Dissertations / Theses on the topic 'Computational neuroimaging, cognitive neuroscience'
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Salimi-Khorshidi, Gholamreza. "Statistical models for neuroimaging meta-analytic inference." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:40a10327-7f36-42e7-8120-ae04bd8be1d4.
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A statistical meta-analysis combines the results of several studies that address a set of related research hypotheses, thus increasing the power and reliability of the inference. Meta-analytic methods are over 50 years old and play an important role in science; pooling evidence from many trials to provide answers that any one trial would have insufficient samples to address. On the other hand, the number of neuroimaging studies is growing dramatically, with many of these publications containing conflicting results, or being based on only a small number of subjects. Hence there has been increasing interest in using meta-analysis methods to find consistent results for a specific functional task, or for predicting the results of a study that has not been performed directly. Current state of neuroimaging meta-analysis is limited to coordinate-based meta-analysis (CBMA), i.e., using only the coordinates of activation peaks that are reported by a group of studies, in order to "localize" the brain regions that respond to a certain type of stimulus. This class of meta-analysis suffers from a series of problems and hence cannot result in as accurate results as desired. In this research, we describe the problems that existing CBMA methods are suffering from and introduce a hierarchical mixed-effects image-based metaanalysis (IBMA) solution that incorporates the sufficient statistics (i.e., voxel-wise effect size and its associated uncertainty) from each study. In order to improve the statistical-inference stage of our proposed IBMA method, we introduce a nonparametric technique that is capable of adjusting such an inference for spatial nonstationarity. Given that in common practice, neuroimaging studies rarely provide the full image data, in an attempt to improve the existing CBMA techniques we introduce a fully automatic model-based approach that employs Gaussian-process regression (GPR) for estimating the meta-analytic statistic image from its corresponding sparse and noisy observations (i.e., the collected foci). To conclude, we introduce a new way to approach neuroimaging meta-analysis that enables the analysis to result in information such as “functional connectivity” and networks of the brain regions’ interactions, rather than just localizing the functions.
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Cooke, Megan E. "Integrating Genetics and Neuroimaging to study Subtypes of Binge Drinkers." VCU Scholars Compass, 2017. https://scholarscompass.vcu.edu/etd/5167.
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Risky alcohol use is a major health concern among college students, with 40.1% reporting binge drinking (5 or more drinks in one occasion) and 14.4% reporting heavy drinking (binge drinking on 5 or more occasions) in the past month. Risky alcohol use is thought to be the result of a complex interplay between genes, biological processes, and other phenotypic characteristics. Understanding this complex relationship is further complicated by known phenotypic heterogeneity in the development of alcohol use. Developmental studies have suggested two pathways to risky alcohol use, characterized by externalizing and internalizing characteristics, respectively. However, the underlying biological processes that differentiate these pathways are not fully understood. Neuroimaging studies have assessed reward sensitivity, emotion reactivity, and behavioral inhibition using fMRI and separately demonstrate associations in externalizing and internalizing disorders more broadly. In addition, previous genetic studies have found associations between specific polymorphisms and these externalizing and internalizing subtypes. Therefore, we sought further characterize the biological influences on binge drinking subtypes through the following specific aims: 1) determine the genetic relationship between externalizing and internalizing characteristics in binge drinkers, 2) test whether externalizing and internalizing binge drinkers show differences in brain activation in response to tasks measuring emotion reactivity, reward sensitivity, and behavioral inhibition. In order to achieve these aims, we conducted a series of genetic analyses assessing differences in overall SNP-based heritability and specific associated variants between the externalizing and internalizing subtypes. There were a few variants that reached genome-wide significance, the most notable being a cluster of SNPs associated with internalizing characteristics that were located in the RP3AL gene. In a subset of these binge drinking young adults, brain activation was measured on tasks assessing behavioral inhibition, reward sensitivity, and emotion reactivity. We found some preliminary differences with regard to emotion reactivity, that suggest internalizing binge drinkers are more reactive to faces overall but have blunted reaction to sad faces compared to externalizers. These findings provide an initial step to better understanding the underlying biology between the classic externalizing and internalizing alcohol use subtypes, which has the potential to elucidate new subtype specific targets for prevention and intervention.
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Cronin, Beau D. "Quantifying uncertainty in computational neuroscience with Bayesian statistical inference." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45336.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008.Includes bibliographical references (p. 101-106).
Two key fields of computational neuroscience involve, respectively, the analysis of experimental recordings to understand the functional properties of neurons, and modeling how neurons and networks process sensory information in order to represent the environment. In both of these endeavors, it is crucial to understand and quantify uncertainty - when describing how the brain itself draws conclusions about the physical world, and when the experimenter interprets neuronal data. Bayesian modeling and inference methods provide many advantages for doing so. Three projects are presented that illustrate the advantages of the Bayesian approach. In the first, Markov chain Monte Carlo (MCMC) sampling methods were used to answer a range of scientific questions that arise in the analysis of physiological data from tuning curve experiments; in addition, a software toolbox is described that makes these methods widely accessible. In the second project, the model developed in the first project was extended to describe the detailed dynamics of orientation tuning in neurons in cat primary visual cortex. Using more sophisticated sampling-based inference methods, this model was applied to answer specific scientific questions about the tuning properties of a recorded population. The final project uses a Bayesian model to provide a normative explanation of sensory adaptation phenomena. The model was able to explain a range of detailed physiological adaptation phenomena.
by Beau D. Cronin.
Ph.D.
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Lundh, Dan. "A computational neuroscientific model for short-term memory." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324742.
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Vellmer, Sebastian. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21597.
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In dieser Arbeit werden mithilfe der Fokker-Planck-Gleichung die Statistiken, vor allem die Leistungsspektren, von Punktprozessen berechnet, die von mehrdimensionalen Integratorneuronen [Engl. integrate-and-fire (IF) neuron],
Netzwerken von IF Neuronen und Entscheidungsfindungsmodellen erzeugt werden.
Im Gehirn werden Informationen durch Pulszüge von Aktionspotentialen kodiert.
IF Neurone mit radikal vereinfachter Erzeugung von Aktionspotentialen haben sich in Studien die auf Pulszeiten fokussiert sind als Standardmodelle etabliert. Eindimensionale IF Modelle können jedoch beobachtetes Pulsverhalten oft nicht beschreiben und müssen dazu erweitert werden. Im erste Teil dieser Arbeit wird eine Theorie zur Berechnung der Pulszugleistungsspektren von stochastischen, multidimensionalen IF Neuronen entwickelt. Ausgehend von der zugehörigen Fokker-Planck-Gleichung werden partiellen Differentialgleichung abgeleitet, deren Lösung sowohl die stationäre Wahrscheinlichkeitsverteilung und Feuerrate, als auch das Pulszugleistungsspektrum beschreibt.
Im zweiten Teil wird eine Theorie für große, spärlich verbundene und homogene Netzwerke aus IF Neuronen entwickelt, in der berücksichtigt wird, dass die zeitlichen Korrelationen von Pulszügen selbstkonsistent sind. Neuronale Eingangströme werden durch farbiges Gaußsches Rauschen modelliert, das von einem mehrdimensionalen Ornstein-Uhlenbeck Prozess (OUP) erzeugt wird. Die Koeffizienten des OUP sind vorerst unbekannt und sind als Lösung der Theorie definiert. Um heterogene Netzwerke zu untersuchen, wird eine iterative Methode erweitert.
Im dritten Teil wird die Fokker-Planck-Gleichung auf Binärentscheidungen von Diffusionsentscheidungsmodellen [Engl. diffusion-decision models (DDM)] angewendet. Explizite Gleichungen für die Entscheidungszugstatistiken werden für den einfachsten und analytisch lösbaren Fall von der Fokker-Planck-Gleichung hergeleitet. Für nichtliniear Modelle wird die Schwellwertintegrationsmethode erweitert.This thesis is concerned with the calculation of statistics, in particular the power spectra, of point processes generated by stochastic multidimensional integrate-and-fire (IF) neurons, networks of IF neurons and decision-making models from the corresponding Fokker-Planck equations. In the brain, information is encoded by sequences of action potentials. In studies that focus on spike timing, IF neurons that drastically simplify the spike generation have become the standard model. One-dimensional IF neurons do not suffice to accurately model neural dynamics, however, the extension towards multiple dimensions yields realistic behavior at the price of growing complexity. The first part of this work develops a theory of spike-train power spectra for stochastic, multidimensional IF neurons. From the Fokker-Planck equation, a set of partial differential equations is derived that describes the stationary probability density, the firing rate and the spike-train power spectrum. In the second part of this work, a mean-field theory of large and sparsely connected homogeneous networks of spiking neurons is developed that takes into account the self-consistent temporal correlations of spike trains. Neural input is approximated by colored Gaussian noise generated by a multidimensional Ornstein-Uhlenbeck process of which the coefficients are initially unknown but determined by the self-consistency condition and define the solution of the theory. To explore heterogeneous networks, an iterative scheme is extended to determine the distribution of spectra. In the third part, the Fokker-Planck equation is applied to calculate the statistics of sequences of binary decisions from diffusion-decision models (DDM). For the analytically tractable DDM, the statistics are calculated from the corresponding Fokker-Planck equation. To determine the statistics for nonlinear models, the threshold-integration method is generalized.
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Cattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.
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This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems.
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Petitet, Pierre. "Sensorimotor adaptation : mechanisms, modulation and rehabilitation potential." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:5935d96d-625a-4778-b42d-bb56c96d96cc.
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Adaptation is a fundamental property of the nervous system that underlies the maintenance of successful actions through flexible reconfiguration of sensorimotor processing. The primary aims of this thesis are 1) to investigate the computational and neural underpinnings of sensorimotor memory formation during prism adaptation (PA) in humans, and 2) how they interact with anodal transcranial direct current stimulation (a-tDCS) of the primary motor cortex (M1), in order to 3) improve efficacy of prism therapy for post-stroke spatial neglect. In chapter 4, we modify an influential state-space model of adaptation in order to characterize the contribution of short and long memory timescales to motor behaviour as sensorimotor after-effects (AEs) develop during PA. This enables us, in the multimodal 7 Tesla MRI experiment reported in chapter 5, to demonstrate that the level of M1 excitation:inhibition causally sets the relative contribution of long versus short memory timescales during PA, thus determining behavioural persistence of the AE at retention in young healthy adults. This finding offers a bridge between different levels of investigation by providing a biologically plausible neuro-computational model of how sensorimotor memories are formed and enhanced by a-tDCS. In chapter 6, we use the ageing motor system as a model of reduced GABAergic inhibition and show that the age-related decrease in M1 GABA explains why older adults demonstrate more persistent prism AEs. Taken together, these data indicate that the reduction in M1 GABAergic inhibition via excitatory a-tDCS during PA has the potential to enhance persistence of adaptation memory in both young and older adults. Informed by these results, we subsequently ask whether standard (multi-session) PA therapy combined with left M1 a-tDCS translates to greater and/or longer-lasting clinical improvements in post-stroke spatial neglect patients. In chapter 7, we compare the multimodal neuroimaging data of six neglect patients to normative data of age-matched controls. We show that in all patients, the lesion interrupted long-range frontoparietal connections, and we provide direct evidence for a pathological left dominance of activity within the lateral occipital cortex during deployment of bilateral visuospatial attention. In chapter 8, we present the behavioural performance of these patients throughout the two phases of the clinical study (i.e. before and after either PA + real M1 a-tDCS or PA + sham M1 atDCS). There was no clear effect of a-tDCS on the therapeutic effect of PA in these patients. The results of the studies presented in this thesis provide a novel insight into the neurocomputational mechanisms of sensorimotor memory formation and its modulation by a-tDCS in the healthy brain. Further investigation of how these mechanisms relate to therapeutic improvements following PA in certain neglect patients is needed.
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Wright, Sean Patrick. "Cognitive neuroscience of episodic memory: behavioral, genetic, electrophysiological, and computational approaches to sequence memory." Thesis, Boston University, 2003. https://hdl.handle.net/2144/27805.
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Boston University. University Professors Program Senior theses.PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
2031-01-02
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Vellmer, Sebastian [Verfasser]. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience / Sebastian Vellmer." Berlin : Humboldt-Universität zu Berlin, 2020. http://d-nb.info/1214240682/34.
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Ging-Jehli, Nadja Rita. "On the implementation of Computational Psychiatry within the framework of Cognitive Psychology and Neuroscience." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555338342285251.
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Asher, Derrik E. "Action Selection and Execution with Computational Neural Networks of Neuromodulation and Sensory Integration." Thesis, University of California, Irvine, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3626926.
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Neuromodulation is a neurophysiological process by which a single neuron can regulate the neural activity of a diverse population of neurons. Sensory integration is a neurobiological process by which the brain combines multiple sensory modality inputs (i.e., vision, proprioception, audition, tactile, olfactory, vestibular, interoception, and taste) into usable functional outputs. In biological systems, neuromodulation and sensory integration have been shown to have a strong influence over action selection (decision-making) and action execution (motor output) respectively. The experiments portrayed in Chapters 1-4 provide empirical and theoretical evidence for neuromodulatory influence over selected actions through predictions of expected costs and rewards. The simulation experiments described in Chapters 5-6 illustrate how sensory integration influences action execution across different neural architectures in visually and memory guided sensorimotor transformation tasks. The implications of these results and future endeavors are discussed in Chapter 7, along with a proposed computational model of both action selection and sensory integration to investigate the dynamics of decision-making influenced by the integration of multiple sensory inputs in order to execute an action.
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De, Pisapia Nicola. "A framework for implicit planning : towards a cognitive/computational neuroscience theory of prefrontal cortex function." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/24519.
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In this thesis we review available experimental findings on rodents, monkeys and humans, and we suggest a unifying interpretation of the role of the Prefrontal Cortex (PFC) in behaviour. We implement computational models to test this interpretation, and propose novel experiments. Our suggestion is, as also other researchers have proposed, that the PFC is involved in Planning, i.e. in evaluating which course of actions to execute in order to reach a goal. Unlike previous researchers, we emphasize and limit ourselves to unconscious aspects of Planning, and describe a view of this process that is quite close to Instrumental Conditioning, and doesn’t involve language, external measures of time (clocks), instructions or social interactions of any kind. Nonetheless unconscious Planning can be a quite complex activity. Under this interpretation, we show reward based computational models that, while mimicking some of the known neural properties of the PFC, can perform planning. One aspect on which we focus is the capacity of neurons in Dorsolateral PFC to code temporal information, namely when to expect task related events to occur. This is a core requirement to organize and plan complex behaviour. Another aspect on which we focus is the fundamental role played in the Planning process by the Basal Ganglia. As a plan is executed successfully several times, the Basal Ganglia build a chunked representations of the whole course of actions needed to reach a goal. At the same time the Posterior cortex retains the detailed information of how and where to execute these actions. This process allows the PFC to plan about more and more complex goals.
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Milano, Isabel. "The Characterization of Alzheimer’s Disease and the Development of Early Detection Paradigms: Insights from Nosology, Biomarkers and Machine Learning." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2192.
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Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads to inconsistent diagnoses. It also hinges on postmortem verification, and so is not a helpful method for identifying patients and research subjects in the beginning phases of the pathophysiological process. Instead, a new biomarker-based approach provides a more biological understanding of the disease and can detect pathological changes up to 20 years before the clinical symptoms emerge. Subjects are assigned a profile according to their biomarker measures of amyloidosis (A), tauopathy (T) and neurodegeneration (N) that reflects their underlying pathology in vivo. AD is confirmed as the underlying pathology when subjects have abnormal values of both amyloid and tauopathy biomarkers, and so have a biomarker profile of A+T+(N)- or A+T+(N)+. This new biomarker based characterization of AD can be combined with machine learning techniques in multimodal classification studies to shed light on the elements of the AD pathological process and develop early detection paradigms. A guiding research framework is proposed for the development of reliable, biologically-valid and interpretable multimodal classification models.
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Jessup, Ryan K. "Neural correlates of the behavioral differences between descriptive and experiential choice an examination combining computational modeling with fMRI /." [Bloomington, Ind.] : Indiana University, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3337258.
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Thesis (Ph.D.)--Indiana University, Dept. of Psychological & Brain Sciences, 2008.Title from PDF t.p. (viewed on Feb. 17, 2010). Source: Dissertation Abstracts International, Volume: 69-12, Section: B, page: 7830. Advisers: Jerome R. Busemeyer; Peter M. Todd.
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Lin, Chia-Shu. "Decision-making in the context of pain." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:3ba80922-3629-4958-a62b-7ebf75871bbf.
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Clinical and behavioural evidence has shown that the threat value of pain biases decisions about whether a stimulus is perceived as painful or not, and if yes, how intense is the sensation. This thesis aims to investigate the neural mechanisms underlying the effect of perceived threat on perceptual decisions about pain. The first study investigates the neural mechanisms underlying the effect of threat on the decision about the quality of the sensation, i.e., whether it is perceived as painful or not. The perception of pain (relative to no pain) was associated with activation in the anterior insula as well as an increased connectivity between this region and the mid-cingulate cortex (MCC). Activity in the MCC was correlated with the threat-related bias to perceived pain. In the second study, probabilistic tractography was performed with diffusion tensor imaging to investigate the structural connectivity between subdivisions of the insula and other pain-related regions. Additional analyses revealed that the structural connectivity between the anterior insula and the MCC, and between the posterior insula and somatosensory cortices, is positively correlated with the threat-related bias toward pain. In the third study, a multivariate pattern analysis (MVPA) was performed to investigate whether pain can be decoded from functional neuroimaging data acquired during the anticipation and during the receipt of pain. The results show that pain can be predicted by the pattern of neural activity in the right anterior insula during anticipation and stimulation. The fourth study investigated the effect of uncertainty about the stimulation intensity as a form of threat on the perceived intensity of pain. Uncertainty was found to be associated with an increased activation in the anterior insula. Overall, these findings suggest that a neural network consisting of the anterior insula and the MCC plays a key role in decisions about the quality and the quantity of nociceptive sensation. Results from the MVPA analysis support the notion that perceptual decisions are encoded by a distributed network of brain regions. The variability in anatomical connections between these regions may account for the individual differences in the susceptibility to a threat-mediated bias toward pain.
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Schmidt, Kristin. "Manipulating the hypothalamic-pituitary-adrenal axis : effects on cognitive and emotional information processing and neural connectivity." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c0475a98-e070-4446-9179-eb87047cb854.
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Despite extensive evidence documenting abnormal hypothalamic-pituitary-adrenal (HPA) axis functioning as a risk factor for the development of depression and other psychiatric disorders, and experimental evidence from acute stress manipulations, the effects of sustained cortisol alterations on clinically relevant cognitive-behavioural and neural processing remain poorly understood. The aim of this thesis was to characterise how non-acute changes in cortisol levels modify behavioural and neural biases implicated in stress-related disorders by following two complementary lines of evidence: firstly, by increasing cortisol via a direct pharmacological intervention; and secondly, by testing the ability of gut microbiota manipulations to alter cortisol reactivity. The first study found that sustained increases in cortisol following 10-day administration of hydrocortisone were associated with altered memory and emotional processing in healthy volunteers. Specifically, participants receiving hydrocortisone showed enhanced recognition of emotional words, while their neutral memory performance was unaffected despite lower parahippocampal and occipital activation during viewing and encoding of neutral pictures. Furthermore, we found that resting-state functional connectivity between limbic-temporal regions of interest (amygdala and hippocampus) and the striatum (head of the caudate), as well as frontal and prelimbic cortices was decreased. In contrast, hippocampal and visual processing during negative facial expressions, and functional connectivity between the amygdala and the brainstem at rest, were increased in the hydrocortisone versus placebo groups. Overall, these findings suggest that non-acute increases in glucocorticoids enhance processing of emotionally salient information in limbic-temporal regions, which may modulate further neural mechanisms of sensory and homeostatic relevance. Enhancements in declarative emotional memory following hydrocortisone also implicate the modulation of amygdalar-hippocampal interactions by cortisol. Conversely, neutral stimulus processing was found to be either reduced or unaffected across a number of cognitive and memory domains. A specific increase for negative processing was further supported by poorer self-reported well-being at the mid-point of the study in participants receiving hydrocortisone. In a separate study exploring the ability of prebiotic supplements to affect cortisol reactivity and emotional processing, a Bimuno-galactooligosaccharide prebiotic was found to reduce the waking cortisol response and increase positive versus negative attentional processing in healthy volunteers. While these effects were not found to be associated, they provide initial promising evidence of the ability to target the HPA axis and emotional processing via the gut microbiota in humans. Overall, this thesis supports the idea that stress-induced physiological changes after prolonged or repeated cortisol exposure are associated with neural and behavioural alterations, which in turn have been crucial in understanding neuropsychological mechanisms underlying psychiatric disease. A better stratification of the effects of sustained HPA axis alterations on psychiatrically relevant cognitive-emotional domains and neural mechanisms thus remains of high priority.
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Zaldivar, Andrew. "Investigating the Interactions of Neuromodulators| A Computational Modeling, Game Theoretic, Pharmacological, Embodiment, and Neuroinformatics Perspective." Thesis, University of California, Irvine, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3631146.
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Neuromodulatory systems originate in nuclei localized in the subcortical region of the brain and control fundamental behaviors by interacting with many areas of the central nervous system. Much is known about neuromodulators, but their structural and functional implications in fundamental behavior remain unclear. This dissertation set out to investigate the interaction of neuromodulators and their role in modulating behaviors by combining methodologies in computational modeling, game theory, embodiment, pharmacological manipulations, and neuroinformatics. The first study introduces a novel computational model that predicts how dopamine and serotonin shape competitive and cooperative behavior in a game theoretic environment. The second study adopted the model from the first study to gauge how humans react to adaptive agents, as well as measuring the influence of embodied agents on game play. The third study investigates functional activity of these neuromodulatory circuits by exploring the expression energy of neuromodulatory receptors using the Allen Brain Atlas. The fourth study features a web application known as the Allen Brain Atlas-Drive Visualization, which provides users with a quick and intuitive way to survey large amounts of expression energy data across multiple brain regions of interest. Finally, the last study continues exploring the interaction of dopamine and serotonin by focusing specifically on the reward circuit using the Allen Brain Atlas. The first two studies provide a more behavioral understanding of how dopamine and serotonin interacts, what that interaction might look like in the brain, and how those interactions transpire in complex situations. The remaining three studies uses a neuroinformatics approach to reveal the underlying empirical structure and function behind the interactions of dopamine, serotonin, acetylcholine and norepinephrine in brain regions responsible for the behaviors discussed in the first two studies. When combined, each study provides an additional level of understanding about neuromodulators. This is of great importance because neuroscience simply cannot be explained through one methodology. It is going to take a multifaceted effort, like the one presented in this dissertation, to obtain a deeper understanding of the complexity behind neuromodulators and their structural and functional relationship with each other.
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Jennette, Kyle Joseph. "The Association of Cognitive Endophenotypes and Risky Single Nucleotide Polymorphisms of Alzheimer's Disease within the Alzheimer's Disease Neuroimaging Initiative (ADNI) Database." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5510.
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Objective: The purpose of this study was to assess the influence of three single nucleotide polymorphisms (SNP) previously associated with Alzheimer's disease on specific domains of cognition, when controlling for Apolipoprotein E gene (APOE), in a sample of individuals with Alzheimer's disease. Methods: The data were drawn from the Alzheimer's Disease Neuroimaging Initiative database, a comprehensive, longitudinal database of controls, persons with mild cognitive impairment, and persons with mild Alzheimer's disease. Each subject has a full neuropsychological assessment, neuroimaging, genetic sequencing, and physical evaluation. For the purposes of this study, individuals were selected based on the presence of the three SNPs of interest: CR1 (rs3818361_T), CLU (rs11136000_T), and PICALM (rs3851179_A). Each SNP was then measured against the available tests of the ADNI neuropsychological battery that measured immediate and long delay memory, semantic fluency, and confrontation naming. Results: Only the CR1 SNP (rs3818361_T) had significant findings. The presence of the CR1 SNP associated with lower performance on logical memory recall total score, AVLT immediate recall trials 2 and 4, AVLT delayed recall, and confrontation naming in the 12-month control group. Logical memory and AVLT delayed recall were also negatively associated with CR1 in the 12-month AD case group. Discussion: These results support previous findings that the CR1 SNP rs3818361_T is a risk factor for cognitive impairment in individuals with and without AD. Such findings can aid in the earlier detection of Alzheimer's disease, risk for domain specific cognitive impairment, and novel targets for personalized pharmacotherapy.
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Cogliati, Dezza Irene. "“Vanilla, Vanilla .but what about Pistachio?” A Computational Cognitive Clinical Neuroscience Approach to the Exploration-Exploitation Dilemma." Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/278730/3/Document1.pdf.
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On the 24th November of 1859, Charles Darwin published the first edition of The Origin of Species. One hundred fifty-nine years later, our understanding of human and animal adaptation to the surrounding environment remains a major scientific challenge. How do humans and animals generate apt decision strategies in order to achieve this adaptation? How does their brain efficiently carry out complex computations in order to produce such adaptive behaviors? Although an exhaustive answer to these questions continues to feel out of reach, the investigation of adaptive processing results relevant in understanding mind/brain relationship and in elucidating scenarios where mind/brain interactions are corrupted such as in psychiatric disorders. Additionally, understanding how the brain efficiently scales problems when producing complex and adaptive behaviors can inspire and contribute to resolve Artificial Intelligence (AI) problems (e.g. scaling problems, generalization etc.) and consequently to the develop intelligent machines. During my PhD, I investigated adaptive behaviors at behavioral, cognitive, and neural level. I strongly believe that, as Marr already pointed out, in order to understand how our brain-machine works we need to investigate the phenomenon from 3 different levels: behavioral, algorithm and neural implementation. For this reason, throughout my doctoral work I took advantages of computational modeling methods together with cognitive neuroscience techniques in order to investigate the underlying mechanisms of adaptive behaviors.Doctorat en Sciences psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished
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Thomas, Adam G. "Brain plasticity and aerobic fitness." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:c941d5b2-4b37-420a-be3f-d71e753fc8d6.
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Regular aerobic exercise has a wide range of positive effects on health and cognition. Exercise has been demonstrated to provide a particularly powerful and replicable method of triggering a wide range of structural changes within both human and animal brains. However, the details and mechanisms of these changes remain poorly understood. This thesis undertakes a comprehensive examination of the relationship between brain plasticity and aerobic exercise. A large, longitudinal experiment was conducted in which healthy but sedentary participants were scanned before and after six-weeks of monitored aerobic exercise. Increases in the volume of the anterior hippocampus were observed, as previously reported in an older cohort after a longer exercise intervention. Multimodal imaging methods allowed an in-depth exploration of the mechanisms underlying this volume change, which proved to be dominated by white matter changes rather than the vascular changes that have been previously reported. A surprising global change in the balance of CSF, blood, and brain tissue within the cranial cavity was also observed. Cross-sectional differences in memory and brain structure associated with fitness were also observed. The volume of the anterior hippocampus was shown to correlate with a measure of working memory. Higher cerebral blood volume throughout the brain was found to correlate with greater fitness and better working memory. Focal associations between fitness and magnetic susceptibility, a measure of iron content, were also observed in the basal ganglia. These findings demonstrate that aerobic fitness is associated with improved cognition and brain structure throughout the lifespan rather than simply acting to mitigate age related brain atrophy or accelerate brain development. Finally, a new pipeline was developed for analysing hippocampal morphometry using high-resolution, 7 Tesla scans. Striking variability in the convolution of the hippocampal surface is reported. This technique shows promise for imaging the precise nature of the change in hippocampal volume associated with aerobic exercise. This thesis adds to the evidence that aerobic exercise is a potent catalyst for behavioural and brain plasticity while also demonstrating that the mechanisms for those plastic changes are likely different than previously supposed. Future work will refine these measurement techniques, perhaps to a point where brain changes can be monitored on a single subject level. This work will provide an important tool to understand how best to utilize aerobic exercise to facilitate adaptive behavioural changes, mitigate the negative effects of ageing and disease on the brain, and maximize the benefits of active lifestyles.
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Haarsma, Joost. "Towards a mechanistic understanding of the neurobiological mechanisms underlying psychosis." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/283000.
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Psychotic symptoms are prevalent in a wide variety of psychiatric and neurological disorders. Yet, despite decades of research, the neurobiological mechanisms via which these symptoms come to manifest themselves remain to be elucidated. I argue in this thesis that using a mechanistic approach towards understanding psychosis that borrows heavily from the predictive coding framework, can help us understand the relationship between neurobiology and symptomology. In the first results chapter I present new data on a biomarker that has often been cited in relation to psychotic disorders, which is glutamate levels in the anterior cingulate cortex (ACC), as measured with magnetic resonance spectroscopy. In this chapter I aimed to replicate previous results that show differences in glutamate levels in psychosis and health. However, no statistically significant group differences and correlations with symptomology were found. In order to elucidate the potential mechanism underlying glutamate changes in the anterior cingulate cortex in psychosis, I tested whether a pharmacological challenge of Bromocriptine or Sulpiride altered glutamate levels in the anterior cingulate cortex. However, no significant group differences were found, between medication groups. In the second results chapter I aimed to address a long-standing question in the field of computational psychiatry, which is whether prior expectations have a stronger or weaker influence on inference in psychosis. I go on to show that this depends on the origin of the prior expectation and disease stage. That is, cognitive priors are stronger in first episode psychosis but not in people at risk for psychosis, whereas perceptual priors seem to be weakened in individuals at risk for psychosis compared to healthy individuals and individuals with first episode psychosis. Furthermore, there is some evidence that these alterations are correlated with glutamate levels. In the third results chapter I aimed to elucidate the nature of reward prediction error aberrancies in chronic schizophrenia. There has been some evidence suggesting that schizophrenia is associated with aberrant coding of reward prediction errors during reinforcement learning. However it is unclear whether these aberrancies are related to disease years and medication use. Here I provide evidence for a small but significant alteration in the coding of reward prediction errors that is correlated with medication use. In the fourth results chapter I aimed to study the influence of uncertainty on the coding of unsigned prediction errors during learning. It has been hypothesized by predictive coding theorists that dopamine plays a role in the precision-weighting of unsigned prediction error. This theory is of particular relevance to psychosis research, as this might provide a mechanism via which dopamine aberrancies, might lead to psychotic symptoms. I found that blocking dopamine using Sulpiride abolishes precision-weighting of unsigned prediction error, providing evidence for a dopamine mediated precision-weighting mechanism. In the fifth results chapter I aimed to extend this research into early psychosis, to elucidate whether psychosis is indeed associated with a failure to precision-weight prediction error. I found that first episode psychosis is indeed associated with a failure to precision-weight prediction errors, an effect that is explained by the experience of positive symptoms. In the sixth results chapter I explore whether the degree of precision-weighting of unsigned prediction errors is correlated with glutamate levels in the anterior cingulate cortex. Such a correlation might be plausible given that psychosis has been associated with both. However, I did not find such a relationship, even in a sample of 137 individuals. Thus I concluded that anterior cingulate glutamate levels might be more related to non-positive symptoms associated with psychotic disorders. In summary, a mechanistic approach towards understanding psychosis can give us valuable insights into the disease mechanisms at play. I have shown here that the influence of expectations on perception is different across disease stage in psychosis. Furthermore, aberrancies in prediction error mechanisms might explain positive symptoms in psychosis, a process likely mediated by dopaminergic mechanisms, whereas evidence for glutamatergic mediation remains absent.
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Clark, Ian Alexander. "A clinical neuroscience investigation into flashbacks and involuntary autobiographical memories." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:04f72e37-73fe-4347-8af1-8d8852c05f1b.
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Recurrent and intrusive distressing recollections of trauma are a hallmark symptom of Posttraumatic Stress Disorder (PTSD). The term ‘flashback’ is used in this thesis to refer to vivid, sensory perceptual (predominantly visual images), emotional memories from a traumatic event that intrude involuntarily into consciousness. Furthermore, intrusive image based memories occur in a number of other psychological disorders, for example, bipolar disorder and depression. Clinically, the presence and occurrence of flashbacks and flashback type memories are well documented. However, in terms of the neural underpinnings there is limited understanding of how such flashback memories are formed or later involuntarily recalled. An experimental psychopathology approach is taken whereby flashbacks are viewed on a continuum with other involuntary autobiographical memories and are studied using analogue emotional events in the laboratory. An initial review develops a heuristic clinical neuroscience framework for understanding flashback memories. It is proposed that flashbacks consistent of five component parts – mental imagery, autobiographical memory, involuntary recall, attention hijacking and negative emotion. Combining knowledge of the component parts helped provide a guiding framework, at both a neural and behavioural level, into how flashback memories may be formed and how they return to mind unbidden. Four studies (1 neuroimaging, 3 behavioural) using emotional film paradigms were conducted. In the first study, the trauma film paradigm was combined with neuroimaging (n = 35) to investigate the neural basis of both the encoding and the involuntary recall of flashback memories. Results provided a first replication of a specific pattern of brain activation at the encoding of memories that later returned as flashbacks. This included elevation in the rostral anterior cingulate cortex, insula, thalamus, ventral occipital cortex and left inferior frontal gyrus (during just the encoding of scenes that returned as flashbacks) alongside suppressed activation in the left inferior frontal gyrus (during the encoding of scenes that returned as flashbacks in other participants, but not that individual). Critically, this is also the first study to show the brain activation at the moment of flashback involuntary recall in the scanner. Activation in the middle and superior frontal gyri and the left inferior frontal gyrus was found to be associated with flashback involuntary recall. In the second study, control conditions from 16 behavioural trauma film paradigm experiments were combined (n = 458) to investigate commonly studied factors that may be protective against flashback development. Results indicated that low emotional response to the traumatic film footage was associated with an absence of flashbacks over the following week. The third study used a positive film to consider the emotional valence of the emotion component of the framework. Positive emotional response at the time of viewing the footage was associated with positive involuntary memories over the following week. The fourth study aimed to replicate and extend this finding, comparing the impact of engaging in two cognitive tasks after film viewing (equated for general load). Predictions were not supported and methodological considerations are discussed. Results may have implications for understanding flashbacks and involuntary autobiographical memories occurring in everyday life and across psychological disorders. Further understanding of the proposed components of the clinical neuroscience framework may even help inform targeted treatments to prevent, or lessen, the formation and frequency of distressing involuntary memories.
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Iyer, Laxmi R. "CANDID - A Neurodynamical Model of Idea Generation." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1326828617.
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Bolenz, Florian, Andrea M. F. Reiter, and Ben Eppinger. "Developmental Changes in Learning: Computational Mechanisms and Social Influences." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-232296.
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Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.
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Hunt, Laurence T. "Modelling human decision under risk and uncertainty." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:244ce799-7397-4698-8dac-c8ca5d0b3e28.
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Humans are unique in their ability to flexibly and rapidly adapt their behaviour and select courses of action that lead to future reward. Several ‘component processes’ must be implemented by the human brain in order to facilitate this behaviour. This thesis examines two such components; (i) the neural substrates supporting action selection during value- guided choice using magnetoencephalography (MEG), and (ii) learning the value of environmental stimuli and other people’s actions using functional magnetic resonance imaging (fMRI). In both situations, it is helpful to formally model the underlying component process, as this generates predictions of trial-to-trial variability in the signal from a brain region involved in its implementation. In the case of value-guided action selection, a biophysically realistic implementation of a drift diffusion model is used. Using this model, it is predicted that there are specific times and frequency bands at which correlates of value are seen. Firstly, there are correlates of the overall value of the two presented options, and secondly the difference in value between the options. Both correlates should be observed in the local field potential, which is closely related to the signal measured using MEG. Importantly, the content of these predictions is quite distinct from the function of the model circuit, which is to transform inputs relating to the value of each option into a categorical decision. In the case of social learning, the same reinforcement learning model is used to track both the value of two stimuli that the subject can choose between, and the advice of a confederate who is playing alongside them. As the confederate advice is actually delivered by a computer, it is possible to keep prediction error and learning rate terms for stimuli and advice orthogonal to one another, and so look for neural correlates of both social and non-social learning in the same fMRI data. Correlates of intentional inference are found in a network of brain regions previously implicated in social cognition, notably the dorsomedial prefrontal cortex, the right temporoparietal junction, and the anterior cingulate gyrus.
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Michael, Elizabeth. "Dissociable sources of uncertainty in perceptual decision making." Thesis, University of Oxford, 2016. http://ora.ox.ac.uk/objects/uuid:581e8fc9-1e12-4877-a89a-44cdc67c45e2.
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The natural world provides sensory systems with noisy and ambiguous information, which is often transformed into a more stable categorical percept. This thesis aims to investigate the nature of the neural representations in the visual system that support this transformation. To do so, we will employ a behavioural task that requires participants to average several independent sources of perceptual information. This task allows for the dissociation of two theoretically orthogonal sources of decision uncertainty: the mean distance of the perceptual information from a category boundary and the variability of the evidence under consideration. Behaviourally, both decreasing the mean distance to bound of information and increasing information variability are associated with increased errors and prolonged response times. We will present a computational model that can account for the independent behavioural effects of these two sources of uncertainty by assuming that categorical decisions are made on the basis of a probabilistic transformation of perceptual evidence. BOLD measurements demonstrate that these effects of mean and variability are supported by a partially dissociable network of brain regions. Electroencephalography demonstrates the differential influence of mean and variance in the pre- and post-decision period. Furthermore, we show that there is adaptation at the level of the perceptual representation to the information variance. Not only does this show that the visual system must represent information at the summary level, in addition to individual feature-based representation, but it also suggests that the costs associated with this form of perceptual uncertainty can be largely mitigated by the adoption of a more suitable representational range.
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Bolenz, Florian, Andrea M. F. Reiter, and Ben Eppinger. "Developmental Changes in Learning: Computational Mechanisms and Social Influences." Frontiers Research Foundation, 2017. https://tud.qucosa.de/id/qucosa%3A30736.
Full textAbstract:
Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.
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Howard, Newton. "Approach to study the brain : towards the early detection of neurodegenerative disease." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f81e9d4-ac91-444f-b966-ce1fc665b065.
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Neurodegeneration is a progressive loss of neuron function or structure, including death of neurons, and occurs at many different levels of neuronal circuitry. In this thesis I discuss Parkinson’s Disease (PD), the second most common neurodegenerative disease (NDD). PD is a devastating progressive NDD often with delayed diagnosis due to detection methods that depend on the appearance of visible motor symptoms. By the time cardinal symptoms manifest, 60 to 80 percent or more of the dopamine-producing cells in the substantia nigra are irreversibly lost. Although there is currently no cure, earlier detection would be highly beneficial to manage treatment and track disease progression. However, today’s clinical diagnosis methods are limited to subjective evaluations and observation. Onset, symptoms and progression significantly vary from patient to patient across stages and subtypes that exceed the scope of a standardized diagnosis. The goal of this thesis is to provide the basis of a more general approach to study the brain, investigating early detection method for NDD with focus on PD. It details the preliminary development, testing and validation of tools and methods to objectively quantify and extrapolate motor and non-motor features of PD from behavioral and cognitive output during everyday life. Measures of interest are categorized within three domains: the motor system, cognitive function, and brain activity. This thesis describes the initial development of non-intrusive tools and methods to obtain high-resolution movement and speech data from everyday life and feasibility analysis of facial feature extraction and EEG for future integration. I tested and validated a body sensor system and wavelet analysis to measure complex movements and object interaction in everyday living situations. The sensor system was also tested for differentiating between healthy and impaired movements. Engineering and design criteria of the sensor system were tested for usability during everyday life. Cognitive processing was quantified during everyday living tasks with varying loaded conditions to test methods for measuring cognitive function. Everyday speech was analyzed for motor and non-motor correlations related to the severity of the disease. A neural oscillation detection (NOD) algorithm was tested in pain patients and facial expression was analyzed to measure both motor and non-motor aspects of PD. Results showed that the wearable sensor system can measure complex movements during everyday living tasks and demonstrates sensitivity to detect physiological differences between patients and controls. Preliminary engineering design supports clothing integration and development of a smartphone sensor platform for everyday use. Early results from loaded conditions suggest that attentional processing is most affected by cognitive demands and could be developed as a method to detect cognitive decline. Analysis of speech symptoms demonstrates a need to collect higher resolution spontaneous speech from everyday living to measure speech motor and non-motor speech features such as language content. Facial expression classifiers and the NOD algorithm indicated feasibility for future integration with additional validation in PD patients. Thus this thesis describes the initial development of tools and methods towards a more general approach to detecting PD. Measuring speech and movement during everyday life could provide a link between motor and cognitive domains to characterize the earliest detectable features of PD. The approach represents a departure from the current state of detection methods that use single data entities (e.g.one-off imaging procedures), which cannot be easily integrated with other data streams, are time consuming and economically costly. The long-term vision is to develop a non-invasive system to measure and integrate behavioral and cognitive features enabling early detection and progression tracking of degenerative disease.
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Vetter, Nora. "Theory of Mind Development in Adolescence and its (Neuro)cognitive Mechanisms." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-110202.
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Theory of Mind (ToM) is the ability to infer others’ mental states and thus to predict their behavior (Perner, 1991). Therefore, ToM is essential for the adequate adjustment of behavior in social situations. ToM can be divided into: 1) cognitive ToM encompassing inferences about intentions and beliefs and 2) affective ToM encompassing inferences about emotions (Shamay-Tsoory, Harari, Aharon-Peretz, & Levkovitz, 2010). Well-functioning skills of both ToM aspects are much-needed in the developmental period of adolescence because in this age phase peer relationships become more important and romantic relationships arise (Steinberg & Morris, 2001). Importantly, affective psychopathological disorders often have their onset in adolescence. ToM development in adolescence might be based on underlying cognitive mechanisms such as the ability to inhibit one’s own thoughts in order to understand another person’s thoughts (Carlson & Moses, 2001). Another possible mechanism relates to functional brain development across adolescence (Blakemore, 2008). Therefore, neurocognitive mechanisms may underlie ongoing ToM development in adolescence. First studies indicate an ongoing behavioral and functional brain development of ToM (e.g. Blakemore, 2008). However, ToM development in adolescence and how this might relate to underlying (neuro)cognitive functions remains largely underexamined.
The major aims of the current thesis were first to answer the overall question whether there is an ongoing development of ToM in adolescence. This question relates to both behavioral and functional brain development. As a second major aim, the present work sought to elucidate possible (neuro)cognitive mechanisms of ongoing ToM development across adolescence. Specifically, these cognitive mechanisms might be basic cognitive functions as well as executive functions. Additionally, the present work aimed at exploring potential (neuro)cognitive mechanisms through an integration of both behavioral and functional brain studies.
The current experimental work spans three cross-sectional studies investigating adolescents (aged around 12-15 years) and young adults (aged around 18-22 years) to examine for the first time both the behavioral (studies I and II) and functional brain development of ToM (study III) in adolescence and its underlying (neuro)cognitive mechanisms. In all three studies, more complex, advanced ToM tasks were employed to avoid ceiling effects. Study I was aimed at investigating if cognitive and affective ToM continues to develop in adolescence and at exploring if basic cognitive variables such as verbal ability, speed of processing, and working memory capacity underlie such development. Hence, two groups of adolescents and young adults completed tasks of ToM and basic cognitive abilities. Large age effects were revealed on both measures of ToM: adolescents performed lower than adults. These age differences remained significant after controlling for basic cognitive variables. However, verbal ability covaried with performance in affective ToM. Overall, results support the hypothesis of an ongoing development of ToM from adolescence to adulthood on both cognitive and affective aspects. Results may further indicate verbal ability being a basic cognitive mechanism of affective ToM.
Study II was designed to further explore if affective ToM, as measured with a dynamic realistic task, continues to develop across adolescence. Importantly, this study sought to explore executive functions as higher cognitive mechanisms of developing affective ToM across adolescence. A large group spanning adolescents and young adults evaluated affective mental states depicted by actors in video clips. Additionally, participants were examined with three subcomponents of executive functions, inhibition, updating, and shifting following the classification of Miyake et al. (2000). Affective ToM performance was positively related to age and all three executive functions. Specifically, inhibition explained the largest amount of variance in age related differences of affective ToM performance. Overall, these results indicate the importance of inhibition as key underlying mechanism of developing an advanced affective ToM in adolescence.
Study III set out to explore the functional brain development of affective ToM in adolescence by using functional magnetic resonance imaging (fMRI). The affective ToM measure was the behavioral developmentally sensitive task from study II. An additional control condition consisted of the same emotional stimuli with the instruction to focus on physical information. This study faced methodical challenges of developmental fMRI studies by matching performance of groups. The ventromedial prefrontal cortex (vMPFC) was significantly less deactivated in adolescents in comparison to adults, which might suggest that adolescents seem to rely more on self-referential processes for affective ToM. Furthermore, adolescents compared to adults showed greater activation in the dorsolateral prefrontal cortex (DLPFC) in the control condition, indicating that adolescents might be distracted by the emotional content and therefore needed to focus more on the physical content of the stimulus. These findings suggest affective ToM continues to develop on the functional brain level and reveals different underlying neurocognitive strategies for adolescents in contrast to adults.
In summary, the current thesis investigated whether ToM continues to develop in adolescence until young adulthood and explored underlying (neuro)cognitive mechanisms. Findings suggest that there is indeed an ongoing development of both the cognitive and affective aspect of ToM, which importantly contributes to the conceptual debate. Moreover, the second benefit to the debate is to demonstrate how this change may occur. As a basic cognitive mechanism verbal ability and as an executive functioning mechanism inhibition was revealed. Furthermore, neurocognitive mechanisms in form of different underlying neurocognitive strategies of adolescents compared to adults were shown. Taken together, ToM development in adolescence seems to mirror a different adaptive cognitive style in adolescence (Crone & Dahl, 2012). This seems to be important for solving the wealth of socio-emotional developmental tasks that are relevant for this age span.
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Foubert, Luc. "Spatio-temporal characteristics of the visual interhemispheric integration via the corpus callosum : computational modeling & optical imaging approaches." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2007. http://tel.archives-ouvertes.fr/tel-00811495.
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Le cerveau des mammifères est composé de deux hémisphères. Bien qu'anatomiquement séparés, ceux-ci coopèrent l'un avec l'autre par l'intermédiaire de faisceaux de fibres qui constituent les commissures cérébrales. Parmi ces commissures, le corps calleux est la plus importante, tout au moins par le nombre de fibres qui la constitue (200 à 800 millions suivant les espèces). Bien que le rôle de cette commissure soit resté longtemps inconnu, il est maintenant bien établi qu'elle transporte des messages aussi divers que des messages visuels, limbiques, auditifs, somesthésiques et moteurs d'un hémisphère à l'autre. En conséquence, le corps calleux s'est révélé être impliqué dans des fonctions cognitives supérieures telles la perception sensorielle, l'apprentissage, la mémoire et la motricité. En dépit de l'établissement de ces concepts importants, la connaissance du corps calleux et de son rôle dans les fonctions cognitives supérieures restent encore extrêmement incomplètes que ce soit au cours du développement ou chez l'adulte. Or, ces questions sont essentielles puisqu'elles posent directement le problème du rôle de l'intégration interhémisphérique dans l'élaboration des fonctions cognitives dans les conditions normales; elles touchent également le problème du rôle de cette même intégration dans les processus de réorganisation et de compensation qui peuvent se développer dans les conditions pathologiques, conduisant à une restructuration des fonctions cognitives. Ce travail de thèse a été réalisé dans le contexte expérimental de C. Milleret et de ses collaborateurs qui étudient les caractéristiques anatomo-fonctionnelles et topographiques des cartes corticales calleuses localisées au niveau des aires visuelles corticales primaires 17 et 18 de chaque hémisphère chez le mammifère et qui sont associées au traitement de la région médiane verticale centrale du champ visuel. Cette région centrale du champ visuel est des plus stratégiques d'un point de vue perceptif puisqu'elle participe à la fusion des deux hémichamps visuels. En utilisant les techniques d'électrophysiologies in vivo et anatomiques (reconstructions 3D d'axones marqués à la biocytine), il a déjà été montré que ces connexions interhémisphériques sont presque exclusivement limitées à la bordure de transition entre les aires visuelle primaires A17 et A18. De plus, les neurones des aires visuels primaires qui sont activés par les axones interhémisphériques présentent des caractéristiques fonctionnelles bien précises. Certaines caractéristiques anatomo-fonctionnelles et topographiques des cartes corticales calleuses sont déjà bien identifiées mais elle se révèlent encore insuffisantes pour préciser le rôle du corps calleux dans les processus d'intégration visuelle interhémisphérique, en particulier en l'absence de données précises des caractéristiques dans les domaines temporels et spatiaux et la façon dont elles sont modifiées dans des conditions de développent visuel asymétriques. Ceci résulte aussi du faible nombre de travaux faisant appel à une approche computationnelle et la modélisation pour aborder ces questions. En particulier, les relations entre les caractéristiques morphologiques des axones calleux et les propriétés spatiales (cartes fonctionnelles) et temporelles (latences de transfert et propriétés spectrales) des populations neurales qu'elle mettent en relation sont encore très imprécises. Caractérisation quantitative des distributions des terminaisons d'axones calleux. Dans sa première partie, notre étude propose de préciser les extensions spatiales et les caractéristiques morphologiques des arborisations d'axones calleux obtenus dans les conditions de développement visuel normal (NR) et dans les conditions de déprivation monoculaire précoces (MD) afin de les différentier quantitativement. Dans cet objectif, deux groupes d'axones reconstruits en 3D sont tout d'abord décrits qualitativement par les méthodes conventionnelles d'anatomie. Cette méthode rencontre néanmoins des difficultés pour caractériser précisément les morphologies des axones, en particulier l'extension de leur terminaisons sur la surface du cortex, leur orientation et leur degré de fragmentation. Pour répondre à ces questions, deux méthodes computationnelles complémentaires et de complexité croissante ont été développées pour caractériser les distributions de terminaison axonales calleuses et mettre en évidences les différences entre les deux groupes. Celles-ci nous ont permis de montrer les plus grandes extensions spatiales ainsi que le plus grand degré de fragmentation des distributions des terminaisons des axones calleux du groupe MD. Dans un dernier chapitre, la simulation de propagation de potentiel d'action dans les structures axonales a permis de montrer que les différences morphologiques constatées dans le groupe MD, ne semblent pas se répercuter sur la dispersion temporelle du signal entre les terminaisons. Ainsi, la distribution temporelle du signal controlatéral demeure pour la grande majorité confinée dans un intervalle inférieur à 2ms, dispersion compatible avec des hypothèses de synchronisation. Développement de la technique d'imagerie optique par colorant sensibles au potentiels Avec la perspective d'explorer expérimentalement les propriétés spatio-temporelles de l'intégration visuelle interhémisphérique et afin de corroborer les résultats présenté dans la première partie de la thèse, la mise en place d'un poste expérimental d'imagerie optique au sein de notre laboratoire est présentée dans la deuxième partie. Cette méthode permet de visualiser in vivo les domaines d'activation spécifique à différents attributs au sein des cartes corticales calleuses et d'approcher certaines caractéristiques temporelles de l'activité neuronale. Réalisé en parallèle avec les travaux de modélisation des axones calleux, le montage complet du poste a montré d'abord permis de cerner les limitations du système initial. Dans un deuxième temps l'adaptation du système à la problématique interhémisphérique, réalisée au fils des mois, a montré d'importants progrès après plusieurs modifications spécifiques. La mise en place du poste expérimental a pu bénéficier de l'expertise en imagerie optique de l'équipe de recherche du Dr. S. Tanaka au RIKEN Brain Science Institute au Japon, où l'auteur a effectué plusieurs séjours au cours desquelles ont pu être initiées un certain nombres d'adaptation importantes comme le développement du protocole d'enregistrement en Voltage Sensitive Dye (VSD), permettant l'enregistrement de l'activité neurale avec un grande précision temporelle, ainsi que le développement de techniques de traitement des signaux appropriées. La mise en place du poste expérimental dans les locaux parisiens a pu être achevée fin 2006 avec l'obtention de données prometteuses pour la poursuite du programme expérimental, comme l'enregistrement à 3 ms/image de l'activation corticale bilatérale et ainsi que celle du transfert interhémisphérique. Ces résultats ouvrent les perspectives de recherche visant la combinaison des données anatomiques morphologiques avec les données d'enregistrement d'activations spatio-temporelles in vivo de l'intégration visuelle hémisphérique au sein des cortex visuels primaires.
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Labache, Loïc. "Création d'Atlas des Réseaux Cérébraux Sous-tendant les Fonctions Cognitives Latéralisées : Application à l'Étude de la Variabilité Inter-individuelle du Langage." Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0155.
Full textAbstract:
Mon travail de thèse s'inscrit dans une approche d'intégration multimodale et multi-échelle qui a conduit à l'émergence de la neuroimagerie cognitive et de population. Il repose sur deux modalités de cartes fonctionnelles tridimensionnelles cérébrales obtenues en IRMf : les cartes d'activation permettant de visualiser les régions dont l’activité est évoquée par un processus cognitif et les cartes de connectivité intrinsèque mesurant la synchronisation entre des régions spatialement distantes, mais connectées fonctionnellement. J'ai appliqué à ces deux types de cartes de nouvelles méthodologies statistiques permettant de traiter à la fois les dimensions individuelles et spatiales. Dans une première partie, j'ai conçu des atlas de régions cérébrales dédiées à des fonctions cognitives spécifiques, basés sur leur latéralisation hémisphérique et ciblant une population sélectionnée pour sa faible variabilité. Je présente ici les deux premiers atlas du langage. En effet, bien qu'il existe de nombreuses approches pour cartographier les régions du langage chez les patients, il n'existait pas d'atlas des réseaux langagiers chez les individus sains. J’ai tout d’abord identifié les régions activées dans l'hémisphère gauche et asymétriques gauche, à la fois pendant la production, l'écoute et la lecture de phrases, chez 137 individus sains droitiers. L’analyse de la connectivité intrinsèque entre les 32 régions identifiées a permis de mettre en évidence qu'elles faisaient partie de 3 réseaux fonctionnels distincts. Le tout constituant ainsi l’atlas cérébral SENSAAS (SENtence Supramodal Areas AtlaS). Parmi ces réseaux, l'un comprenant 18 régions contient les zones essentielles du langage (SENT_CORE), c'est-à-dire les aires cérébrales dont la lésion entraînerait une déficience dans l'intégration du sens de la parole. Plus particulièrement, SENT_CORE contient 3 régions clés (hubs) de l’intégration et de la diffusion de l'information situées au niveau de l’aire de Broca et de Wernicke. J'ai ensuite appliqué cette méthodologie à l’élaboration d’un atlas des réseaux du traitement du mot. J’ai ainsi identifié 21 régions cérébrales organisées en 2 réseaux distincts, dont un réseau phonologique incluant la boucle audio-motrice. Pour la première fois, une forte connectivité intrinsèque entre la boucle audio-motrice de l’hémisphère gauche et le traitement prosodique situé au niveau du sillon temporal supérieur de l’hémisphère droit a été mis en évidence. Enfin, j'ai développé une nouvelle méthode d’étude de la variabilité de données tridimensionnelles. Cette nouvelle méthode comporte deux outils mathématiques différents se basant sur un algorithme de classification ascendante hiérarchique. Le premier permet d'identifier les variables conduisant à une instabilité des partitions, le second permet d'extraire des sous-populations stables d'une population de départ. Les applications de l’ensemble de ce travail sont nombreuses : j'ai par exemple utilisé le réseau SENT_CORE pour étudier la variabilité interindividuelle de la latéralisation hémisphérique des aires supramodales de la phrase. J’ai ainsi identifié deux groupes de sujets typiques asymétriques gauche pour le langage, avec une forte connectivité intra-hémisphérique gauche et une faible connectivité inter-hémisphérique, ainsi qu’un groupe de sujets atypiques : asymétriques droit pour le langage, présentant une forte connectivité intrinsèque des réseaux du langage dans les deux hémisphères et une forte connectivité inter-hémisphérique. SENSAAS a également été utilisé afin d’étudier le support génétique de l’atypicalité du langage, ainsi que pour la caractérisation topologique des réseaux mnésiques et linguistiques des individus souffrant d'épilepsie temporale. La nouvelle méthode d’évaluation de la variabilité interindividuelle a, elle, été utilisée afin d’évaluer la stabilité des réseaux intrinsèques d’un nouvel atlas fonctionnel adapté aux individus de plus de 55 ansMy thesis work is part of a multi-modal and multi-scale integration approach which has led to the emergence of cognitive and population neuroimaging. More specifically, fMRI provides two types of three-dimensional functional brain maps: activation maps allowing for visualizing brain regions directly involved in a cognitive process, and intrinsic connectivity maps measuring the synchronization between spatially distant but functionally connected regions. I have applied new statistical methodologies to these two types of maps, allowing me to deal with both the individual and the spatial dimensions. In the first part, I designed atlases of brain regions dedicated to specific cognitive functions, based on their hemispheric lateralization and targeting a population selected for its low variability. I present here the first two language atlases. Indeed, although there are many approaches to map language areas in patients, there was no atlas of networks supporting language functions in healthy individuals so far. I first identified left activated and left asymmetrical regions, both during sentence production, listening and reading, in 137 healthy right-handed individuals. Analysis of the intrinsic connectivity between the 32 identified regions reveals that they are part of 3 distinct functional networks, which constitute the SENSAAS (SENtence Supramodal Areas AtlaS) brain atlas. Among these networks, one with 18 regions contains the essential language areas (SENT_CORE), i.e. the brain areas whose lesion leads to an impairment in the integration of the meaning of speech. Specifically, SENT_CORE contains 3 hubs supporting the information integration and dissemination, localized in the Broca and Wernicke area. I then applied this methodology to the elaboration of an atlas of word processing networks. I identified 21 brain regions organized into 2 distinct networks, one of which is a phonological network including the audio-motor loop. For the first time, a strong intrinsic connectivity between the left audio-motor loop and the prosodic processing, located in the upper temporal sulcus of the right hemisphere, is evidenced. Finally, I developed a new method for studying the variability of three-dimensional data. This new method includes two different mathematical tools based on hierarchical agglomerative clustering algorithms. The first one makes it possible to identify variables leading to partition instability, the second one allows for extracting stable sub-populations from a starting population. The applications of all of this work are numerous: for example, I used the SENT_CORE network to study the inter-individual variability of hemispheric lateralization of the sentence supramodal areas. I have thus identified two groups of typical asymmetric left language individuals, with high left intra-hemispheric intrinsic connectivity and low inter-hemispheric connectivity, and a group of atypical individuals: rightward asymmetrical for language, with high intrinsic connectivity of language networks in both hemispheres and high inter-hemispheric connectivity. SENSAAS has also been used to study the genetic support of language atypicality, as well as for the topological characterization of the memory and language networks of individuals with mesial temporal lobe epilepsy. The new method for assessing inter-individual variability was used to evaluate the stability of the intrinsic networks of a new functional atlas adapted for late adulthood
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Pallarés, Picazo Vicente. "Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666806.
Full textAbstract:
Es conocido en la literatura de neuroimagen que las redes cerebrales funcionales reflejan rasgos personales. Estas características individuales, podrían interferir al caracterizar la cognición entendida como la manera en que se coordinan las redes para realizar una tarea, como mantener la atención, recordar, o procesar información visual. Cómo estos aspectos individuales coexisten con mecanismos generales es, por tanto, una pregunta clave en investigación sobre conectividad cerebral. Este trabajo estudia la relación entre marcadores de conectividad específicos tanto de sujetos, como de tareas. Se centra en dos escalas temporales distintas: la variabilidad entre sesiones, y las fluctuaciones rápidas producidas durante una sesión de adquisición. Utilizamos técnicas de machine learning para separar cuantitativamente las contribuciones de información del sujeto y del estado cognitivo a la conectividad. La metodología presentada nos permite extraer aquellas redes representativas de ambas dimensiones, así como profundizar en su evolución, sugiriendo las escalas temporales relevantes en la cognición.És conegut en la literatura de neuroimatge que les xarxes cerebrals funcionals reflecteixen trets personals. Aquestes característiques individuals podrien interferir en caracteritzar la cognició entesa com la manera en què les xarxes es coordinen per realitzar una tasca, com mantenir l'atenció, recordar o processar informació visual. Cóm aquests aspectes individuals coexisteixen amb mecanismes generals, és, per tant, una pregunta clau en recerca sobre connectivitat cerebral. Aquest treball estudia la relació entre marcadors de connectivitat específics tant de subjectes, com de tasques. Se centra en dues escales temporals: la variabilitat entre sessions, i les fluctuacions ràpides produïdes durant una sessió d'adquisició. Utilitzem tècniques de machine learning per separar quantitativament les contribucions d'informació del subjecte i de l'estat cognitiu a la connectivitat. La metodologia presentada ens permet extreure aquelles xarxes representatives d'ambdues dimensions, així com aprofundir en la seva evolució, suggerint les escales temporals rellevants en la cognició.
There is consistent evidence in the neuroimaging literature that functional brain networks reflect personal traits. Individual specificity may interfere with the characterization of cognition, in terms of coordination of brain networks to perform a task, such as sustained attention, memory retrieval or visual information processing. How individual traits coexist with invariant mechanisms is, therefore, a key question in brain connectivity research. This work aims to examine the relationship between subject- and task-specific connectivity signatures. It focuses on two different timescales: day-to-day variability and faster fluctuations exhibited within a scanning session. We adopt a machine learning approach to quantitatively disentangle the contribution of subject information and cognitive state to the connectivity patterns. The proposed methodology allows us to extract the specific brain networks that support each of the two dimensions, as well as to delve into their changes over time, suggesting the relevant timescales for cognition.
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Bartley, Jessica E. "Exploring the Neural Mechanisms of Physics Learning." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3889.
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This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive
the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics.
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Insabato, Andrea. "Neurodynamical theory of decision confidence." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/129463.
Full textAbstract:
Decision confidence offers a window on introspection and onto the evaluation mechanisms associated
with decision-making. Nonetheless we do not have yet a thorough understanding of its neurophysiological and computational substrate.
There are mainly two experimental paradigms to measure decision confidence in animals: post-decision wagering and uncertain option.
In this thesis we explore and try to shed light on the computational mechanisms underlying confidence based decision-making in both experimental paradigms.
We propose that a double-layer attractor neural network can account for neural recordings and behavior of rats in a post-decision wagering experiment. In this model a decision-making layer takes the perceptual decision and a separate confidence layer monitors the activity of the decision-making layer and makes a judgment about the confidence in the decision.
Moreover we test the prediction of the model by analyizing neuronal data from monkeys performing a
decision-making task. We show the existence of neurons in ventral Premotor cortex that encode decision confidence. We also found that both a continuous and discrete encoding of decision confidence are
present in the primate brain. In particular we show that different neurons encode confidence through three different mechanisms: 1. Switch time coding, 2. rate coding and 3. binary coding.
Furthermore we propose a multiple-choice attractor network model in order to account for uncertain option tasks. In this model the confidence emerges from the stochastic dynamics of decision neurons, thus making a separate monitoring network (like in the model of the post-decision wagering task) unnecessary. The model explains the behavioral and neural data recorded in monkeys lateral intraparietal area as a result of the multistable dynamics of the attractor network, whereby it is possible to make several testable predictions.
The rich neurophysiological representation and computational mechanisms of decision confidence evidence the basis of different functional aspects of confidence in the making of a decision.El estudio de la confianza en la decisión ofrece una perspectiva ventajosa sobre los procesos de introspección y sobre los procesos de evaluación de la toma de decisiones. No obstante todav'ia no tenemos un conocimiento exhaustivo del sustrato neurofisiológico y computacional de la confianza en la decisión. Existen principalmente dos paradigmas experimentales para medir la confianza en la decisión en los sujetos no humanos: apuesta post-decisional (post-decision wagering) y opción insegura (uncertain option). En esta tesis tratamos de aclarar los mecanísmos computacionales que subyacen a los procesos de toma de decisiones y juicios de confianza en ambos paradigmas experimentales. El modelo que proponemos para explicar los experimentos de apuesta post-decisional es una red neuronal de atractores de dos capas. En este modelo la primera capa se encarga de la toma de decisiones, mientras la segunda capa vigila la actividad de la primera capa y toma un juicio sobre la confianza en la decisión. Sucesivamente testeamos la predicción de este modelo analizando la actividad de neuronas registrada en el cerebro de dos monos, mientras estos desempeñaban una tarea de toma de decisiones. Con este análisis mostramos la existencia de neuronas en la corteza premotora ventral que codifican la confianza en la decisión. Nuestros resultados muestran también que en el cerebro de los primates existen tanto neuronas que codifican confianza como neuronas que la codifican de forma continua. Más en específico mostramos que existen tres mecanismos de codificación: 1. codificación por tiempo de cambio, 2. codificación por tasa de disparo, 3. codificación binaria. En relación a las tareas de opción insegura proponemos un modelo de red de atractores para opciones multiplas. En este modelo la confianza emerge de la dinámica estocástica de las neuronas de decisión, volviéndose así innecesaria la supervisión del proceso de toma de decisiones por parte de otra red (como en el modelo de la tarea de apuesta post-decisional). El modelo explica los datos de comportamiento de los monos y los registros de la actividad de neuronas del área lateral intraparietal como efectos de la dinámica multiestable de la red de atractores. Además el modelo produce interesantes y novedosas predicciones que se podrán testear en experimentos futuros. La compleja representación neurofisiológica y los distintos mecanísmos computacionales que emergen de este trabajo sugieren distintos aspectos funcionales de la confianza en la toma de decisiones.
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Sreenivasan, Varsha. "Structural connectivity correlates of human cognition explored with diffusion MRI and tractography." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5228.
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Intact structural connectivity among brain regions is critical to cognition. Structural connectivity forms the substratum for information flow between brain regions, and its plasticity is a hallmark of learning in the brain. Moreover, structural connectivity markers constitute a heritable phenotype. Investigating neuroanatomical connectivity in the human brain is, therefore, critical not only for uncovering the neural underpinnings of behavior but also for understanding connectomic bases of neurodevelopmental and neurodegenerative disorders, such as autism and Alzheimer’s Disease. Diffusion magnetic resonance imaging (dMRI) and tractography are among the only techniques, at present, that enable estimation of anatomical connectivity in the human brain, in-vivo. By tracking the anisotropic diffusion of water molecules in white matter, dMRI and tractography enable post hoc reconstruction of contiguous fascicles between distal brain regions. How accurately can dMRI and tractography track these connections to match ground-truth in the brain? Are structural connections between specific pairs of brain regions informative about subjects’ cognitive capacities, like attention? Could changes in these connections indicate mechanisms of cognitive decline, both in healthy and pathologically aging populations?
In this thesis, I report results from three studies, each of which addresses one of these key questions. In the first study, I explored how the midbrain contributes to attention, by combining model-based analysis of behavior with dMRI-tractography. Specifically, I examined the role of the superior colliculus (SC), a vertebrate midbrain structure, in attention. Does the SC control perceptual sensitivity to attended information, does it enable biasing choices toward attended information, or both? I mapped structural connections of the human SC with neocortical regions and found that the strengths of these connections correlated with, and were strongly predictive of, individuals’ choice bias, but not sensitivity. Taken together with previous studies, these results indicate that the human SC may play an evolutionarily conserved role in controlling choice bias during visual attention.
In the second study, I developed a novel approach, implemented on GPUs, for pruning structural connectomes, at scale. First, I identified key limitations of a state-of-the-art connectome pruning technique, Linear Fascicle Evaluation (LiFE), and introduced a GPU-based implementation that achieves >100x speedups over conventional CPU-based implementations. Leveraging these speedups, I advanced LiFE’s algorithm by imposing a regularization constraint on estimated fiber weights. This regularized, accelerated, LiFE algorithm (“ReAl-LiFE”) estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal, and enables rapid and accurate connectome evaluation at scale.
In the third study, I demonstrated several real-world applications of the ReAl-LiFE technique for analysis of large datasets. First, I showed that structural connectivity estimated with ReAl-LiFE predicts behavioral scores across a range of cognitive tasks in a cohort with 200 healthy human volunteers from the Human Connectome Project database. Moreover, ReAl-LiFE pruned connection weights provided a more reliable marker for structural connectivity strength than the number of fibers in the unpruned connectome. Second, ReAl-LiFE connection weights effectively predicted both chronological age, as well as age-related decline in cognitive factor scores in a cohort of 101 healthy, aged volunteers whose data were acquired as part of the Tata longitudinal study on aging at IISc. Finally, analyzing nearly 100 dMRI scans from the ADNI database, I showed that ReAl-LiFE outperformed competing approaches in terms of its accuracy with classifying patients with Alzheimer’s Dementia from healthy, age-matched controls, based on cortico-hippocampal connection weights.
In summary, these findings show that diffusion MRI and tractography can serve as powerful tools for addressing key questions regarding brain-behavior relationships. In this thesis, I developed a technique to reliably estimate structural connectivity between distal brain regions, identified the role of subcortical structural connections in attention, and showed that cortical connectivity can be used to predict behavioral scores and cognitive decline. Broadly, these results will be relevant for understanding the connectomic basis of various cognitive processes, like attention, in healthy populations, and its dysfunction in diseased patients.
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Berteau, Stefan André. "Modeling biophysical and neural circuit bases for core cognitive abilities evident in neuroimaging patterns: hippocampal mismatch, mismatch negativity, repetition positivity, and alpha suppression of distractors." Thesis, 2018. https://hdl.handle.net/2144/27671.
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This dissertation develops computational models to address outstanding problems in the domain of expectation-related cognitive processes and their neuroimaging markers in functional MRI or EEG. The new models reveal a way to unite diverse phenomena within a common framework focused on dynamic neural encoding shifts, which can arise from robust interactive effects of M-currents and chloride currents in pyramidal neurons. By specifying efficient, biologically realistic circuits that achieve predictive coding (e.g., Friston, 2005), these models bridge among neuronal biophysics, systems neuroscience, and theories of cognition.
Chapter one surveys data types and neural processes to be examined, and outlines the Dynamically Labeled Predictive Coding (DLPC) framework developed during the research. Chapter two models hippocampal prediction and mismatch, using the DLPC framework. Chapter three presents extensions to the model that allow its application for modeling neocortical EEG genesis. Simulations of this extended model illustrate how dynamic encoding shifts can produce Mismatch Negativity (MMN) phenomena, including pharmacological effects on MMN reported for humans or animals.
Chapters four and five describe new modeling studies of possible neural bases for alpha-induced information suppression, a phenomenon associated with active ignoring of stimuli. Two models explore the hypothesis that in simple rate-based circuits, information suppression might be a robust effect of neural saturation states arising near peaks of resonant alpha oscillations. A new proposal is also introduced for how the basal ganglia may control onset and offset of alpha-induced information suppression. Although these rate models could reproduce many experimental findings, they fell short of reproducing a key electrophysiological finding: phase-dependent reduction in spiking activity correlated with power in the alpha frequency band.
Therefore, chapter five also specifies how a DLPC model, adapted from the neocortical model developed in chapter three, can provide an expectation-based model of alpha-induced information suppression that exhibits phase-dependent spike reduction during alpha-band oscillations. The model thus can explain experimental findings that were not reproduced by the rate models. The final chapter summarizes main theses, results, and basic research implications, then suggests future directions, including expanded models of neocortical mismatch, applications to artificial neural networks, and the introduction of reward circuitry.
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Kriete, Trenton E. "Impaired cognitive flexibility and intact cognitive control in autism a computational cognitive neuroscience approach /." Diss., 2005. http://etd.library.vanderbilt.edu/ETD-db/available/etd-04012005-125030/.
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Murugan, Rohini. "Computational mechanisms underlying eye-head coordination." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5536.
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Gaze or eye-head coordination is an ethologically and physiologically important process that directs the visual system to objects of interest. Due to its relative simplicity, it is an ideal system to study multi-effector coordination. Whereas in the past, gaze control models have been developed on experimental data that focused on the mean responses, I used variability as a tool to study and model temporal coordination, spatial coordination and tested stochastic models of gaze control to test the predictions of the independent, common and interactive models of gaze control. I also used McLaughlin’s task paradigm of gaze adaptation to study gaze coordination under more dynamic conditions and studied its implication for models of gaze control. In the context of temporal coordination, the results suggest a common movement preparation phase underlying eye and head movement initiation. In the case of spatial coordination, I found evidence for the presence of a global gaze error feedback. However, the gaze learning results, which showed effector-specific learning suggested the presence of separate controllers that contribute unequally to gaze adaptation. Taken together, my results collectively suggest that an interactive model, that has a separate internal saccadic feedback controller and a global gaze feedback controller maybe the best model that explains my results.
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GUAZZINI, ANDREA. "Computational models of cognitive activity: from neural to social dynamics." Doctoral thesis, 2009. http://hdl.handle.net/2158/822745.
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Lin, Yao. "FMRI of a Visual Patterns N-back Task in Typical Development." Thesis, 2012. http://hdl.handle.net/1807/33290.
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The term working memory (WM) refers to a set of cognitive processes that allows for the temporary storage and manipulation of information. Neural correlates of the N- back task, a well-established WM measure used in neuroimaging, have been studied extensively in adults but less so in developmental populations. This thesis determines the effect of age on brain activations that mediate cognitive processes for remembering non- verbal/visual stimuli. Block-design fMRI was used to record activity in 84 subjects (6-35 years) during a visual-patterns 0- and 1-back task. Regions activated during the 1-back condition were largely common to all age groups, with adults displaying the largest extent of activations. Children and adolescents showed similar 0-back activations (distinct from 1-back) while adults engaged an analogous 1-back activation pattern during 0-back, suggesting that brain mechanisms underlying memory and attentional processes required for this task in children and adolescents are not yet mature and that strategy usage is still developing.
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Hassall, Cameron Dale. "Learning in Non-Stationary Environments." 2013. http://hdl.handle.net/10222/36240.
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Real-world decision making is challenging due, in part, to changes in the underlying reward structure: the best option last week may be less rewarding today. Determining the best response is even more challenging when feedback validity is low. Presented here are the results of two experiments designed to determine the degree to which midbrain reward processing is responsible for detecting reward contingency changes when feedback validity is low. These results suggest that while midbrain reward systems may be involved in detecting unexpected uncertainty in non-stationary environments, other systems are likely involved when feedback validity is low – namely, the locus-coeruleus-norepinephrine system. Finally, a computational model that combines these systems is described and tested. Taken together, these results downplay the role of the midbrain reward system when feedback validity is low, and highlight the importance of the locus-coeruleus-norepinephrine system in detecting reward contingency changes.
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Prince, Steven Eric. "Functional Neuroimaging Investigations of Human Memory Comparisons of Successful Encoding and Retrieval for Relational and Item Information." Diss., 2007. http://hdl.handle.net/10161/201.
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Conroy, Susan Kim. "Chemotherapy, estrogen, and cognition : neuroimaging and genetic variation." Thesis, 2014. http://hdl.handle.net/1805/4027.
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Indiana University-Purdue University Indianapolis (IUPUI)The time course and biological mechanisms by which breast cancer (BC) and/or alterations in estrogen status lead to cognitive and brain changes remain unclear. The studies presented here use neuroimaging, cognitive testing, genetics, and biomarkers to investigate how post-chemotherapy interval (PCI), chemotherapy-induced amenorrhea (CIA), and genetic variation in the estrogen pathway affect the brain. Chapter 1 examines the association of post-chemotherapy interval (PCI) with gray matter density (GMD) and working memory-related brain activation in BC survivors (mean PCI 6.4, range 3-10 years). PCI was positively associated with GMD and activation in the right frontal lobe, and GMD in this region was correlated with global neuropsychological function. In regions where BC survivors showed decreased GMD compared to controls, this was inversely related to oxidative DNA damage and learning and memory scores. This is the first study to show neural effects of PCI and relate DNA damage to brain alterations in BC survivors. Chapter 2 demonstrates prospectively, in an independent cohort, decreased combined magnitudes of brain activation and deactivation from pre-to post-chemotherapy in patients undergoing CIA compared to both postmenopausal BC patients undergoing chemotherapy and healthy controls. CIA’s change in activity magnitude was strongly correlated with change in processing speed, suggesting this activity increase reflects effective cognitive compensation. These results demonstrate that the pattern of change in brain activity from pre- to post-chemotherapy varies according to pre-treatment menopausal status. Chapter 3 presents the effects of variation in ESR1, the gene that codes for estrogen receptor-α, on brain structure in healthy older adults. ESR1 variation was associated with hippocampus and amygdala volumes, particularly in females. Single nucleotide polymorphism (SNP) rs9340799 influenced cortical GMD and thickness differentially by gender. Apolipoprotein E (APOE)-ε4 carrier status modulated the effect of SNP rs2234693 on amygdala volumes in women. This study showed that genetic variation in estrogen relates to brain morphology in ways that differ by sex, brain region and APOE-ε4 carrier status. The three studies presented here explore the interplay of BC, estrogen, and cognition, showing that PCI, CIA, and ESR1 genotype influence brain phenotypes. Cognitive correlates of neuroimaging findings indicate potential clinical significance of these results.
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(9790292), Michael Cvirn. "The effects of temperature, sleep restriction, and physical activity on the sleep architecture and cognitive performance of volunteer firefighters during various simulation wildland fireground tours." Thesis, 2018. https://figshare.com/articles/thesis/The_effects_of_temperature_sleep_restriction_and_physical_activity_on_the_sleep_architecture_and_cognitive_performance_of_volunteer_firefighters_during_various_simulation_wildland_fireground_tours/13447676.
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The overall aim of this thesis was to investigate the interactions between firefighters’ sleep, ambient temperature, hydration status, and cognitive performance during simulated single and multi-day wildfire suppressions. These exact aims are addressed through four studies:
1.To assess the effect of ambient heat during day-(33-35°C) and night-time (23-25°C) exposures on firefighters’ sleep quantity and quality during a simulated multi-day wildfire suppression compared to thermoneutral temperatures (18-20°C; Study 1 -Chapter 4).
2.To quantify the effect of sleep restriction in either ambient heat with day- (33-35°C) and night-time (23-25°C) exposures or thermoneutral conditions (18-20°C) on firefighters’ sleep architecture during a simulated multi-day wildfire suppression compared to a control condition with normal sleep in temperate conditions (18-20°C; Study 2 –Chapter 5).
3.To examine the association between firefighters’ hydration status and cognitive performance during a simulated prolonged wildfire suppression shift in the heat (33-35°C) compared to thermoneutral temperatures (18-20°C; Study 3 –Chapter 6).
4.To examine the effect on cognitive performance of sleep restriction in either ambient heat with day-(33-35°C) and night-time (18-20°C) temperature changes or temperate conditions (18-20°C) during a simulated multi-day wildfire suppression compared to a control condition with full sleep opportunities in thermoneutral temperatures (18-20°C; Study 4 –Chapter 7).
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Thompson, Jessica A. F. "Characterizing and comparing acoustic representations in convolutional neural networks and the human auditory system." Thesis, 2020. http://hdl.handle.net/1866/24665.
Full textAbstract:
Le traitement auditif dans le cerveau humain et dans les systèmes informatiques consiste en une cascade de transformations représentationnelles qui extraient et réorganisent les informations pertinentes pour permettre l'exécution des tâches. Cette thèse s'intéresse à la nature des représentations acoustiques et aux principes de conception et d'apprentissage qui soutiennent leur développement. Les objectifs scientifiques sont de caractériser et de comparer les représentations auditives dans les réseaux de neurones convolutionnels profonds (CNN) et la voie auditive humaine. Ce travail soulève plusieurs questions méta-scientifiques sur la nature du progrès scientifique, qui sont également considérées.
L'introduction passe en revue les connaissances actuelles sur la voie auditive des mammifères et présente les concepts pertinents de l'apprentissage profond. Le premier article soutient que les questions philosophiques les plus pressantes à l'intersection de l'intelligence artificielle et biologique concernent finalement la définition des phénomènes à expliquer et ce qui constitue des explications valables de tels phénomènes. Je surligne les théories pertinentes de l'explication scientifique que j’espére fourniront un échafaudage pour de futures discussions. L'article 2 teste un modèle populaire de cortex auditif basé sur des modulations spectro-temporelles. Nous constatons qu'un modèle linéaire entraîné uniquement sur les réponses BOLD aux ondulations dynamiques simples (contenant seulement une fréquence fondamentale, un taux de modulation temporelle et une échelle spectrale) peut se généraliser pour prédire les réponses aux mélanges de deux ondulations dynamiques. Le troisième article caractérise la spécificité linguistique des couches CNN et explore l'effet de l'entraînement figé et des poids aléatoires. Nous avons observé trois régions distinctes de transférabilité: (1) les deux premières couches étaient entièrement transférables, (2) les couches 2 à 8 étaient également hautement transférables, mais nous avons trouvé évidence de spécificité de la langue, (3) les couches suivantes entièrement connectées étaient plus spécifiques à la langue mais pouvaient être adaptées sur la langue cible. Dans l'article 4, nous utilisons l'analyse de similarité pour constater que la performance supérieure de l'entraînement figé obtenues à l'article 3 peuvent être attribuées aux différences de représentation dans l'avant-dernière couche: la deuxième couche entièrement connectée. Nous analysons également les réseaux aléatoires de l'article 3, dont nous concluons que la forme représentationnelle est doublement contrainte par l'architecture et la forme de l'entrée et de la cible. Pour tester si les CNN acoustiques apprennent une hiérarchie de représentation similaire à celle du système auditif humain, le cinquième article compare l'activité des réseaux «freeze trained» de l'article 3 à l'activité IRMf 7T dans l'ensemble du système auditif humain. Nous ne trouvons aucune évidence d'une hiérarchie de représentation partagée et constatons plutôt que tous nos régions auditifs étaient les plus similaires à la première couche entièrement connectée. Enfin, le chapitre de discussion passe en revue les mérites et les limites d'une approche d'apprentissage profond aux neurosciences dans un cadre de comparaison de modèles.
Ensemble, ces travaux contribuent à l'entreprise naissante de modélisation du système auditif avec des réseaux de neurones et constituent un petit pas vers une science unifiée de l'intelligence qui étudie les phénomènes qui se manifestent dans l'intelligence biologique et artificielle.Auditory processing in the human brain and in contemporary machine hearing systems consists of a cascade of representational transformations that extract and reorganize relevant information to enable task performance. This thesis is concerned with the nature of acoustic representations and the network design and learning principles that support their development. The primary scientific goals are to characterize and compare auditory representations in deep convolutional neural networks (CNNs) and the human auditory pathway. This work prompts several meta-scientific questions about the nature of scientific progress, which are also considered. The introduction reviews what is currently known about the mammalian auditory pathway and introduces the relevant concepts in deep learning.The first article argues that the most pressing philosophical questions at the intersection of artificial and biological intelligence are ultimately concerned with defining the phenomena to be explained and with what constitute valid explanations of such phenomena. I highlight relevant theories of scientific explanation which we hope will provide scaffolding for future discussion. Article 2 tests a popular model of auditory cortex based on frequency-specific spectrotemporal modulations. We find that a linear model trained only on BOLD responses to simple dynamic ripples (containing only one fundamental frequency, temporal modulation rate, and spectral scale) can generalize to predict responses to mixtures of two dynamic ripples. Both the third and fourth article investigate how CNN representations are affected by various aspects of training. The third article characterizes the language specificity of CNN layers and explores the effect of freeze training and random weights. We observed three distinct regions of transferability: (1) the first two layers were entirely transferable between languages, (2) layers 2--8 were also highly transferable but we found some evidence of language specificity, (3) the subsequent fully connected layers were more language specific but could be successfully finetuned to the target language. In Article 4, we use similarity analysis to find that the superior performance of freeze training achieved in Article 3 can be largely attributed to representational differences in the penultimate layer: the second fully connected layer. We also analyze the random networks from Article 3, from which we conclude that representational form is doubly constrained by architecture and the form of the input and target. To test whether acoustic CNNs learn a similar representational hierarchy as that of the human auditory system, the fifth article presents a similarity analysis to compare the activity of the freeze trained networks from Article 3 to 7T fMRI activity throughout the human auditory system. We find no evidence of a shared representational hierarchy and instead find that all of our auditory regions were most similar to the first fully connected layer. Finally, the discussion chapter reviews the merits and limitations of a deep learning approach to neuroscience in a model comparison framework. Together, these works contribute to the nascent enterprise of modeling the auditory system with neural networks and constitute a small step towards a unified science of intelligence that studies the phenomena that are exhibited in both biological and artificial intelligence.
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(10514360), Uttara Vinay Tipnis. "Data Science Approaches on Brain Connectivity: Communication Dynamics and Fingerprint Gradients." Thesis, 2021.
Find full textAbstract:
The innovations in Magnetic Resonance Imaging (MRI) in the recent decades have given rise to large open-source datasets. MRI affords researchers the ability to look at both structure and function of the human brain. This dissertation will make use of one of these large open-source datasets, the Human Connectome Project (HCP), to study the structural and functional connectivity in the brain.
Communication processes within the human brain at different cognitive states are neither well understood nor completely characterized. We assess communication processes in the human connectome using ant colony-inspired cooperative learning algorithm, starting from a source with no a priori information about the network topology, and cooperatively searching for the target through a pheromone-inspired model. This framework relies on two parameters, namely pheromone and edge perception, to define the cognizance and subsequent behaviour of the ants on the network and the communication processes happening between source and target. Simulations with different configurations allow the identification of path-ensembles that are involved in the communication between node pairs. In order to assess the different communication regimes displayed on the simulations and their associations with functional connectivity, we introduce two network measurements, effective path-length and arrival rate. These measurements are tested as individual and combined descriptors of functional connectivity during different tasks. Finally, different communication regimes are found in different specialized functional networks. This framework may be used as a test-bed for different communication regimes on top of an underlying topology.
The assessment of brain fingerprints has emerged in the recent years as an important tool to study individual differences. Studies so far have mainly focused on connectivity fingerprints between different brain scans of the same individual. We extend the concept of brain connectivity fingerprints beyond test/retest and assess fingerprint gradients in young adults by developing an extension of the differential identifiability framework. To do so, we look at the similarity between not only the multiple scans of an individual (subject fingerprint), but also between the scans of monozygotic and dizygotic twins (twin fingerprint). We have carried out this analysis on the 8 fMRI conditions present in the Human Connectome Project -- Young Adult dataset, which we processed into functional connectomes (FCs) and time series parcellated according to the Schaefer Atlas scheme, which has multiple levels of resolution. Our differential identifiability results show that the fingerprint gradients based on genetic and environmental similarities are indeed present when comparing FCs for all parcellations and fMRI conditions. Importantly, only when assessing optimally reconstructed FCs, we fully uncover fingerprints present in higher resolution atlases. We also study the effect of scanning length on subject fingerprint of resting-state FCs to analyze the effect of scanning length and parcellation. In the pursuit of open science, we have also made available the processed and parcellated FCs and time series for all conditions for ~1200 subjects part of the HCP-YA dataset to the scientific community.
Lastly, we have estimated the effect of genetics and environment on the original and optimally reconstructed FC with an ACE model.
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Vetter, Nora. "Theory of Mind Development in Adolescence and its (Neuro)cognitive Mechanisms." Doctoral thesis, 2012. https://tud.qucosa.de/id/qucosa%3A26820.
Full textAbstract:
Theory of Mind (ToM) is the ability to infer others’ mental states and thus to predict their behavior (Perner, 1991). Therefore, ToM is essential for the adequate adjustment of behavior in social situations. ToM can be divided into: 1) cognitive ToM encompassing inferences about intentions and beliefs and 2) affective ToM encompassing inferences about emotions (Shamay-Tsoory, Harari, Aharon-Peretz, & Levkovitz, 2010). Well-functioning skills of both ToM aspects are much-needed in the developmental period of adolescence because in this age phase peer relationships become more important and romantic relationships arise (Steinberg & Morris, 2001). Importantly, affective psychopathological disorders often have their onset in adolescence. ToM development in adolescence might be based on underlying cognitive mechanisms such as the ability to inhibit one’s own thoughts in order to understand another person’s thoughts (Carlson & Moses, 2001). Another possible mechanism relates to functional brain development across adolescence (Blakemore, 2008). Therefore, neurocognitive mechanisms may underlie ongoing ToM development in adolescence. First studies indicate an ongoing behavioral and functional brain development of ToM (e.g. Blakemore, 2008). However, ToM development in adolescence and how this might relate to underlying (neuro)cognitive functions remains largely underexamined.
The major aims of the current thesis were first to answer the overall question whether there is an ongoing development of ToM in adolescence. This question relates to both behavioral and functional brain development. As a second major aim, the present work sought to elucidate possible (neuro)cognitive mechanisms of ongoing ToM development across adolescence. Specifically, these cognitive mechanisms might be basic cognitive functions as well as executive functions. Additionally, the present work aimed at exploring potential (neuro)cognitive mechanisms through an integration of both behavioral and functional brain studies.
The current experimental work spans three cross-sectional studies investigating adolescents (aged around 12-15 years) and young adults (aged around 18-22 years) to examine for the first time both the behavioral (studies I and II) and functional brain development of ToM (study III) in adolescence and its underlying (neuro)cognitive mechanisms. In all three studies, more complex, advanced ToM tasks were employed to avoid ceiling effects. Study I was aimed at investigating if cognitive and affective ToM continues to develop in adolescence and at exploring if basic cognitive variables such as verbal ability, speed of processing, and working memory capacity underlie such development. Hence, two groups of adolescents and young adults completed tasks of ToM and basic cognitive abilities. Large age effects were revealed on both measures of ToM: adolescents performed lower than adults. These age differences remained significant after controlling for basic cognitive variables. However, verbal ability covaried with performance in affective ToM. Overall, results support the hypothesis of an ongoing development of ToM from adolescence to adulthood on both cognitive and affective aspects. Results may further indicate verbal ability being a basic cognitive mechanism of affective ToM.
Study II was designed to further explore if affective ToM, as measured with a dynamic realistic task, continues to develop across adolescence. Importantly, this study sought to explore executive functions as higher cognitive mechanisms of developing affective ToM across adolescence. A large group spanning adolescents and young adults evaluated affective mental states depicted by actors in video clips. Additionally, participants were examined with three subcomponents of executive functions, inhibition, updating, and shifting following the classification of Miyake et al. (2000). Affective ToM performance was positively related to age and all three executive functions. Specifically, inhibition explained the largest amount of variance in age related differences of affective ToM performance. Overall, these results indicate the importance of inhibition as key underlying mechanism of developing an advanced affective ToM in adolescence.
Study III set out to explore the functional brain development of affective ToM in adolescence by using functional magnetic resonance imaging (fMRI). The affective ToM measure was the behavioral developmentally sensitive task from study II. An additional control condition consisted of the same emotional stimuli with the instruction to focus on physical information. This study faced methodical challenges of developmental fMRI studies by matching performance of groups. The ventromedial prefrontal cortex (vMPFC) was significantly less deactivated in adolescents in comparison to adults, which might suggest that adolescents seem to rely more on self-referential processes for affective ToM. Furthermore, adolescents compared to adults showed greater activation in the dorsolateral prefrontal cortex (DLPFC) in the control condition, indicating that adolescents might be distracted by the emotional content and therefore needed to focus more on the physical content of the stimulus. These findings suggest affective ToM continues to develop on the functional brain level and reveals different underlying neurocognitive strategies for adolescents in contrast to adults.
In summary, the current thesis investigated whether ToM continues to develop in adolescence until young adulthood and explored underlying (neuro)cognitive mechanisms. Findings suggest that there is indeed an ongoing development of both the cognitive and affective aspect of ToM, which importantly contributes to the conceptual debate. Moreover, the second benefit to the debate is to demonstrate how this change may occur. As a basic cognitive mechanism verbal ability and as an executive functioning mechanism inhibition was revealed. Furthermore, neurocognitive mechanisms in form of different underlying neurocognitive strategies of adolescents compared to adults were shown. Taken together, ToM development in adolescence seems to mirror a different adaptive cognitive style in adolescence (Crone & Dahl, 2012). This seems to be important for solving the wealth of socio-emotional developmental tasks that are relevant for this age span.:Abstract 1
1 General Introduction 4
1.1 Concept of ToM: cognitive and affective aspects 7
1.2 ToM Development 8
1.2.1 ToM Development until Adolescence 9
1.2.2 ToM Development in Adolescence 12
1.3 Cognitive Mechanisms 14
1.3.1 Basic Cognitive Functions 15
1.3.2 Executive Functions 17
1.4 Neurocognitive Mechanisms 19
1.4.1 Functional brain development of ToM 20
1.4.2 Integrating behavioral and functional brain studies 21
2 Outline and Central Questions 24
2.1 Does ToM continue to develop in adolescence? 24
2.1.1 Does ToM continue to develop on the behavioral level? 24
2.1.2 Does ToM continue to develop on the level of brain function? 25
2.2 What are (neuro)cognitive mechanisms of ToM development in adolescence? 26
2.2.1 What are basic cognitive and executive functioning mechanisms? 26
2.2.2 Can mechanisms be concluded from the integration of behavioral data and functional brain processes? 26
3 Study I – ToM Development in Adolescence and its Basic Cognitive Mechanisms 28
3.1 Introduction 28
3.2 Method 32
3.2.1 Participants 32
3.2.2 Materials 33
3.3 Results 36
3.3.1 Age Effects 36
3.3.2 Influence of puberty on social cognition 37
3.3.3 Controlling for Basic Cognitive Abilities 39
3.4 Discussion 40
3.4.1 Overview 40
3.4.2 Age differences in social cognition 40
3.4.3 Influence of puberty on social cognition 42
3.4.4 Covariates of age differences in social cognition 42
3.4.5 Conclusions 43
4 Study II – ToM Development in Adolescence and its Executive Functioning Mechanisms 45
4.1 Introduction 45
4.2 Method 49
4.2.1 Participants 49
4.2.2 Materials 49
4.3 Results 52
4.3.1 Decomposing the Age Effect in Affective Theory of Mind 54
4.4 Discussion 55
4.4.1 Overview 55
4.4.2 Conclusions 57
5 Study III – ToM Development in Adolescence and its Neurocognitive Mechanisms 59
5.1 Introduction 59
5.2 Method 61
5.2.1 Participants 61
5.2.2 Stimuli, design and procedure 62
5.2.3 Statistical analysis of behavioral data 65
5.2.4 Functional imaging 65
5.2.5 Statistical analysis of fMRI data 66
5.3 Results 67
5.3.1 Behavioral results 67
5.3.2 fMRI results 68
5.4 Discussion 71
5.4.1 Developmental differences in brain activations 71
5.4.2 Conclusions 74
6 General Discussion 75
6.1 Summary of empirical findings 75
6.2 Discussion and integration of the main empirical findings 76
6.2.1 Continued ToM development in adolescence 76
6.2.2 (Neuro)cognitive mechanisms of ToM development in adolescence 80
6.3 Implications and outlook 89
6.3.1 Current findings and their conceptual fit to present models of ToM 90
6.3.2 Underpinning the concept of cognitive and affective ToM 91
6.3.3 Conceptual and methodical implications of performance matching 92
6.3.4 The role of puberty on ToM 94
6.3.5 Predicting other’s economic behavior 95
6.3.6 Structural brain development 96
6.3.7 Applied perspective 97
6.4 Summary 98
References 99