Reinforcement Learning, Error-Related Negativity, and Genetic Risk for Alzheimer\u27s Disease

Reinforcement learning (RL) has been widely used as a model of animal and human learning and decision-making. The neural networks underlying RL involve many of the same structures primarily affected by Alzheimer’s disease (AD) such as the hippocampus. Yet, RL and non-invasive evaluation of its neural underpinnings have been underutilized as a framework for understanding disease pathology and its pre-clinical states. This study aimed to provide a novel approach for assessing subtle changes in asymptomatic apolipoprotein-E (APOE) carriers and non-carriers. Electroencephalography was collected from forty APOE genotyped older adults (Male n = 11; Mage = 79.30; Meducation = 14.88 years) during an RL task comprised of distinct phases (RL, implicit). Neural components associated with the error detection system involved in RL, the response error-related negativity (ERN) and the feedback error-related negativity (FRN), were examined for individuals at low (APOE ε4-; n=20) and high risk (APOE ε4+; n=20). RL task performance did not differ between risk groups. However, the high-risk group consistently elicited greater peak amplitudes than the low-risk group. The pattern of results indicated that the high-risk group deviated from typical RL processes such that peak amplitudes did not differ between early and late learning. Additionally, despite intact learning, latent hippocampal atrophy is believed to have disrupted the transfer and use of learned information to novel situations thus altering the hippocampal-frontostriatal circuit responsible for adaptive behavior and the corresponding neural signal. The results indicate that disease related changes can be identified prior to clinical diagnosis or functional decline using RL and a non-invasive assessment of neural function, which may prove to inform clinical conceptualization, assessment, and treatment

Mechanisms of cognitive impairment in epilepsy

Cognitive impairment and dementia are increasingly reported as people with epilepsy grow older, with major impact on quality of life. The underlying mechanisms of cognitive dysfunction, however, and the magnitude of associated dementia risk in epilepsy remains unclear. This thesis will explore how cardiovascular risk factors and hippocampal dysfunction are two important mechanistic links between epilepsy and dementia. Using data from a large population-based cohort, the studies in this thesis demonstrate that cardiovascular risk factors are closely related to impairment of executive function. One finding highlighted a continuous dose-response relation of cognition with blood pressure, even in non-hypertensive individuals. The relationship between cardiovascular risk and cognition was mediated through changes in both frontoparietal white and grey matter structural networks. In the same cohort, people with epilepsy and a high cardiovascular risk were over 13 times more likely to develop dementia compared to healthy controls with low cardiovascular risk. People with epilepsy had a greater risk of developing dementia even compared with individuals with a history of stroke, with associated changes to hippocampal volume. To examine specific hippocampal-related cognitive mechanisms that may underlie memory difficulties in epilepsy, I examined two processes believed to be central to encoding and retrieval in the hippocampus: pattern separation and pattern completion. I devised a novel computer-based behavioural paradigm called the Memory Pinhole Task to distinguish them. Impairment of these two cognitive operations have been identified in ageing and other conditions such as Alzheimer’s disease. Compared to healthy controls, pattern separation deficits were observed in people with epilepsy while reduced pattern completion was seen in healthy older individuals. I then mapped these findings from the Memory Pinhole task onto potential brain mechanisms through computational modelling using a neural network. The work in this thesis describes how modifiable cardiovascular risk factors significantly contribute to cognitive ageing and dementia risk in healthy individuals and people with epilepsy. This has important implications for personal health practices and clinical guidelines, especially in people with epilepsy as there is no current guidance to mitigate dementia risk. Further, I describe a framework for testing encoding and retrieval of information into memory which may allow us to better understand difficulties seen in both health and disease

Pathway and biomarker discovery in a posttraumatic stress disorder mouse model

Posttraumatic stress disorder (PTSD), a prevalent psychiatric disorder, is caused by exposure to a traumatic event. Individuals diagnosed for PTSD not only experience significant functional impairments but also have higher rates of physical morbidity and mortality. Despite intense research efforts, the neurobiological pathways affecting fear circuit brain regions in PTSD remain obscure and most of the previous studies were limited to characterization of specific markers in periphery or defined brain regions. In my PhD study, I employed proteomics, metabolomics and transcriptomcis technologies interrogating a foot shock induced PTSD mouse model. In addition, I studied the effects of early intervention of chronic fluoxetine treatment. By in silico analyses, altered cellular pathways associated with PTSD were identified in stress-vulnerable brain regions, including prelimbic cortex (PrL), anterior cingulate cortex (ACC), basolateral amygdala (BLA), central nucleus of amygdala(CeA), nucleus accumbens (NAc) and CA1 of the dorsal hippocampus. With RNA sequencing, I compared the brain transcriptome between shocked and control mice, with and without fluoxetine treatment. Differentially expressed genes were identified and clustered, and I observed increased inflammation in ACC and decreased neurotransmitter signaling in both ACC and CA1. I applied in vivo 15N metabolic labeling combined with mass spectrometry to study alterations at proteome level in the brain. By integrating proteomics and metabolomics profiling analyses, I found decreased Citric Acid Cycle pathway in both NAc and ACC, and dysregulated cytoskeleton assembly and myelination pathways in BLA, CeA and CA1. In addition, chronic fluoxetine treatment 12 hours after foot shock prevented altered inflammatory gene expression in ACC, and Citric Acid Cycle in NAc and ACC, and ameliorated conditioned fear response in shocked mice. These results shed light on the role of immune response and energy metabolism in PTSD pathogenesis. Furthermore, I performed microdialysis in medial prefrontal cortex and hippocampus to measure the changes in extracellular norepinephrine and free corticosterone (CORT) in the shocked mouse and related them to PTSD-like symptoms, including hyperaroual and contextual fear response. I found that increased free CORT was related to immediate stress response, whereas norepinephrine level, in a brain region specific manner, predicted arousal and contextual fear response one month after trauma. I also applied metabolomics analysis to investigate molecular changes in prefrontal microdialysates of shocked mice. Citric Acid Cycle, Glyoxylate and Dicarboxylate metabolism and Alanine, Aspartate and Glutamate metabolism pathways were found to be involved in foot shock induced hyperarousal. Taken together, my study provides novel insights into PTSD pathogenesis and suggests potential therapeutic applications targeting dysregulated pathways

Pathway and biomarker discovery in a posttraumatic stress disorder mouse model

Posttraumatic stress disorder (PTSD), a prevalent psychiatric disorder, is caused by exposure to a traumatic event. Individuals diagnosed for PTSD not only experience significant functional impairments but also have higher rates of physical morbidity and mortality. Despite intense research efforts, the neurobiological pathways affecting fear circuit brain regions in PTSD remain obscure and most of the previous studies were limited to characterization of specific markers in periphery or defined brain regions. In my PhD study, I employed proteomics, metabolomics and transcriptomcis technologies interrogating a foot shock induced PTSD mouse model. In addition, I studied the effects of early intervention of chronic fluoxetine treatment. By in silico analyses, altered cellular pathways associated with PTSD were identified in stress-vulnerable brain regions, including prelimbic cortex (PrL), anterior cingulate cortex (ACC), basolateral amygdala (BLA), central nucleus of amygdala(CeA), nucleus accumbens (NAc) and CA1 of the dorsal hippocampus. With RNA sequencing, I compared the brain transcriptome between shocked and control mice, with and without fluoxetine treatment. Differentially expressed genes were identified and clustered, and I observed increased inflammation in ACC and decreased neurotransmitter signaling in both ACC and CA1. I applied in vivo 15N metabolic labeling combined with mass spectrometry to study alterations at proteome level in the brain. By integrating proteomics and metabolomics profiling analyses, I found decreased Citric Acid Cycle pathway in both NAc and ACC, and dysregulated cytoskeleton assembly and myelination pathways in BLA, CeA and CA1. In addition, chronic fluoxetine treatment 12 hours after foot shock prevented altered inflammatory gene expression in ACC, and Citric Acid Cycle in NAc and ACC, and ameliorated conditioned fear response in shocked mice. These results shed light on the role of immune response and energy metabolism in PTSD pathogenesis. Furthermore, I performed microdialysis in medial prefrontal cortex and hippocampus to measure the changes in extracellular norepinephrine and free corticosterone (CORT) in the shocked mouse and related them to PTSD-like symptoms, including hyperaroual and contextual fear response. I found that increased free CORT was related to diate stress response, whereas norepinephrine level, in a brain region specific manner, predicted arousal and contextual fear response one month after trauma. I also applied metabolomics analysis to investigate molecular changes in prefrontal microdialysates of shocked mice. Citric Acid Cycle, Glyoxylate and Dicarboxylate metabolism and Alanine, Aspartate and Glutamate metabolism pathways were found to be involved in foot shock induced hyperarousal. Taken together, my study provides novel insights into PTSD pathogenesis and suggests potential therapeutic applications targeting dysregulated pathways

Efeitos do tratamento com ácido rosmarínico em parâmetros bioquímicos e motores em modelo pré-clínico da Doença de Parkinson

A doença de Parkinson (DP) é a segunda doença neurodegenerativa mais comum em todo mundo. Atualmente, a principal estratégia farmacológica no tratamento da DP alivia apenas os sintomas motores, porém não previne ou diminui a neurodegeneração. O Ácido Rosmarínico (AR) é um éster dos ácidos cafeico, e 3,4-dihidroxifenil lático, obtido de muitas espécies vegetais nativas da flora como a Salvia officinales L. (sálvia) e a Rosmarinus officinales (alecrim). Esse composto apresenta potencial medicinal vasto com amplo espectro de ação biológica, sendo as atividades antioxidantes e de sequestro de radicais livres fatores chaves nos resultados in vivo reportados para o AR. Entretanto, a literatura é escassa em trabalhos mostrando os efeitos do AR em animais saudáveis e animais tratados com MPTP para a geração das alterações semelhantes à DP. No presente estudo, os camundongos foram aleatoriamente separados em 4 grupos distintos: Grupo controle/salina (CN), Ácido Rosmarínico/ veículo (AR); MPTP (1-metil-4-fenil-tetrahidropiridina)/salina; MPTP e AR (MPTP +AR). O AR (ou veículo) foi administrado oralmente por via intragástrica uma vez ao dia durante 14 dias, uma hora antes do MPTP ou solução salina. Os grupos MPTP e MPTP+AR receberam administração intraperitoneal de MPTP na dose de 30 mg/kg uma vez ao dia durante 5 dias (no 4-8 dia do experimento). Nos parâmetros motores, um comportamento de hiperlocomoção foi observado em animais tratados somente com MPTP, tal efeito foi significativamente prevenido pelo tratamento com AR. No contexto bioquímico, mostramos que o tratamento com AR pode aumentar a neurotransmissão dopaminérgica e serotoninérgica em animais saudáveis, uma vez que houve aumento no conteúdo das monoaminas, porém não houve a normalização das disfunções dos neurotransmissores observados nos camundongos parkinsonianos. A análise das alterações do mRNA nos componentes do sistema dopaminérgico no estriado mostrou up-regulation da expressão da enzima catecol-O-metil-transferase (COM-T) no grupo MPTP+AR, enquanto a expressão normal desta enzima foi observado nos demais grupos. Nosso estudo evidencia um potencial neuroprotetor do AR, na prevenção da alteração locomotora induzida pelo tratamento com MPTP e demonstra um potencial de prevenção do ínicio da DP por elevar o conteúdo de dopamina e serotonina no estriado em animais saudáveis. Palavras-chave: Doença de Parkinson, MPTP, Ácido Rosmarínico, neuroproteção

Ion Channel SUMOylation: a Novel Role for SUMO in Homeostatic Regulation of Multiple Ionic Conductances

Neurons can adjust their ionic currents to maintain a stable output. The homeostatic mechanisms that produce compensatory changes in ionic currents operate over multiple time scales. The rapid mechanisms that act over minutes are mostly unknown. We have been characterizing a fast homeostatic mechanism that stabilizes activity phase in the rhythmically active lateral pyloric neuron (LP) of the crustacean stomatogastric ganglion. LP activity phase is invariant. It is determined, in part, by the balance between the hyperpolarization-activated current (Ih) and the transient potassium current (IA). When LP IA is experimentally decreased, activity phase is initially disrupted, but then it recovers over minutes. This is because the decrease in IA modifies LP activity, which in turn alters cytosolic Ca2+ levels. Ca-dependent enzymes then mediate a reduction in LP Ih to restore the balance between the two conductances. We have been studying the molecular mechanisms that correlate LP IA and Ih in an activity- dependent fashion. We have found that neuronal activity adjusts the level of ion channel post- translational modification by Small Ubiquitin-like Modifier (SUMO), a peptide which when conjugated to target proteins alters their protein-protein interactions. Using a heterologous expression system, we showed that enhancing SUMOylation of HCN or Kv4 ion channels that mediate Ih and IA, respectively, produced opposite effects on the amplitudes of Ih and IA. We also demonstrated that a given change in activity produced the opposite effect on SUMOylation levels associated with each current. Thus, activity-dependent regulation of ion channel SUMOylation specified a positive correlation between the two currents. We have also demonstrated that activity-dependent regulation of ion channel SUMOylation is conditional; it only occurs in the presence of the appropriate modulatory tone. We showed this is because modulators, like dopamine, specify the targets of the SUMOylation machinery. In sum, we have discovered a novel mechanism that acts over minutes to correlate ionic conductances and thereby stabilize neuronal output

The Nexus between Artificial Intelligence and Economics

This book is organized as follows. Section 2 introduces the notion of the Singularity, a stage in development in which technological progress and economic growth increase at a near-infinite rate. Section 3 describes what artificial intelligence is and how it has been applied. Section 4 considers artificial happiness and the likelihood that artificial intelligence might increase human happiness. Section 5 discusses some prominent related concepts and issues. Section 6 describes the use of artificial agents in economic modeling, and section 7 considers some ways in which economic analysis can offer some hints about what the advent of artificial intelligence might bring. Chapter 8 presents some thoughts about the current state of AI and its future prospects.

Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

Immunohistochemical and electrophysiological investigation of E/I balance alterations in animal models of frontotemporal dementia

Behavioural variant frontotemporal dementia (bvFTD) is a neurodegenerative disease characterised by changes in behaviour. Apathy, behavioural disinhibition and stereotyped behaviours are the first symptoms to appear and all have a basis in reward and pleasure deficits. The ventral striatum and ventral regions of the globus pallidus are involved in reward and pleasure. It is therefore reasonable to suggest alterations in these regions may underpin bvFTD. One postulated contributory factor is alteration in E/I balance in striatal regions. GABAergic interneurons play a role in E/I balance, acting as local inhibitory brakes, they are therefore a rational target for research investigating early biological predictors of bvFTD. To investigate this, we will carry out immunohistochemical staining for GABAergic interneurons (parvalbumin and neuronal nitric oxide synthase) in striatal regions of brains taken from CHMP2B mice, a validated animal model of bvFTD. We hypothesise that there will be fewer GABAergic interneurons in the striatum which may lead to ‘reward-seeking’ behaviour in bvFTD. This will also enable us to investigate any preclinical alterations in interneuron expression within this region. Results will be analysed using a mixed ANOVA and if significant, post hoc t-tests will be used. The second part of our study will involve extracellular recordings from CHMP2B mouse brains using a multi-electrode array (MEA). This will enable us to determine if there are alterations in local field potentials (LFP) in preclinical and symptomatic animals. We will also be able to see if neuromodulators such as serotonin and dopamine effect LFPs after bath application. We will develop slice preparations to preserve pathways between the ventral tegmental area and the ventral pallidum, an output structure of the striatum, and the dorsal raphe nucleus and the VP. Using the MEA we will stimulate an endogenous release of dopamine and serotonin using the slice preparations as described above. This will enable us to see if there are any changes in LFPs after endogenous release of neuromodulators. We hypothesise there will be an increase in LFPs due to loss of GABAergic interneurons