Executive functions, like the capacity to control and organize thoughts and behavior, develop from childhood to young adulthood. Although task switching and working memory processes are known to undergo strong developmental changes from childhood to adulthood, it is currently unknown how task switching processes are modulated between childhood and adulthood given that working memory processes are central to task switching. The aim of the current study is therefore to examine this question using a combined cue- and memory-based task switching paradigm in children (N = 25) and young adults (N = 25) in combination with neurophysiological (EEG) methods. We obtained an unexpected paradoxical effect suggesting that memory-based task switching is better in late childhood than in young adulthood. No group differences were observed in cue-based task switching. The neurophysiological data suggest that this effect is not due to altered attentional selection (P1, N1) or processes related to the updating, organization, and implementation of the new task-set (P3). Instead, alterations were found in the resolution of task-set conflict and the selection of an appropriate response (N2) when a task has to be switched. Our observation contrasts findings showing that cognitive control mechanisms reach their optimal functioning in early adulthood.

Accelerated long-term forgetting (ALF) is a recently described memory impairment associated with epilepsy. Patients with ALF appear to learn and initially retain new information normally, but forget it at an accelerated rate over subsequent days. ALF can have a profound impact on the lives of the people who suffer from it, but it is also of theoretical interest. In particular, the study of this disorder may provide insight into the mechanisms of memory consolidation. ALF is especially prevalent in transient epileptic amnesia (TEA), an epileptic syndrome in which the seizure focus is thought to be the medial temporal lobes (MTL). The MTL house the hippocampus and a number of other structures critical for declarative memory function. The aims of this doctoral thesis were to investigate which aspects of memory function are disrupted in patients with TEA-associated ALF, and to shed light on the neural basis of the memory impairment. Slow wave sleep (i.e. deep sleep) is known to exacerbate epileptic activity. It is also thought to play a key role in the consolidation of declarative memory. The most commonly posited explanation of ALF is the disruption of sleep- dependent memory consolidation. However, it remains possible that ALF is caused by a subtle problem with encoding that usually goes undetected until delayed memory tests. The results of this thesis demonstrate that sleep can actually benefit memory retention in TEA ALF patients just as much as it does in healthy people, and that it is not necessary for the retention interval to contain sleep in order for ALF to be seen. However, the relationship between slow wave sleep and memory was found to be abnormal in these patients. The amount of slow wave sleep, and the power in the slow oscillation frequency range, during the post-learning night correlated negatively with the benefit of that night of sleep for memory retention. Furthermore, resting-state brain activity patterns thought to reflect post-encoding memory reprocessing were found to correlate negatively with subsequent memory performance in these patients. Another chapter of this thesis provides evidence that TEA ALF patients encode memories abnormally; these patients showed reduced activity in the left hippocampus while viewing stimuli that they went on to forget. Furthermore, this encoding-related brain activity correlated with their long-term forgetting. The final experimental chapter reports a correlation in these patients between grey matter in the left hippocampus and long-term forgetting, which cannot entirely account for the encoding-related brain activity results. The hippocampus and its surrounding structures are thought to be critical to our ability to discriminate between similar stimuli and events. An intriguing hypothesis consistent with the pattern of results in this thesis is that ALF is caused by a functional impairment of the MTL that results in a diminished capacity to distinguish between similar experiences, ultimately causing memory problems; abnormally formed memories may interact with new material and memory consolidation processes in an aberrant manner, leading to retrieval deficits.

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.

Our current knowledge about the relationship between linguistic skills and psychological disorders is somewhat diffuse. One reason is because it is difficult to investigate this relationship without including conditions that clearly influence the results in one way or another (e.g. culture, environment, socioeconomic class etc). This study aims to investigate the relationship in an attempt to highlight a potential link. By using the lens of several fields altogether; cognitive science, linguistics, neuroscience, neurocognition, this study shed light on the relationship and encourage further studies in this field to determine the role of linguistic skills in mental health in general. In the experiment, linguistic skills were measured opposed to depression as a specific disorder to quantify specific data. Linguistic skills were measured by density and diversity and PHQ-9 survey question were used to determine depression scale. Statistical analysis showed significant correlations between some measures of linguistic skills and PHQ-9. The significant statistical correlation found points towards the hypothesis that, better linguistic skills promote well-being, and psychological disorders take minor effect relative to poorer linguistic skills. This topic is large-scaled which means that background variables must be acknowledged thoroughly, which due to the extent of this thesis, were not. The results are discussed further as well as limitations of the study. Improvements for further research are proposed.

Contemporary understanding of human visual spatial attention rests on the hypothesis of a top-down control sending from cortical regions carrying higher-level functions to sensory regions. Evidence has been gathered through functional Magnetic Resonance Imaging (fMRI) experiments. The Frontal Eye Field (FEF) and IntraParietal Sulcus (IPS) are candidates proposed to form the frontoparietal attention network for top-down control. In this work we examined the influence patterns between frontoparietal network and Visual Occipital Cortex (VOC) using a statistical measure, Granger Causality (GC), with fMRI data acquired from subjects participated in a covert attention task. We found a directional asymmetry in GC between FEF/IPS and VOC, and further identified retinotopically specific control patterns in top-down GC. This work may lead to deeper understanding of goal-directed attention, as well as the application of GC to analyzing higher-level cognitive functions in healthy functioning human brain. / by Wei Tang. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

The current thesis addresses some of the unresolved predictions of recent models of the semantic brain system, such as the hub-and-spokes model. In particular, we tackle different aspects of the hypothesis that a widespread network of interacting heteromodal (hub(s)) and unimodal (spokes) cortices underlie semantic cognition. For this purpose, we use connectivity analyses, measures of graph theory and permutation-based statistics with source reconstructed Electro-/MagnetoEncephaloGraphy (EEG/MEG) data in order to track dynamic modulations of activity and connectivity within the semantic networks while a concept unfolds in the brain. Moreover, in order to obtain more accurate connectivity estimates of the semantic networks, we propose novel methods for some of the challenges associated with EEG/MEG connectivity analysis in source space. We utilised data-driven analyses of EEG/MEG recordings of visual word recognition paradigms and found that: 1) Bilateral Anterior Temporal Lobes (ATLs) acted as potential processor hubs for higher-level abstract representation of concepts. This was reflected in modulations of activity by multiple contrasts of semantic variables; 2) ATL and Angular Gyrus (AG) acted as potential integrator hubs for integration of information produced in distributed semantic areas. This was observed using Dynamic Causal Modelling of connectivity among the main left-hemispheric candidate hubs and modulations of functional connectivity of ATL and AG to semantic spokes by word concreteness. Furthermore, examining whole-brain connectomes using measures of graph theory revealed modules in the right ATL and parietal cortex as global hubs; 3) Brain oscillations associated with perception and action in low-level cortices, in particular Alpha and Gamma rhythms, were modulated in response to words with those sensory-motor attributes in the corresponding spokes, shedding light on the mechanism of semantic representations in spokes; 4) Three types of hub-hub, hub-spoke and spoke-spoke connectivity were found to underlie dynamic semantic graphs. Importantly, these results were obtained using novel approaches proposed to address two challenges associated with EEG/MEG connectivity. Firstly, in order to find the most suitable of several connectivity metrics, we utilised principal component analysis (PCA) to find commonalities and differences of those methods when applied to a dataset and identified the most suitable metric based on the maximum explained variance. Secondly, reconstruction of EEG/MEG connectomes using anatomical or fMRI-based parcellations can be significantly contaminated by spurious leakage-induced connections in source space. We, therefore, utilised cross-talk functions in order to optimise the number, size and locations of cortical parcels, obtaining EEG/MEG-adaptive parcellations. In summary, this thesis proposes approaches for optimising EEG/MEG connectivity analyses and applies them to provide the first empirical evidence regarding some of the core predictions of the hub-and-spokes model. The key findings support the general framework of the hub(s)-and-spokes, but also suggest modifications to the model, particularly regarding the definition of semantic hub(s).

Every year over 2.5 million traumatic brain injuries (TBI) occur and are the leading cause of death and disability among adolescents. There are no approved treatments for TBI. Survivors suffer from persistent cognitive impairment due to posttraumatic tissue damage and disruption of neural networks which significantly detract from their quality of life. Posttraumatic cognitive impairment depends in part on the brain's limited ability to repair or replace damaged cells. Immature neurons in the hippocampus dentate gyrus, a brain region required for learning and memory, are particularly vulnerable to TBI. Insulin-like growth factor-1 (IGF1) is a potential therapeutic for TBI because it is a potent neurotrophic factor capable of mediating neuroprotection, neuro-repair, and neurogenesis. We hypothesized that conditional IGF1 overexpression in the mouse hippocampus following experimental controlled cortical impact injury (CCI) would enhance posttraumatic neurogenesis chronically. To this end, conditional astrocyte-specific IGF1 overexpressing mice (IGFtg) and wild-type (WT) mice received CCI or sham injury. The proliferation marker BrdU was used to label neurons born the first week after injury. Six weeks after injury, when surviving posttrauma-born neurons would be fully developed, we counted proliferated cells (BrdU+) and the subset expressing a mature neuronal marker (NeuN+/BrdU+) in the hippocampus. We also assessed cognitive performance during radial arm water-maze reversal (RAWM-R) testing, a neurogenesis-sensitive assay. IGF1 promoted end-stage maturity and decreased mis-migration of neurons born after trauma. These effects coincide with IGF1 induced improvements in performance on neurogenesis sensitive cognition following TBI. Mammalian target of rapamycin (mTOR), an early signaling molecule downstream of IGF1, has been identified as a potential target for TBI interventions because of its regulatory role in neuronal plasticity and neurogenesis. However, recent studies have also reported maladaptive plasticity and recovery associated with posttraumatic mTOR activation. It is imperative to elucidate the mechanism of action of IGF1 during pre-clinical evaluations. We hypothesized that IGF1 mediates posttraumatic neurogenic effects through IGF1 induction of mTOR activation. We injured cohorts of IGFtg and WT mice and harvested their brains for immunohistochemistry to assess IGF1 overexpression effects on posttraumatic mTOR activation at 1, 3, and 10 days post-injury (dpi). We found that IGF1 upregulated mTOR activation following TBI in a region-specific manner at 1 and 3dpi. To determine if IGF1 regulated differentiation and arborization through the mTOR pathway, injured WT and IGFtg mice received daily i.p. injections of rapamycin (10mg/kg), the inhibitor of mTOR, or its vehicle for 7 days. Vehicle and rapamycin administration began 3dpi, after the cells dividing at the peak of posttraumatic proliferation were labeled with BrdU. IGF1 enhancement of posttraumatic neurogenesis was not dependent on mTOR activation. In summary, IGF1 directs newborn neuron localization, promotes end-stage maturation, and chronically improves cognition. IGF1 can stimulate posttraumatic neurogenesis and plasticity independent of mTOR activation. These data suggest that IGF1 can stimulate neuron replacement following trauma-induced hippocampal neuron loss and cognitive improvement. Further studies should investigate IGF1 and mTOR inhibition as a combination therapy for neurorehabilitation.

In the last decade, (non)prescription opioid abuse, opioid use disorder (OUD) diagnoses, and opioid-related overdoses have risen and represent a significant public health concern. One method of understanding OUD is as a disorder of choice that requires choosing opioid rewards at the expense of other nondrug rewards. The characterization of OUD as a disorder of choice is important as it implicates decision- making processes as therapeutic targets, such as the valuation of opioid rewards. However, reward-value measurement and interpretation are traditionally different in substance abuse research compared to related fields such as economics, animal behavior, and neuroeconomics and may be less effective for understanding how opioid rewards are valued. The present research therefore used choice procedures in line with behavioral/neuroeconomic studies to determine if drug-associated decision making could be predicted from economic choice theories. In Experiment 1, rats completed an isomorphic food-food probabilistic choice task with dynamic, unpredictable changes in reward probability that required constant updating of reward values. After initial training, the reward magnitude of one choice subsequently increased from one to two to three pellets. Additionally, rats were split between the Signaled and Unsignaled groups to understand how cues modulate reward value. After each choice, the Unsignaled group received distinct choice-dependent cues that were uninformative of the choice outcome. The Signaled group also received uninformative cues on one option, but the alternative choice produced reward-predictive cues that informed the trial outcome as a win or loss. Choice data were analyzed at a molar level using matching equations and molecular level using reinforcement learning (RL) models to determine how probability, reward magnitude, and reward-associated cues affected choice. Experiment 2 used an allomorphic drug versus food procedure where the food reward for one option was replaced by a self-administered remifentanil (REMI) infusion at doses of 1, 3 and 10 μg/kg. Finally, Experiment 3 assessed the potential for both REMI and food reward value to be commonly scaled within the brain by examining changes in nucleus accumbens (NAc) Oxygen (O2) dynamics. Results showed that increasing reward probability, magnitude, and the presence of reward-associated cues all independently increased the propensity of choosing the associated choice alternative, including REMI drug choices. Additionally, both molar matching and molecular RL models successfully parameterized rats’ decision dynamics. O2 dynamics were generally commensurate with the idea of a common value signal for REMI and food with changes in O2 signaling scaling with the reward magnitude of REMI rewards. Finally, RL model-derived reward prediction errors significantly correlated with peak O2 activity for reward delivery, suggesting a possible neurological mechanism of value updating. Results are discussed in terms of their implications for current conceptualizations of substance use disorders including a potential need to change the discourse surrounding how substance use disorders are modeled experimentally. Overall, the present research provides evidence that a choice model of substance use disorders may be a viable alternative to the disease model and could facilitate future treatment options centered around economic principles.

A combined view of the human brain and computer-generated virtual realities is motivated by recent developments in cognitive neuroscience and human-computer interaction (HCI). The emergence of new theories of human brain function, together with an increasing use of realistic human-computer interaction, give reason to believe that a better understanding of the relationship between human brains and virtual realities is both possible and valuable. The concept of “presence”, described as the subjective feeling of being in a place that feels real, can serve as a cornerstone concept in the development of such an understanding, as computer-generated presence is tightly related to how human brains work in virtual realities. In this thesis, presence is related both to theoretical discussions rooted in theories of human brain function, and to measurements of brain activity during realistic interaction. The practical implications of such results are further developed by considering potential applications. This includes the development and evaluation of a prototype application, motivated by presented principles. The theoretical conception of presence in this thesis relies on general principles of brain function, and describes presence as a general cognitive function, not specifically related to virtual realities. Virtual reality (VR) is an excellent technology for investigating and taking advantage of all aspects of presence, but a more general interpretation allows the same principles to be applied to a wide range of applications. Functional magnetic resonance imaging (fMRI) was used to study the working human brain in VR. Such data can inform and constrain further discussion about presence. Using two different experimental designs we have investigated both the effect of basic aspects of VR interaction, as well as the neural correlates of disrupted presence in a naturalistic environment. Reality-based brain-computer interaction (RBBCI) is suggested as a concept for summarizing the motivations for, and the context of, applications building on an understanding of human brains in virtual realities. The RBBCI prototype application we developed did not achieve the set goals, but much remains to be investigated and lessons from our evaluation point to possible ways forward. A developed use of methods and techniques from computer gaming is of particular interest. / Ett kombinerat perspektiv på den mänskliga hjärnan och datorgenererade virtuella verkligheter motiveras av den senaste utvecklingen inom kognitiv neurovetenskap och människa-datorinteraktion (MDI). Framväxten av nya teorier om den mänskliga hjärnan, tillsammans med en ökande användning av realistisk människa-datorinteraktion, gör det troligt att en bättre förståelse för relationen mellan mänskliga hjärnor och virtuella verkligheter är både möjlig och värdefull. Begreppet "närvaro", som i detta sammanhang beskrivs som den subjektiva känslan av att vara på en plats som känns verklig, kan fungera som en hörnsten i utvecklingen av en sådan förståelse, då datorgenererad närvaro är tätt kopplat till hur mänskliga hjärnor fungerar i virtuella verkligheter. I denna avhandling kopplas närvaro både till teoretiska diskussioner grundade i teorier om den mänskliga hjärnan, och till mätningar av hjärnans aktivitet under realistisk interaktion. De praktiska konsekvenserna av sådana resultat utvecklas vidare med en närmare titt på potentiella tillämpningar. Detta inkluderar utveckling och utvärdering av en prototypapplikation, motiverad av de presenterade principerna. Den teoretiska diskussionen av närvaro i denna avhandling bygger på allmänna principer för hjärnans funktion, och beskriver känslan av närvaro som en generell kognitiv funktion, inte specifikt relaterad till virtuella verkligheter. Virtuell verklighet (virtual reality, VR) är en utmärkt teknik för att undersöka och dra nytta av alla aspekter av närvaro, men en mer allmän tolkning gör att samma principer kan tillämpas på ett brett spektrum av applikationer. Funktionell hjärnavbildning (fMRI) användes för att studera den arbetande mänskliga hjärnan i VR. Sådant data kan informera och begränsa en vidare diskussion av närvaro. Med hjälp av två olika försöksdesigner har vi har undersökt både effekten av grundläggande aspekter av VR-interaktion, och neurala korrelat av störd närvaro i en naturalistisk miljö. Verklighets-baserad hjärna-dator interaktion (reality-based brain-computer interaction, RBBCI) föreslås som ett begrepp för att sammanfatta motiv och kontext för applikationer som bygger på en förståelse av den mänskliga hjärnan i virtuella verkligheter. Den prototypapplikation vi utvecklade uppnådde inte de uppsatta målen, men mycket återstår att utforska och lärdomar från vår utvärdering pekar på möjliga vägar framåt. En vidare användning av metoder och tekniker från dataspel är speciellt intressant.

Implicit association of racial stereotypes is brought about by social conditioning (Greenwald & Krieger, 2006). This conditioning can be explained by attractor networks (Sharp, 2011). Reducing implicit bias through meditation can show the effectiveness of reducing the rigidity of attractor networks, thereby reducing subjectivity. Mindfulness meditation has shown to reduce bias from the use of one single guided session conducted before performing an Implicit Association Test (Lueke & Gibson, 2015). Attachment to socially conditioned racial bias should become less prevalent through practicing meditation over time. An experimental model is proposed to test this claim along with a reconceptualization of consciousness based in meditative practice.