Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools

Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

IntroductionMajor depressive disorder (MDD) is the most common mental disorder worldwide, leading to impairment in quality and independence of life. Electroencephalography (EEG) biomarkers processed with machine learning (ML) algorithms have been explored for objective diagnoses with promising results. However, the generalizability of those models, a prerequisite for clinical application, is restricted by small datasets. One approach to train ML models with good generalizability is complementing the original with synthetic data produced by generative algorithms. Another advantage of synthetic data is the possibility of publishing the data for other researchers without risking patient data privacy. Synthetic EEG time-series have not yet been generated for two clinical populations like MDD patients and healthy controls.MethodsWe first reviewed 27 studies presenting EEG data augmentation with generative algorithms for classification tasks, like diagnosis, for the possibilities and shortcomings of recent methods. The subsequent empirical study generated EEG time-series based on two public datasets with 30/28 and 24/29 subjects (MDD/controls). To obtain baseline diagnostic accuracies, convolutional neural networks (CNN) were trained with time-series from each dataset. The data were synthesized with generative adversarial networks (GAN) consisting of CNNs. We evaluated the synthetic data qualitatively and quantitatively and finally used it for re-training the diagnostic model.ResultsThe reviewed studies improved their classification accuracies by between 1 and 40% with the synthetic data. Our own diagnostic accuracy improved up to 10% for one dataset but not significantly for the other. We found a rich repertoire of generative models in the reviewed literature, solving various technical issues. A major shortcoming in the field is the lack of meaningful evaluation metrics for synthetic data. The few studies analyzing the data in the frequency domain, including our own, show that only some features can be produced truthfully.DiscussionThe systematic review combined with our own investigation provides an overview of the available methods for generating EEG data for a classification task, their possibilities, and shortcomings. The approach is promising and the technical basis is set. For a broad application of these techniques in neuroscience research or clinical application, the methods need fine-tuning facilitated by domain expertise in (clinical) EEG research

Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application

EFFECTS OF NEUROMODULATION ON NEUROVASCULAR COUPLING

The communication between neurons within neural circuits relies on neurotransmitters (glutamate, γ-aminobutyric acid (GABA)) and neuromodulators (acetylcholine, dopamine, serotonin, etc.). However, despite sharing similar molecular elements, neurotransmitters and neuromodulators are distinct classes of molecules and mediate different aspects of neural activity and metabolism. Neurotransmitters on one hand are responsible for synaptic signal transmission (classical transmission) while neuromodulators exert their functions by mediating different postsynaptic events that result in changes to the balance between excitation and inhibition. Neuromodulation, while essential to nervous system function, has been significantly more difficult to study than neurotransmission. This is principally due to the fact that effects elicited by neuromodulators are usually of slow onset, long lasting, and are not simply excitation or inhibition. In contrast to the effects of neurotransmitters, neuromodulators enable neurons to be more flexible in their ability to encode different sorts of information (e.g. sensory information) on a variety of time scales. However, it is important to appreciate that one of the challenges in the study of neuromodulation is to understand the extent to which neuromodulators’ actions are coordinated at all levels of brain function. That is, from the cellular and metabolic level to network and cognitive control. Therefore, understanding the molecules that mediate brain networks interactions is essential to understanding the brain dynamic, and also helps to put the cellular and molecular processes in perspective. Functional magnetic resonance imaging (fMRI) is a technique that allows access to various cellular and metabolic aspects of network communication that are difficult to access when studying one neuron at the time. Its non-invasiveness nature allows the comparison of data and hypotheses of the primate brain to that of the human brain. Hence, understanding the effects of neuromodulation on local microcircuits is needed. Furthermore, given the massive projections of the neuromodulatory diffuse ascending systems, fMRI combined with pharmacological and neurophysiological methods may provide true insight into their organization and dynamics. However, little is known about how to interpret the effects of neuromodulation in fMRI and neurophysiological data, for instance, how to disentangle blood oxygenation level dependent (BOLD) signal changes relating to cognitive changes (presumably neuromodulatory influences) from stimulus-driven or perceptual effects. The purpose of this dissertation is to understand the causal relationship between neural activity and hemodynamic responses under the influence of neuromodulation. To this end we present the results of six studies. In the first study, we aimed to establish a mass-spectrometry-based technique to uncover the distribution of different metabolites, neurotransmitters and neuromodulators in the macaque brain. We simultaneously measured the concentrations of these biomolecules in brain and in blood. In a second study, we developed a multimodal approach consisting of fMRI (BOLD and cerebral blood flow or CBF), electrophysiological recording with a laminar probe and pharmacology to assess the effects of neuromodulation on neurovascular coupling. We developed a pharmacological injection delivery system using pressure-operated pumps to reliably apply drugs either systemically or intracortically in the NMR scanner. In our third study, we systemically injected lactate and pyruvate to explore whether the plasma concentration of either of these metabolites affects the BOLD responses. This is important given that both metabolites are in a metabolic equilibrium; if this equilibrium is disrupted, changes in the NAD and NADH concentrations would elicit changes in the CBF. In a fourth study, we explored the influence of dopaminergic (DAergic) neuromodulation in the BOLD, CBF and neurophysiological activity. Here we found that DAergic neuromodulation dissociated the BOLD responses from the underlying neural activity. Interestingly, the changes in the neural activity were tightly coupled to the effects seen in the CBF responses. In a subsequent study, we explored whether the effects of dopamine (DA) on the electrophysiological responses are cortical layer dependent and whether specific patterns of neural activity can be used to infer the effects of neuromodulation on the neural activity. This is important, given that different types of neural activity provide independent information about the amplitude and dynamics from BOLD responses, and studies have shown that these bands originate from different cortical layers. What this study revealed, is that local field potentials (LFPs) in the midrange frequencies can indeed provide indications about the sustained effects of neuromodulation on cortical sensory processing. Given the results from the previous study, in our sixth study, we aimed at understanding how different cortical layers may process incoming and outgoing information in the different LFP bands. These findings provide evidence that neuromodulation has profound effects on neurovascular coupling. By changing the excitation-inhibition balance of neural circuits, neuromodulators not only mediate the neural activity, but also adjust the metabolic demands. Therefore, understanding how the different types of neuromodulators affect the BOLD response is essential for an effective interpretation of fMRI-data, not only in tasks involving attentional and reward-related processes, but also for future diagnostic use of fMRI, since many psychiatric disorders are the result of alterations in neuromodulatory systems.Die Kommunikation zwischen den Neuronen innerhalb neuronalen Schaltkreise beruht auf Neurotransmitter (Glutamat, γ-Aminobuttersäure (GABA)) und Neuromodulatoren (Acetylcholin, Dopamin, Serotonin, etc.). Neurotransmitter und Neuromodulatoren sind jedoch unterschiedliche Klassen von Molekülen und verschiedenen Aspekte der neuronalen Aktivität und den Stoffwechsel vermitteln. Neurotransmitters sind einerseits verantwortlich für die synaptische Signalübertragung (klassische Übertragung), während ihre Funktionen ausüben, Neuromodulatoren durch verschiedene postsynaptischen Ereignisse zu vermitteln, die in Änderungen an der Balance zwischen Erregung und Hemmung führen. Neuromodulation , während wesentlich Funktion des Nervensystems hat sich als Neurotransmission wesentlich schwieriger gewesen, zu studieren. Dies ist hauptsächlich auf die Tatsache zurückzuführen, die durch Neuromodulatoren sind in der Regel von langsamen Beginn, langlebig, und sind nicht einfach Anregung oder Hemmung ausgelöst beeinflusst. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Jedoch ist es wichtig, dass eine der Herausforderungen bei der Untersuchung von Neuromodulations zu schätzen ist, das Ausmaß, in dem Neuromodulatoren Aktionen koordiniert sind auf allen Ebenen der Gehirnfunktion zu verstehen. Das heißt, von der zellulären und metabolischen Ebene zu vernetzen und kognitive Kontrolle. Daher die Moleküle zu verstehen, die Gehirn Netzwerke Interaktionen vermitteln ist wesentlich für das Verständnis des Gehirns dynamisch, und hilft auch, die zellulären und molekularen Prozesse in Perspektive zu setzen. Funktionellen Kernspintomographie (fMRI) ist eine Technik, die Zugang zu verschiedenen zellulären und metabolischen Aspekte der Netzwerk-Kommunikation ermöglicht, die schwer zugänglich sind, wenn zu der Zeit eines Neurons zu studieren. Seine nicht-Invasivität Natur ermöglicht den Vergleich von Daten und Hypothesen des Primatengehirn zu der des menschlichen Gehirns. Somit wurde das Verständnis der Auswirkungen der Neuromodulation auf lokale Mikro benötigt. Darüber hinaus sind die massiven Projektionen der neuromodulatorischen diffuse Aufstiegsanlagen gegeben, kombiniert fMRI mit pharmakologischen und neurophysiologischen Methoden wahren Einblick in ihre Organisation und Dynamik liefern. Allerdings ist nur wenig darüber bekannt, wie die Auswirkungen der Neuromodulations in fMRI und neurophysiologische Daten zu interpretieren, zum Beispiel, wie Blutoxydation pegelabhängig (BOLD) Signaländerungen in Bezug auf kognitive Veränderungen (vermutlich neuromodulatorischen Einflüsse) von Stimulus-driven oder Wahrnehmungseffekte zu entwirren. Der Zweck dieser Arbeit ist es, die kausale Beziehung zwischen neuronaler Aktivität und hämodynamischen Reaktionen unter dem Einfluss der Neuromodulations zu verstehen. Zu diesem Zweck stellen wir die Ergebnisse von sechs Studien. In der ersten Studie wollten wir eine auf Massenspektrometrie basierende Technik einzurichten, um die Verteilung von verschiedenen Metaboliten, Neurotransmittern und Neuromodulatoren in Makakengehirn aufzudeckenWir maßen gleichzeitig die Konzentrationen dieser Biomoleküle im Gehirn und im Blut. In einer zweiten Studie entwickelten wir einen multimodalen Ansatz, bestehend aus fMRI (BOLD und zerebralen Blutflusses oder CBF), elektrophysiologische Aufzeichnung mit einer laminaren Sonde und Pharmakologie, die Auswirkungen der Neuromodulation auf neurovaskulären Kopplung zu beurteilen. Wir entwickelten eine pharmakologische Injektionsverabreichungssystem druckbetriebenen Pumpen mit zuverlässiger Medikamente gelten entweder systemisch oder intrakortikale im NMR-Scanner. In unserer dritten Studie injizierten wir systemisch Laktat und Pyruvat zu untersuchen, ob die Plasmakonzentration von entweder dieser Metaboliten die BOLD-Antworten beeinflusst. Dies ist wichtig, dass beide gegeben Metaboliten in einem Stoffwechselgleichgewicht sind; wenn dieses Gleichgewicht gestört ist, Veränderungen in den NAD und NADH-Konzentrationen würden Veränderungen in der CBF entlocken. In einer vierten Studie untersuchten wir den Einfluss von dopaminergen (DA-erge) -Neuromodulation im BOLD, CBF und neurophysiologische Aktivität. Hier fanden wir, dass DAerge -Neuromodulation die BOLD-Antworten von der zugrunde liegenden neuronalen Aktivität distanzierte. Interessanterweise waren verbunden, um die Veränderungen in der neuronalen Aktivität eng auf die in den CBF Reaktionen gesehen Wirkungen. In einer nachfolgenden Studie untersuchten wir, ob die Wirkungen von Dopamin (DA) auf die elektrophysiologischen Reaktionen sind Rindenschicht abhängig, und ob bestimmte Muster der neuronalen Aktivität verwendet werden kann, die Wirkungen von Neuromodulations auf die neurale Aktivität zu schließen. Dies ist wichtig, da verschiedene Arten von neuralen Aktivität liefern unabhängige Informationen über die Amplitude und die Dynamik von BOLD-Antworten, und Studien haben gezeigt, dass diese Bands aus verschiedenen kortikalen Schichten stammen. Was diese Studie ergab, dass lokale Feldpotentiale (LFP) in den mittleren Frequenzen in der Tat Hinweise über die nachhaltige Wirkung der Neuromodulation auf die kortikale sensorische Verarbeitung zur Verfügung stellen kann. In Anbetracht der Ergebnisse der früheren Studie, in unserer sechsten Studie wollten wir auf das Verständnis, wie die verschiedenen kortikalen Schichten verarbeiten kann ein- und ausgehenden Informationen in den verschiedenen LFP-Bands. Diese Ergebnisse belegen, dass -Neuromodulation profunde Auswirkungen auf die neurovaskulären Kopplung hat. Durch die Veränderung der Erregungs Hemmung Gleichgewicht neuronaler Schaltkreise vermitteln Neuromodulatoren nicht nur die neurale Aktivität, sondern auch die metabolischen Anforderungen anzupassen. Daher verstehen, wie die verschiedenen Arten von Neuromodulatoren beeinflussen die BOLD-Antwort für eine effektive Interpretation von fMRI-Daten notwendig ist, nicht nur in Aufgaben attentional und Belohnung bezogenen Prozessen mit, sondern auch für zukünftige diagnostische Verwendung von fMRI, da viele psychiatrische Störungen sind das Ergebnis von Veränderungen in neuromodulatorischen Systemen.La comunicación de las neuronas en los circuitos neuronales depende de los neurotransmisores (glutamato, acido γ-amino-butírico o GABA) y los neuromoduladores (acetilcolina, dopamina, serotonina, etc.). Sin embargo, tanto neurotransmisores como neuromoduladores son diferentes clases de moléculas y median diferentes aspectos de la actividad neuronal y del metabolismo, a pesar de compartir elementos moleculares muy similares. Los neurotransmisores, por una lado, son responsables de la transmisión sináptica de la información mientras que los neuromoduladores median diferentes eventos pos-sinápticos que resultan en cambios en el balance de la excitación e inhibición. La influencia de la neuromodulación es esencial para la función del sistema nerviosos, sin embargo es más difícil de estudiar que neurotransmisión. Esto se debe a que los efectos de los neuromoduladores suelen ser de un inicio lento, de larga duración, y no reflejan excitación o inhibición. En contraste a los efectos de los neurotransmisores, los neuromoduladores permiten que las neuronas sean más flexibles en su habilidad de codificar diferentes tipos de información (por ejemplo, información sensorial) en varias escalas temporales. Sin embargo, es importante darse cuenta que uno de objetivos primordiales en el estudio de neuromodulación es el de entender el grado en que la acción de los neuromoduladores está coordinada a todos los niveles de la función cerebral. Es decir, desde los aspectos celulares y metabólicos hasta los niveles de redes neuronales y control cognitivo. Por lo tanto, comprender los forma en la que diferentes moléculas median la interacción entre redes neuronal es esencial para el entendimiento de la dinámica cerebral, y también nos ayudara a comprender los procesos celulares y moleculares asociados a la percepción. La resonancia magnética funcional (fMRI, por sus siglas en inglés) es una técnica que permite acceder a varios aspectos celulares y metabólicos de la comunicación entre redes neuronales que suele ser de difícil acceso. Al mismo tiempo y debido que la fMRI es de naturaleza no invasiva, también permite comparar resultados e hipótesis entre humanos y primates. Por lo tanto, entender los efectos de la neuromodulación en la actividad de los circuitos neuronales es de alta relevancia. Dado que las proyecciones anatómicas de los sistemas de neuromoduladores, el uso de fMRI en combinación con farmacología y neurofisiología puede incrementar nuestro conocimiento sobre la estructura y dinámica de los sistemas de neuromoduladores. Sin embargo, poco se sabe sobre cómo interpretar los efectos de neuromodulation usando fMRI y neurofisiología, por ejemplo, como diferenciar los cambios en la señal BOLD que están relacionados a diferentes estados cognitivos (presumiblemente reflejando la influencia de neuromodulation). El propósito de esta disertación es la de comprender la relación causal que existe entre la actividad neural y la respuesta hemodinámica bajo la influencia de neuromodulación. Para tal fin presentamos los resultados de seis estudios que fueron producto de esta disertacion. En el primer estudio, desarrollamos una técnica basada en espectrometría de masa para detectar y medir la concentración de diferente metabolitos, neurotransmisores y neuromoduladores en el cerebro de primates. Dicha cuantificación se desarrollo simultáneamente tanto in sangre y cerebro. En un segundo estudio, utilizamos varias técnicas de fMRI (BOLD y flujo cerebral sanguíneo, CBF por sus siglas en ingles), registros electrofisiológicos con electrodos laminares y farmacología para acceder a los efectos de neuromodulation en el acople neurovascular. Para este fin, desarrollamos un sistema de inyecciones, basada en cambios de presión, para aplicar substancias sistémicamente o intracorticalmente dentro de un escáner de resonancia magnética. En nuestro tercer estudio, comparamos los efectos de lactato y piruvato para explorar como el desequilibrio metabólico de estas dos substancias afecta la respuesta BOLD. Esto es de gran importancia ya que ambas substancias metabólicas usualmente están en equilibrio. Sin embargo, cuando dicho equilibrio es interrumpido, los procesos metabólicos que acontecen en la mitocondria afectan las concentraciones de NAD y NADH causado cambios en el CBF. En un cuarto estudio, exploramos los efectos de las modulación dopaminergica (DAergic) en las señales BOLD, CBF y en la actividad neuronal. Encontramos que la modulación DAergic disocia las respuesta BOLD de la respuesta neuronal. Interesalmente, los cambios que observamos en la actividad de las neuronas estaba altamente acoplados a los efectos que observamos en la señal de CBF. En un estudio subsecuente, exploramos si los efectos de dopamina en la actividad neuronal es diferentes en las distintas capas de la corteza cerebral. Al mismo tiempo y ya que los neuromoduladores afectan la actividad de circuitos neuronales, exploramos si dichos efectos pueden usados como marcadores de la influencia de la neuromodulación . Esto es importante, ya que diferentes tipos de actividad neuronal brinda información sobre la amplitud y dinámica de la repuesta BOLD, y estudies han demostrado que estas bandas se originan de diferentes capas cortical. Este estudio revelo, que los potenciales de capo (LFPs, por sus siglas en ingles) en frecuencias intermedias puede ser indicativos sobre los efectos de neuromodulation en el procesamiento cortical. Dado los resultados en el estudio previo, en un sexto estudio, nos enfocamos a entender que tan diferentes las capas de la corteza procesan información entrante y saliente en diferentes frecuencias de los LFPs. Estos descubrimientos demuestran que los efectos de los neuromoduladores tiene una fuerte influencia en el acople neurovascular. Los neuromoduladores cambian el balance de excitación e inhibición de los circuitos neuronal, pero también median las demandas metabólicas. De esta manera, entender cómo interpretar los efectos de los neuromoduladores en la respuesta BOLD es esencial para una interpretación veraz y efectiva de los datos generados con fMRI. Estos resultados, no solo nos permiten comprender los procesos que están relacionados a la atención o de varios procesos cognitivos, sino que a su vez, nos permite comprender la señal de fMRI para su futuro uso en la medicina diagnostica, ya que muchas enfermedades psiquiátricas están asociadas a trastornos en el sistemas neuromoduladores

Functional Magnetic Resonance Imaging

"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states

Computational explorations of semantic cognition

Motivated by the widespread use of distributional models of semantics within the cognitive science community, we follow a computational modelling approach in order to better understand and expand the applicability of such models, as well as to test potential ways in which they can be improved and extended. We review evidence in favour of the assumption that distributional models capture important aspects of semantic cognition. We look at the models’ ability to account for behavioural data and fMRI patterns of brain activity, and investigate the structure of model-based, semantic networks. We test whether introducing affective information, obtained from a neural network model designed to predict emojis from co-occurring text, can improve the performance of linguistic and linguistic-visual models of semantics, in accounting for similarity/relatedness ratings. We find that adding visual and affective representations improves performance, especially for concrete and abstract words, respectively. We describe a processing model based on distributional semantics, in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can account for response time and accuracies in lexical and semantic decision tasks, as well as for concreteness/imageability and similarity/relatedness ratings. We evaluate the differences between concrete and abstract words, in terms of the structure of the semantic networks derived from distributional models of semantics. We examine how the structure is related to a number of factors that have been argued to differ between concrete and abstract words, namely imageability, age of acquisition, hedonic valence, contextual diversity, and semantic diversity. We use distributional models to explore factors that might be responsible for the poor linguistic performance of children suffering from Developmental Language Disorder. Based on the assumption that certain model parameters can be given a psychological interpretation, we start from “healthy” models, and generate “lesioned” models, by manipulating the parameters. This allows us to determine the importance of each factor, and their effects with respect to learning concrete vs abstract words

Molecular mechanisms of PAH function in response to phenylalanine and tetrahydrobiopterin binding

Phenylketonuria (PKU) is an autosomal recessive inborn error of metabolism (IEM) caused by mutations in the phenylalanine hydroxylase (PAH) gene. The molecular mechanism underlying deficiency of the PAH protein is, in most of the cases, loss of function due to protein misfolding. PAH mutations induce disturbed oligomerisation, decreased stability and accelerated degradation of hepatic PAH, a key enzyme in phenylalanine metabolism. Since the development of a phenylalanine-restricted diet in the 1950ies, PKU is a prototype for treatable inherited diseases. About 60 years later, the natural PAH cofactor tetrahydrobiopterin (BH4) was shown to act as a pharmacological chaperone stabilising the misfolded PAH protein. In consequence, BH4 (KUVAN®) was introduced to the pharmaceutical market as an alternative treatment for BH4-responsive PAH deficiency. Therefore, PKU is also regarded as a prototype for a pharmacologically treatable protein misfolding disease. Despite the progress in PKU therapy, knowledge on the molecular basis of PKU and the BH4 mode of action was still incomplete. Biochemical and biophysical characterisation of purified variant PAH proteins, which were derived from patient’s mutations, aimed at a better understanding of the molecular mechanisms of PAH loss of function. We showed that local side-chain replacements induce global conformational changes with negative impact on molecular motions that are essential for physiological enzyme function. The development of a continuous real-time fluorescence-based assay of PAH activity allowed for robust analysis of steady state kinetics and allosteric behaviour of recombinantly expressed PAH proteins. We identified positive cooperativity of the PAH enzyme towards BH4, where cooperativity does not rely on the presence of phenylalanine but is determined by activating conformational rearrangements. In vivo investigations on the mode-of-action of BH4 revealed differences in pharmacodynamics but not in pharmacokinetics between different strains of PAH-deficient mice (wild-type, Pahenu1/1 and Pahenu1/2). These observations pointed to a significant impact of the genotype on responsiveness to BH4. The available database information on PAH function associated with PAH mutations was based on non-standardised enzyme activity assays performed in different cellular systems and under different conditions usually focusing on single PAH mutations. These inconsistent data on PAH enzyme activity hindered robust prediction of the patient’s phenotype. Furthermore, assays on single PAH mutations do not reflect the high allelic and phenotypic heterogeneity of PKU with 89 % of patients being compound heterozygotes. In addition, the knowledge on enzyme function and regulation in the therapeutic and pathologic metabolic context was still scarce. In order to get more insight into the interplay of the PAH genotype, the phenylalanine concentration and BH4 treatment, we performed functional analyses of both, single, purified PAH variants as well as PAH full genotypes in the physiological, pathological and therapeutic context. The analysis of PAH activity as a function of phenylalanine and BH4 concentrations enabled determination of the optimal working ranges of the enzyme and visualisation of differences in the regulation of PAH activity by BH4 and phenylalanine depending on the underlying genotype. Moreover, these PAH activity landscapes allowed for setting rules for dietary regimens and pharmacological treatment based on the genotype of the patient. Taken together, precise knowledge on the mechanism of the misfolding-induced loss of function in PAH deficiency enabled a better understanding of the molecular mode of action of pharmacological rescue of enzyme function by BH4. We implemented the combination of genotype-specific functional analyses together with biochemical, clinical and therapeutic data of individual patients as a powerful tool for phenotype prediction and paved the way for personalised medicine strategies in phenylketonuria

Delineating the unique functional contribution of the retrosplenial cortex in the hippocampal-diencephalic-cingulate network

The research described in this thesis investigates the unique anatomy of the retrosplenial cortex and its functional contributions to spatial working memory in the rat. The retrosplenial cortex, which is composed of Brodmann’s areas 29 and 30, has attracted attention due its apparent variable size across species and its strategic anatomical position. Reflecting its anatomical connectivity, the area has been associated with a range of cognitive functions including but not limited to episodic memory, visual processing, and navigation, but yet its exact functions have been hard to define. In humans, damage to the retrosplenial cortex can result in both anterograde and retrograde amnesia (Valenstein et al., 1987; Maguire, 2001) and can cause an interesting type of topographical disorientation where patients can recognise landmarks but are unable to utilise them to orient themselves and navigate an environment (Maguire, 2001; Vann et al., 2009). Additionally, the retrosplenial cortex is one of the first regions to exhibit pathological changes in Alzheimer’s disease and its deterioration can predict mild cognitive impairment (Pangas et al., 2010). Most recently, the area has also been associated with schizophrenia (Bluhm et al., 2009) and states of dissociation (Vesna et al., 2020). Due to its deep anatomical position in the human brain, the majority of intervention research concerning the functions of the retrosplenial cortex comes from animal studies using rodents. Although there is no consensus to its precise function, rodent studies point to multiple roles in spatial cognition including landmark coding, consolidation of spatial knowledge, and particularly, the integration between spatial reference frames

Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning