Cortical Dynamics of Language

The human capability for fluent speech profoundly directs inter-personal communication and, by extension, self-expression. Language is lost in millions of people each year due to trauma, stroke, neurodegeneration, and neoplasms with devastating impact to social interaction and quality of life. The following investigations were designed to elucidate the neurobiological foundation of speech production, building towards a universal cognitive model of language in the brain. Understanding the dynamical mechanisms supporting cortical network behavior will significantly advance the understanding of how both focal and disconnection injuries yield neurological deficits, informing the development of therapeutic approaches

Determination and evaluation of clinically efficient stopping criteria for the multiple auditory steady-state response technique

Background: Although the auditory steady-state response (ASSR) technique utilizes objective statistical detection algorithms to estimate behavioural hearing thresholds, the audiologist still has to decide when to terminate ASSR recordings introducing once more a certain degree of subjectivity. Aims: The present study aimed at establishing clinically efficient stopping criteria for a multiple 80-Hz ASSR system. Methods: In Experiment 1, data of 31 normal hearing subjects were analyzed off-line to propose stopping rules. Consequently, ASSR recordings will be stopped when (1) all 8 responses reach significance and significance can be maintained for 8 consecutive sweeps; (2) the mean noise levels were ≤ 4 nV (if at this “≤ 4-nV” criterion, p-values were between 0.05 and 0.1, measurements were extended only once by 8 sweeps); and (3) a maximum amount of 48 sweeps was attained. In Experiment 2, these stopping criteria were applied on 10 normal hearing and 10 hearing-impaired adults to asses the efficiency. Results: The application of these stopping rules resulted in ASSR threshold values that were comparable to other multiple-ASSR research with normal hearing and hearing-impaired adults. Furthermore, in 80% of the cases, ASSR thresholds could be obtained within a time-frame of 1 hour. Investigating the significant response-amplitudes of the hearing-impaired adults through cumulative curves indicated that probably a higher noise-stop criterion than “≤ 4 nV” can be used. Conclusions: The proposed stopping rules can be used in adults to determine accurate ASSR thresholds within an acceptable time-frame of about 1 hour. However, additional research with infants and adults with varying degrees and configurations of hearing loss is needed to optimize these criteria

On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology

Introduction Subtle inhomogeneities in the scanner’s magnetic fields (B0 and B1) alter the intensity levels of the structural magnetic resonance imaging (MRI) affecting the volumetric assessment of WMH changes. Here, we investigate the influence that (1) correcting the images for the B1 inhomogeneities (i.e. bias field correction (BFC)) and (2) selection of the WMH change assessment method can have on longitudinal analyses of WMH progression and discuss possible solutions. Methods We used brain structural MRI from 46 mild stroke patients scanned at stroke onset and 3 years later. We tested three BFC approaches: FSL-FAST, N4 and exponentially entropy-driven homomorphic unsharp masking (E2D-HUM) and analysed their effect on the measured WMH change. Separately, we tested two methods to assess WMH changes: measuring WMH volumes independently at both time points semi-automatically (MCMxxxVI) and subtracting intensity-normalised FLAIR images at both time points following image gamma correction. We then combined the BFC with the computational method that performed best across the whole sample to assess WMH changes. Results Analysis of the difference in the variance-to-mean intensity ratio in normal tissue between BFC and uncorrected images and visual inspection showed that all BFC methods altered the WMH appearance and distribution, but FSL-FAST in general performed more consistently across the sample and MRI modalities. The WMH volume change over 3 years obtained with MCMxxxVI with vs. without FSL-FAST BFC did not significantly differ (medians(IQR)(with BFC) = 3.2(6.3) vs. 2.9(7.4)ml (without BFC), p = 0.5), but both differed significantly from the WMH volume change obtained from subtracting post-processed FLAIR images (without BFC)(7.6(8.2)ml, p < 0.001). This latter method considerably inflated the WMH volume change as subtle WMH at baseline that became more intense at follow-up were counted as increase in the volumetric change. Conclusions Measurement of WMH volume change remains challenging. Although the overall volumetric change was not significantly affected by the application of BFC, these methods distorted the image intensity distribution affecting subtle WMH. Subtracting the FLAIR images at both time points following gamma correction seems a promising technique but is adversely affected by subtle WMH. It is important to take into account not only the changes in volume but also in the signal intensity

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

The Developmental Trajectory of Contour Integration in Autism Spectrum Disorders

Sensory input is inherently ambiguous and complex, so perception is believed to be achieved by combining incoming sensory information with prior knowledge. One model envisions the grouping of sensory features (the local dimensions of stimuli) to be the outcome of a predictive process relying on prior experience (the global dimension of stimuli) to disambiguate possible configurations those elements could take. Contour integration, the linking of aligned but separate visual elements, is one example of perceptual grouping. Kanizsa-type illusory contour (IC) stimuli have been widely used to explore contour integration processing. Consisting of two conditions which differ only in the alignment of their inducing elements, one induces the experience of a shape apparently defined by a contour and the second does not. This contour has no counterpart in actual visual space – it is the visual system that fills-in the gap between inducing elements. A well-tested electrophysiological index associated with this process (the IC-effect) provided us with a metric of the visual system’s contribution to contour integration. Using visually evoked potentials (VEP), we began by probing the limits of this metric to three manipulations of contour parameters previously shown to impact subjective experience of illusion strength. Next we detailed the developmental trajectory of contour integration processes over childhood and adolescence. Finally, because persons with autism spectrum disorders (ASDs) have demonstrated an altered balance of global and local processing, we hypothesized that contour integration may be atypical. We compared typical development to development in persons with ASDs to reveal possible mechanisms underlying this processing difference. Our manipulations resulted in no differences in the strength of the IC-effect in adults or children in either group. However, timing of the IC-effect was delayed in two instances: 1) peak latency was delayed by increasing the extent of contour to be filled-in relative to overall IC size and 2) onset latency was delayed in participants with ASDs relative to their neurotypical counterparts

Exploring memory impairment and post-traumatic amnesia following traumatic brain injury

Memory disturbances are among the most common and significant consequences of traumatic brain injury (TBI). The severity of these deficits can vary widely across the trajectory of recovery from TBI and can be highly heterogenous across individuals. In the acute stages memory disturbance can occur in the form of post-traumatic amnesia (PTA), but deficits are also present into the chronic stages of recovery. I present four studies that aim to understand the characteristics and underlying mechanisms of memory impairment following TBI. I investigated the cognitive profile of acute TBI patients with and without PTA. I found PTA patients show a transient deficit in working memory binding. I then assessed electrophysiological abnormalities to test the hypothesis that the binding deficit is underpinned by pathological low frequency slow-wave activity. PTA patients showed a significantly higher delta to alpha power ratio that correlated with binding impairment. To understand how this disruption to cortical communication impacts upon large-scale networks I performed a dynamic functional connectivity analysis on the resting state fMRI of acute TBI patients. I found four independent brain states that showed striking anti-correlation between core cognitive control networks. Patients in a more profound period of PTA spent more time in fewer states than those with less cognitive impairment. These findings suggest that PTA is likely underpinned by disruption to communication required for integration of features in working memory. Finally, I examined enduring memory failures in chronic TBI patients and found that patients with episodic memory impairment showed differential activation of key networks required for memory and attention. Memory impairment related to the white matter integrity directly underpinning the task-derived encoding networks. These findings suggest that in chronic TBI memory impairment may be associated with failed control of attentional resources.Open Acces

Semantic radical consistency and character transparency effects in Chinese: an ERP study

BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin

Neurobiological Foundations Of Stability And Flexibility

In order to adapt to changing and uncertain environments, humans and other organisms must balance stability and flexibility in learning and behavior. Stability is necessary to learn environmental regularities and support ongoing behavior, while flexibility is necessary when beliefs need to be revised or behavioral strategies need to be changed. Adjusting the balance between stability and flexibility must often be based on endogenously generated decisions that are informed by information from the environment but not dictated explicitly. This dissertation examines the neurobiological bases of such endogenous flexibility, focusing in particular on the role of prefrontally-mediated cognitive control processes and the neuromodulatory actions of dopaminergic and noradrenergic systems. In the first study (Chapter 2), we examined the role of frontostriatal circuits in instructed reinforcement learning. In this paradigm, inaccurate instructions are given prior to trial-and-error learning, leading to bias in learning and choice. Abandoning the instructions thus necessitates flexibility. We utilized transcranial direct current stimulation over dorsolateral prefrontal cortex to try to establish a causal role for this area in this bias. We also assayed two genes, the COMT Val158Met genetic polymorphism and the DAT1/SLC6A3 variable number tandem repeat, which affect prefrontal and striatal dopamine, respectively. The results support the role of prefrontal cortex in biasing learning, and provide further evidence that individual differences in the balance between prefrontal and striatal dopamine may be particularly important in the tradeoff between stability and flexibility. In the second study (Chapter 3), we assess the neurobiological mechanisms of stability and flexibility in the context of exploration, utilizing fMRI to examine dynamic changes in functional brain networks associated with exploratory choices. We then relate those changes to changes in norepinephrine activity, as measured indirectly via pupil diameter. We find tentative support for the hypothesis that increased norepinephrine activity around exploration facilitates the reorganization of functional brain networks, potentially providing a substrate for flexible exploratory states. Together, this work provides further support for the framework that stability and flexibility entail both costs and benefits, and that optimizing the balance between the two involves interactions of learning and cognitive control systems under the influence of catecholamines

Implantable Micro-Device for Epilepsy Seizure Detection and Subsequent Treatment

RÉSUMÉ L’émergence des micro-dispositifs implantables est une voie prometteuse pour le traitement de troubles neurologiques. Ces systèmes biomédicaux ont été exploités comme traitements non-conventionnels sur des patients chez qui les remèdes habituels sont inefficaces. Les récents progrès qui ont été faits sur les interfaces neuronales directes ont permis aux chercheurs d’analyser l’activité EEG intracérébrale (icEEG) en temps réel pour des fins de traitements. Cette thèse présente un dispositif implantable à base de microsystèmes pouvant capter efficacement des signaux neuronaux, détecter des crises d’épilepsie et y apporter un traitement afin de l’arrêter. Les contributions principales présentées ici ont été rapportées dans cinq articles scientifiques, publiés ou acceptés pour publication dans les revues IEEE, et plusieurs autres tels que «Low Power Electronics» et «Emerging Technologies in Computing». Le microsystème proposé inclus un circuit intégré (CI) à faible consommation énergétique permettant la détection de crises d’épilepsie en temps réel. Cet CI comporte une pré-amplification initiale et un détecteur de crises d’épilepsie. Le pré-amplificateur est constitué d’une nouvelle topologie de stabilisateur d’hacheur réduisant le bruit et la puissance dissipée. Les CI fabriqués ont été testés sur des enregistrements d’icEEG provenant de sept patients épileptiques réfractaires au traitement antiépileptique. Le délai moyen de la détection d’une crise est de 13,5 secondes, soit avant le début des manifestations cliniques évidentes. La consommation totale d’énergie mesurée de cette puce est de 51 μW. Un neurostimulateur à boucle fermée (NSBF), quant à lui, détecte automatiquement les crises en se basant sur les signaux icEEG captés par des électrodes intracrâniennes et permet une rétroaction par une stimulation électrique au même endroit afin d’interrompre ces crises. La puce de détection de crises et le stimulateur électrique à base sur FPGA ont été assemblés à des électrodes afin de compléter la prothèse proposée. Ce NSBF a été validé en utilisant des enregistrements d’icEEG de dix patients souffrant d’épilepsie réfractaire. Les résultats révèlent une performance excellente pour la détection précoce de crises et pour l’auto-déclenchement subséquent d’une stimulation électrique. La consommation énergétique totale du NSBF est de 16 mW. Une autre alternative à la stimulation électrique est l’injection locale de médicaments, un traitement prometteur de l’épilepsie. Un système local de livraison de médicament basé sur un nouveau détecteur asynchrone des crises est présenté.----------ABSTRACT Emerging implantable microdevices hold great promise for the treatment of patients with neurological conditions. These biomedical systems have been exploited as unconventional treatment for the conventionally untreatable patients. Recent progress in brain-machine-interface activities has led the researchers to analyze the intracerebral EEG (icEEG) recording in real-time and deliver subsequent treatments. We present in this thesis a long-term safe and reliable low-power microsystem-based implantable device to perform efficient neural signal recording, seizure detection and subsequent treatment for epilepsy. The main contributions presented in this thesis are reported in five journal manuscripts, published or accepted for publication in IEEE Journals, and many others such as Low Power Electronics, and Emerging Technologies in Computing. The proposed microsystem includes a low-power integrated circuit (IC) intended for real-time epileptic seizure detection. This IC integrates a front-end preamplifier and epileptic seizure detector. The preamplifier is based on a new chopper stabilizer topology that reduces noise and power dissipation. The fabricated IC was tested using icEEG recordings from seven patients with drug-resistant epilepsy. The average seizure detection delay was 13.5 sec, well before the onset of clinical manifestations. The measured total power consumption of this chip is 51 µW. A closed-loop neurostimulator (CLNS) is next introduced, which is dedicated to automatically detect seizure based on icEEG recordings from intracranial electrode contacts and provide an electrical stimulation feedback to the same contacts in order to disrupt these seizures. The seizure detector chip and a dedicated FPGA-based electrical stimulator were assembled together with common recording electrodes to complete the proposed prosthesis. This CLNS was validated offline using recording from ten patients with refractory epilepsy, and showed excellent performance for early detection of seizures and subsequent self-triggering electrical stimulation. Total power consumption of the CLNS is 16 mW. Alternatively, focal drug injection is the promising treatment for epilepsy. A responsive focal drug delivery system based on a new asynchronous seizure detector is also presented. The later system with data-dependent computation reduces up to 49% power consumption compared to the previous synchronous neurostimulator