'environnement naturel, des expériences avec une ICM binaire asynchrone ont été réalisées avec des animaux libres de se mouvoir. En comparaison avec les études précédentes, des expériences sur le long terme ont été réalisées, ce qui est plus conforme aux exigences des applications de la vie réelle. L'objectif principal de cette étude est de différencier le modèle spécifique neuronal lié à l'intention d'action de l'activité de fond du cerveau chez des animaux libres de tous mouvements. Pour atteindre le niveau nécessaire de sélectivité, l'analyse Multi-Voies PLS a été choisie sachant qu'elle fournit simultanément un traitement du signal dans plusieurs domaines, à savoir, temporel, fréquentiel et spatial. Pour améliorer la capacité de l'approche générique Multi-Voies PLS pour le traitement de données à grandes dimensions, l'algorithme « Itérative NPLS » est introduit dans notre travail. En ayant des besoins plus faibles en mémoire, cet algorithme fournit des traitements de grands ensembles de données, permet une résolution élevée, préserve l'exactitude de l'algorithme générique et démontre une meilleure robustesse. Pour la calibration adaptative d'un système ICM, l'algorithme récursif NPLS est proposé. Finalement, l'algorithme pénalisé NPLS est développé pour la sélection efficace d'un sous-ensemble de fonctions, à savoir, un sous-ensemble d'électrodes. Les algorithmes proposés ont été testés sur des ensembles de données artificielles et réelles. Ils ont démontré une performance qui est comparable à celle d'un algorithme générique NPLS. Leur efficacité de calcul est acceptable pour les applications en temps réel. Les algorithmes développés ont été appliqués à la calibration d'un système ICM et ont été utilisés dans des expériences d'ICM avec bouclage en temps réel chez des animaux. Enfin, les méthodes proposées représentent une approche prospective pour de futurs développements de systèmes ICM humains. / Brain Computer Interface (BCI) is a system for translation of brain neural activity into commands for external devices. This study was undertaken as a step toward the fully autonomous (self-paced) BCI functioning in natural environment which is of crucial importance for BCI clinical applications. To model the natural environment binary self-paced BCI experiments were carried out in freely moving animals. In comparison to the previous works, the long-term experimental sessions were carried out, which better comply with the real-life applications requirements. The main goal of the study was to discriminate the specific neuronal pattern related to the animal's control action against background brain activity of freely-moving animal. To achieve the necessary level of selectivity the Multi-Way Analysis was chosen since it provides a simultaneous signal processing in several domains, namely, temporal, frequency and spatial. To improve the capacity of the generic Multy-Way PLS approach for treatment of high-dimensional data, the Iterative NPLS algorithm is introduced in the current study. Having lower memory requirements it provides huge datasets treatment, allows high resolution, preserves the accuracy of the generic algorithm, and demonstrates better robustness. For adaptive calibration of BCI system the Recursive NPLS algorithm is proposed. Finally, the Penalized NPLS algorithm is developed for effective selection of feature subsets, namely, for subset of electrodes. The proposed algorithms were tested on artificial and real datasets. They demonstrated performance which either suppress or is comparable with one of the generic NPLS algorithm. Their computational efficiency is acceptable for the real-time applications. Developed algorithms were applied for calibration of the BCI system and were used in the real-time close-loop binary BCI experiments in animals. The proposed methods represent a prospective approach for further development of a human BCI system.

Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain–computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients.

Made available in DSpace on 2014-12-17T15:28:53Z (GMT). No. of bitstreams: 1 AndersonBS_DISSERT.pdf: 4240084 bytes, checksum: 0331343a1aab5e54d0d9cb6baeccb72d (MD5) Previous issue date: 2013-12-13 / Epilepsies are neurological disorders characterized by recurrent and spontaneous seizures due to an abnormal electric activity in a brain network. The mesial temporal lobe epilepsy (MTLE) is the most prevalent type of epilepsy in adulthood, and it occurs frequently in association with hippocampal sclerosis. Unfortunately, not all patients benefit from pharmacological treatment (drug-resistant patients), and therefore become candidates for surgery, a procedure of high complexity and cost. Nowadays, the most common surgery is the anterior temporal lobectomy with selective amygdalohippocampectomy, a procedure standardized by anatomical markers. However, part of patients still present seizure after the procedure. Then, to increase the efficiency of this kind of procedure, it is fundamental to know the epileptic human brain in order to create new tools for auxiliary an individualized surgery procedure. The aim of this work was to identify and quantify the occurrence of epilepticform activity -such as interictal spikes (IS) and high frequency oscillations (HFO) - in electrocorticographic (ECoG) signals acutely recorded during the surgery procedure in drug-resistant patients with MTLE. The ECoG recording (32 channels at sample rate of 1 kHz) was performed in the surface of temporal lobe in three moments: without any cortical resection, after anterior temporal lobectomy and after amygdalohippocampectomy (mean duration of each record: 10 min; N = 17 patients; ethic approval #1038/03 in Research Ethic Committee of Federal University of S?o Paulo). The occurrence of IS and HFO was quantified automatically by MATLAB routines and validated manually. The events rate (number of events/channels) in each recording time was correlated with seizure control outcome. In 8 hours and 40 minutes of record, we identified 36,858 IS and 1.756 HFO. We observed that seizure-free outcome patients had more HFO rate before the resection than non-seizure free, however do not differentiate in relation of frequency, morphology and distribution of IS. The HFO rate in the first record was better than IS rate on prediction of seizure-free patients (IS: AUC = 57%, Sens = 70%, Spec = 71% vs HFO: AUC = 77%, Sens = 100%, Spec = 70%). We observed the same for the difference of the rate of pre and post-resection (IS: AUC = 54%, Sens = 60%, Spec = 71%; vs HFO: AUC = 84%, Sens = 100%, Spec = 80%). In this case, the algorithm identifies all seizure-free patients (N = 7) with two false positives. To conclude, we observed that the IS and HFO can be found in intra-operative ECoG record, despite the anesthesia and the short time of record. The possibility to classify the patients before any cortical resection suggest that ECoG can be important to decide the use of adjuvant pharmacological treatment or to change for tailored resection procedure. The mechanism responsible for this effect is still unknown, thus more studies are necessary to clarify the processes related to it / As epilepsias s?o dist?rbios neurol?gicos caracterizados por crises espont?neas e recorrentes, resultantes de uma atividade el?trica anormal de uma rede neural. Dentre os diferentes tipos de epilepsia, a epilepsia mesial do lobo temporal (EMLT) ? a mais observada em adultos, sendo frequentemente associada ? esclerose hipocampal. Infelizmente, nem todos os pacientes s?o beneficiados pelo tratamento farmacol?gico (pacientes f?rmaco-resistentes). Para estes sujeitos, uma alternativa ? a realiza??o de cirurgia, um procedimento de alta complexidade e elevado custo. Atualmente, o procedimento mais realizado ? a lobectomia temporal anterior com amigdalo-hipocampectomia seletiva, uma cirurgia padronizada por marcos anat?micos. Entretanto, uma parcela dos pacientes continua a apresentar crises incapacitantes ap?s o tratamento cir?rgico. Desta forma, para aumentar a efici?ncia deste tipo de tratamento, ? fundamental a compreens?o do enc?falo humano epil?ptico com vistas a se criar ferramentas que auxiliem na realiza??o de procedimentos individualizados. O objetivo do presente trabalho foi identificar e quantificar a ocorr?ncia de atividade epileptiforme - esp?culas interictais (EI) e oscila??es de alta frequ?ncia (OAF) - em registros eletrocorticogr?ficos (ECoG) realizados durante procedimento cir?rgico em pacientes com EMLT refrat?ria ao tratamento farmacol?gico. Registros ECoG (32 canais a uma taxa de amostragem de 1 kHz) foram realizados na superf?cie do lobo temporal em 3 momentos cir?rgicos: no c?rtex intacto, ap?s lobectomia temporal anterior e ap?s amigdalo-hipocampectomia (dura??o m?dia de cada um desses registros: 10 min; N=17 pacientes). A ocorr?ncia de EI e OAF foi quantificada automatica-mente, por meio de rotinas em MATLAB, e validadas manualmente. A taxa de ocorr?ncia em cada um dos tempos cir?rgicos foi correlacionada com o resultado cir?rgico quanto ao controle das crises, num seguimento de 2 anos. De um total de 8 h e 40 min de registro, identificamos 36.858 EI e 1.756 OAF. Observamos que os pacientes que ficaram livres de crises no p?s-operat?rio apresentaram maior quanti-dade de OAF antes da cirurgia do que aqueles que continuaram a ter crises; por?m, n?o diferiram quanto a frequ?ncia, morfologia e distribui??o de EI. A ocorr?ncia de OAF no registro basal apresentou melhor desempenho que as EI na previs?o do controle total das crises no p?s-operat?rio (EI: AUC = 57%, S = 71% , E = 70% vs OAF: AUC = 77%, S = 100%, E=70%). O mesmo foi observado com a varia??o da ocorr?ncia entre os momentos pr?- e p?s-ressec??o (EI: AUC = 54%, S = 71%, E = 60% vs OAF: AUC = 84%, S = 100%, E = 80%). Nesse caso, o classificador foi capaz de identificar todos os pacientes livres de crises (N = 7) , apresentando apenas dois falsos positivos. Desta forma, podemos concluir que as OAF, juntamente com as EI, podem ser encontradas no registro ECoG intra-operat?rio, mesmo na presen?a de anest?sicos e em uma curta sess?o de registro. Al?m disso, a observa??o de que a ocorr?ncia desses eventos no in?cio da cirurgia permite classificar o paciente quanto ao progn?stico cir?rgico abre caminho para aplicar o ECoG intra-operat?rio, por exemplo, na decis?o sobre o uso de tratamento farmacol?gico adjuvante ou da convers?o para ressec??es individualizadas. No entanto, o mecanismo respons?vel por esse efeito ainda ? desconhecido, logo novos estudos s?o necess?rios para melhor esclarec?-lo

Epilepsy, as the most common chronic neurological disease, affects a significant part of population (0.5-1%). Drug resistant epilepsy has a significant negative effect on the quality of life, psychiatric comorbidities, neurocognitive performance and the risk of SUDEP in children. Therefore, resective epilepsy surgery, the only curative treatment of this condition, can fundamentally reverse this unfavorable prognosis. An inevitable prerequisite for a good postoperative result is complete removal of the epileptogenic zone (EC) and preservation of eloquent areas (EC). At present, even with improving and new preoperative non-invasive methods, we don't have an exclusive diagnostic method for theirs delineation. The aim of this PhD study is to assess benefit of individual intraoperative electrophysiological (iEF) methods in pediatric patients with focal intractable epilepsy. The first study evaluates the importance of intraoperative electrocorticography (iECoG) in the localization of EZ. The study proved that iECoG serves as a reliable tool to guide surgical resection and may predict results of epilepsy surgery. iECoG-based modification of surgical plan is not associated with increased risk of significant complications. The second presented study analyzed the contribution of intraoperative electrical...