The nature of procedural memory deficits in specific language impairment : an investigation using the serial reaction time task

&nbsp;This thesis investigates whether deficits in the procedural memory network can explain grammar problems in children with language disorders. Results show that procedural memory is implicated in a wide range of other difficulties. The conceptualisation and assessment of procedural memory requires refinement before a direct tie to grammar acquisition can be made.<br /

The role of preoperative diffusion tensor imaging in predicting and improving functional outcome in pediatric patients undergoing epilepsy surgery: a systematic review

Objective: Diffusion tensor imaging (DTI) is a useful neuroimaging technique for surgical planning in adult patients. However, no systematic review has been conducted to determine its utility for pre-operative analysis and planning of Pediatric Epilepsy surgery. We sought to determine the benefit of pre-operative DTI in predicting and improving neurological functional outcome after epilepsy surgery in children with intractable epilepsy. Methods: A systematic review of articles in English using PubMed, EMBASE and Scopus databases, from inception to January 10, 2020 was conducted. All studies that used DTI as either predictor or direct influencer of functional neurological outcome (motor, sensory, language and/or visual) in pediatric epilepsy surgical candidates were included. Data extraction was performed by two blinded reviewers. Risk of bias of each study was determined using the QUADAS 2 Scoring System. Results: 13 studies were included (6 case reports/series, 5 retrospective cohorts, and 2 prospective cohorts) with a total of 229 patients. Seven studies reported motor outcome; three reported motor outcome prediction with a sensitivity and specificity ranging from 80 to 85.7 and 69.6 to 100%, respectively; four studies reported visual outcome. In general, the use of DTI was associated with a high degree of favorable neurological outcomes after epilepsy surgery. Conclusion: Multiple studies show that DTI helps to create a tailored plan that results in improved functional outcome. However, more studies are required in order to fully assess its utility in pediatric patients. This is a desirable field of study because DTI offers a non-invasive technique more suitable for children. Advances in knowledge: This systematic review analyses, exclusively, studies of pediatric patients with drug-resistant epilepsy and provides an update of the evidence regarding the role of DTI, as part of the pre-operative armamentarium, in improving post-surgical neurological sequels and its potential for outcome prediction

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Genes and Gene Networks Related to Age-associated Learning Impairments

The incidence of cognitive impairments, including age-associated spatial learning impairment (ASLI), has risen dramatically in past decades due to increasing human longevity. To better understand the genes and gene networks involved in ASLI, data from a number of past gene expression microarray studies in rats are integrated and used to perform a meta- and network analysis. Results from the data selection and preprocessing steps show that for effective downstream analysis to take place both batch effects and outlier samples must be properly removed. The meta-analysis undertaken in this research has identified significant differentially expressed genes across both age and ASLI in rats. Knowledge based gene network analysis shows that these genes affect many key functions and pathways in aged compared to young rats. The resulting changes might manifest as various neurodegenerative diseases/disorders or syndromic memory impairments at old age. Other changes might result in altered synaptic plasticity, thereby leading to normal, non-syndromic learning impairments such as ASLI. Next, I employ the weighted gene co-expression network analysis (WGCNA) on the datasets. I identify several reproducible network modules each highly significant with genes functioning in specific biological functional categories. It identifies a “learning and memory” specific module containing many potential key ASLI hub genes. Functions of these ASLI hub genes link a different set of mechanisms to learning and memory formation, which meta-analysis was unable to detect. This study generates some new hypotheses related to the new candidate genes and networks in ASLI, which could be investigated through future research

Neural mechanisms of social cognition – the mirror neuron system and beyond

In my PhD thesis, I present three functional magnetic resonance imaging studies aimed at investigating neurobiological mechanisms underlying social cognition. My thesis focuses on fast and automatic processes that are proposed to build the basis of social understanding, and might be activated in parallel to more effortful deliberate mechanisms. The proposed neural substrate of fast and automatic processes are mirror neurons, which according to the theory of embodied simulation allow humans to understand other individuals’ actions, and even emotions and intentions. Since non-invasive techniques cannot be applied to measure mirror neurons, but only neural populations assumed to constitute the mirror neuron system, experimental paradigms and analysis routines that allow approximation of mirror neuron functions need to be developed. In study 1, I demonstrated that different social cognitive skills, including imitation, affective empathy and theory of mind share a common neural basis, located in regions associated with the mirror neuron system. In addition to standard analyses, a shared voxel analysis was applied that revealed common activation for social-cognitive processes not only across, but also within participants. Study 2 was set up to investigate whether the mirror neuron system can distinguish the valence of facial configurations. The use of a functional magnetic resonance imaging adaptation paradigm allowed to determine neural populations sensitive to emotional valence. While the fusiform gyrus was sensitive to changes from fearful to smiling faces and also from smiling to fearful faces, Brodmann area 44 reaching into insula, and superior temporal sulcus, i.e. regions more commonly associated with the mirror neuron system and with the so called mentalizing network, showed particularly increased activation for switches from smiling to fearful faces. Study 3 was dedicated to the investigation of decision making in the context of ambiguous facial configurations. While probabilistic decision making on these facial configurations lead to activation in the executive control network, final decisions for an emotion resulted in nucleus accumbens activation. In addition, perceiving fear in a face lead to higher nucleus accumbens activation during final decisions than perceiving happiness. This finding can be linked to salience processing in the nucleus accumbens. In conclusion, all three studies show an involvement of fast and automatic processing regions for different social-cognitive processes. Study 3 additionally examined the interaction with slower and more deliberate processes, as involved in probabilistic decision making on ambiguous faces. The mirror neuron system seems to be critically involved in different social-cognitive tasks and also sensitive to emotional valence. In cases when automatic processing is not possible, as when presented with ambiguous facial configurations, brain regions commonly associated with probabilistic decision making assist, and the nucleus accumbens, possibly by directing salience, is involved in the final decision. These results deepen the understanding of the mechanisms of social cognition and encourage the use of sophisticated methods in experimental paradigms and analysis

The neurobiological basis of gait dysfunction in Parkinson’s disease: A cross-sectional and longitudinal approach

Ph. D. Thesis.Gait impairments are a cardinal feature of Parkinson’s disease (PD) and significantly affect the well-being of patients. However, current therapies are not effective at improving specific aspects of gait in PD nor preventing them from worsening over time. This is largely due to poor understanding of the mechanisms that the brain uses to control discrete gait characteristics in PD. The aim of this thesis was, therefore, to investigate associations between the brain and gait characteristics in PD, using both cross-sectional and longitudinal analytical approaches. Newly diagnosed PD participants (n=99) and age-matched controls (n=47) completed quantitative gait, structural magnetic resonance imaging and clinical assessments soon after diagnosis; additional gait assessments were completed every 18 months for up to six years. Partial correlations and linear regression analyses determined cross-sectional associations between regional brain volumes and gait. Linear mixed-effects models identified gait characteristics that changed over six years in PD, more so than in controls, and assessed the predictive nature of regional brain volumes on gait changes. Original contributions to knowledge were that regional brain volumes selectively associated with discrete gait characteristics in PD; many associations were unique to PD, even in early disease. Brain regions which primarily relate to both motor and non-motor functions correlated with PD gait impairment. Associations with non-motor structures might be attributable to contributions from the cortical cholinergic system, given its role in maintaining gait in PD. This thesis provides evidence for the reliance on alternative and compensatory neural mechanisms during PD gait. Additionally, this thesis demonstrates the first evidence for regional brain volumes predicting disease-specific changes in gait. This not only provides greater understanding of neural underpinnings of gait dysfunction in PD, but demonstrates the potential for regional brain volumes to be considered clinically as an indicator of those at greater risk of mobility loss and fallsWellcome Trust, Parkinson’s U

Modeling of human aging using a systems approach

The healthy aging process involves a range of biological pathways which were investigated based on data from adult human fibroblasts of various ages. Two hybridcomputational models integrating organelle phenotypes with molecular mechanisms were developed using a fuzzy logic, rule‐based approach. One was a vicious cycle model which represents uncontrolled damage accumulation in a positive feedback loop, leading to a rapid cellular degradation. The second model was an adaptive response model which showed that the stress sensors NF‐kB and mTOR provide negative feedback loops causing a linear decline with age, observed in many cellular and physiological parameters.Simulations of mortality data led to discovery that a serial linear model of viability decline with a variable stochastic component would result in mortality rates to match recent data from industrialized countries. Further exploration using general computational models of biochemical networks showed the impact of the rate of linear decline, initial stochastic changes in reaction rates, and network topology on resilience and stability. The conclusion is that linear decline with age is observed and modeled at the cellular through organ system levels however mortality rates are compounded by several factors at the somatic level, leading to the serial linear decline model which combines linear decline with power law frailty models.Ph.D., Biomedical Engineering -- Drexel University, 201

Modelling of quantitative Adverse Outcome Pathways

With the growth of green chemistry initiatives, there is a demand for improved regulatory assessment of human exposure to exogenous factors. Proposed a decade ago, the adverse outcome pathway (AOP) framework serves as a knowledge assembly, evaluation, interpretation, and communication tool, designed to support pathway-oriented chemical risk assessment (CRA). The increasing number of resources and advances in machine learning (ML), artificial intelligence (AI), and the quantification of AOPs (qAOPs) has allowed for the integration of a variety of data streams including new approach methodologies (NAMs). These may predict causally inferred tipping points of the relationships that characterise a disease/adverse effect across multiple levels of biological organisation. This thesis aimed to provide an in-depth analysis of the qAOP concept and reinforces the types of efforts required to achieve validation, harmonisation and regulatory acceptance of qAOP models. The first part of this thesis assesses available qAOP models against a series of predefined common features, which enabled the challenges and opportunities for improving current practices to be identified. The second part of this thesis proposes improved methodologies for qAOPs, including the derivation of a network of linear AOPs that better depicts the complexity of biological effects and quantification of a simplified mechanistic AOP network based on domain knowledge and topology analysis. The thesis ends with a case study focused on the identification of empirical quantitative data associated with a linear AOP for quantification purposes. To apply the methodologies formulated, neurotoxicity, represented by neurodegenerative diseases such as impairment of cognitive function and Parkinsonian motor deficits, was studied. Lastly, the role of causality and reasons of why pattern-recognition is not sufficient to translate qualitative/mechanistic information into predictive models are discussed. Overall, the findings contribute to the advancement of the qAOP framework by expanding the knowledge, proposing recommendations and setting future directions towards the development and regulatory and scientific consensus of causal predictive qAOP models in toxicology. Other benefits to the field of study include how to combine information from linear AOPs into a more realistic representation of biological processes for the development of predictive models and the identification of which information (from alternatives) would be required for toxicological understanding. The work underlines knowledge gaps that need to be addressed, and exemplifies how to make use of, and integrate, the variety of available evidence for more informed predictions and improved decision making