<p>Biological drives such as hunger, thirst, and sexual reproduction are potent motivators of behavior. Extrinsic rewards in the environment (i.e. food, drink, money) are also important behavioral and cognitive motivators. In addition to the relevance of an extrinsic reward in meeting the needs of biological drives, an individual’s sensitivity to the physiological state of their body (interoceptive awareness) would also be expected to mediate motivation for these extrinsic primary rewards (i.e. food, drink). Importantly, a better characterization of the predicted behavioral and neural interactions between interoception, motivation, and memory systems can highlight novel targets for interventions to facilitate motivation and memory for adaptive behaviors and/or impede motivation and memory for maladaptive behaviors (i.e. addiction, relapse, overeating). </p><p> The present dissertation examines how individual differences in interoceptive awareness may modulate motivated memory formation via motivational and affective mechanisms. Specifically, interoceptive accuracy is associated with increased motivation for relevant primary rewards and enhanced encoding for these rewards. However, anxiety, negatively predicted by interoceptive accuracy, negatively predicts memory the next day. Furthermore, memory for relevant primary rewards was negatively predicted by insula-parahippocampal and ventral tegmental area-hippocampal background connectivity.</p> / Dissertation

Classification analyses are a promising way to localize signal, especially scattered signal, in functional magnetic resonance imaging data. However, there is not yet a consensus on the most effective analysis pathway. We explore the efficacy of k-Nearest Neighbors classifiers on simulated functional magnetic resonance imaging data. We utilize a novel construction of the classification data. Additionally, we vary the spatial distribution of signal, the design matrix of the linear model used to construct the classification data, and the feature set available to the classifier. Results indicate that the k-Nearest Neighbors classifier is not sufficient under the current paradigm to adequately classify neural data and localize signal. Further exploration of the data using k-means clustering indicates that this is likely due in part to the amount of noise present in each data point. Suggestions are made for further research.

Scientific research on free will was started by Libet et al. (1982). They detected that thereadiness potential (RP) proceeded urges with up to 350ms. One interpretation of the RP wasthat it represented motor planning. The research progress of antecedent brain activity inrelation to conscious urges is investigated by looking at contemporary studies. How differentassumptions and definitions of the free will concept influences interpretation of these studiesis also discussed. The evidence is in favor that the RP is not representing motor planning.Antecedent activity has been detected with numerous technologies, most notably fMRIclassifiers which have been used to predict decisions in advance. Scrutiny of these resultsreveals that the experimental setups are dependent on time-locking trials which may construethe results. It is shown that predictions based on probabilistic antecedents can be interpretedin numerous ways. The review shows that free will positions differ from each other onseveral factors, such as whether free will is either-or or exists on a spectrum. Some notablepositions are not dependent on antecedent activity at all. The notion of control is one of thepivotal factors deciding if a subject experience free will, not if they are the causer per se.Future discussion will be improved by systematizing the differences between the free willpositions and communicating them clearly. Convergent evidence points at the explanatorymodel of free will being a cognitive feeling – A feeling which reports ownership over actionsbut does not cause them.

Current thinking suggests that specialised modules process visual information in a hierarchical manner, using local circuitry in order to maximise efficiency both in terms of wiring costs and stimulus coverage (Reichl et al. (2012)). The resulting organisation has been shown to contain structure in the form of stripes, columns and pinwheels, which in animal models have been linked to functional segregation and specificity. In human cortex, post-mortem investigations have assisted in the visualisation of two such key features: ocular dominance columns (ODC) within V1, and a stripe system within V2 (Adams et al. (2007); Hockfield et al. (1990)). However, functional observations in humans have until recently been beyond the investigatory scope of in-vivo methodology, and as such, a role of these networks has yet to be conclusively determined. In the small number of instances of in-vivo investigations of human ODC and V2 stripes (Cheng et al. (2001); Yacoub et al. (2001); Nasr et al. (2016)), data have been acquired for a small number of carefully selected participants over long scan durations. I aimed to overcome these limitations and explore the functional similarities further, employing a novel, ultra-high resolution fMRI sequence to do so. I measured the cortical response to monocular stimulation and recorded a robust response within V1. However, the regular and repeating functional patterns of ODCs were not observed. Using multivariate techniques I concluded, based on robust classification, that reliable monocular signals were present but that they were subtle and difficult to differentiate from noise. I then investigated the segregation of colour, form and motion within V2, where I found evidence for spatially segregated signals in response to colour and motion, but not to form. I hypothesised that the form stimulus was sub-optimal in driving the neural population of the associated stripes. Based on a limited number of samples, activity in response to colour and motion stimulation conformed on average to the neuroanatomical profile of the V2 stripe system. I suggest that my results offer encouragement for in-vivo investigations of small-scale functional organisation in visual cortex.

Grupper och grupprestationer har studerats länge utifrån olika perspektiv och discipliner, vilket har gett upphov till ett flertal teorier och förklaringsmodeller som innefattar olika tillvägagångssätt för hur grupper kan studeras. I denna uppsats förklaras begreppen grupper, grupprocesser och grupprestationer utifrån olika perspektiv och discipliner innan ett neurovetenskapligt perspektiv på grupper och grupprestationer antas. Detta för att redogöra för hur kognitiv neurovetenskap och neurovetenskapliga mätmetoder som electroencephalography (EEG) och functional magnetic resonance imaging (fMRI) kan förhålla sig till forskning på grupper, grupprocesser och grupprestationer. Syftet med uppsatsen är att beskriva hur grupper kan studeras utifrån ett neurovetenskapligt perspektiv samt hur kognitiv neurovetenskap kan komplettera studier på grupper och grupprestationer. Först presenteras en förklaring på vad grupper, grupprocesser och grupprestationer är, inklusive en redogörelse för relevanta begrepp som ledarskap, gruppsammanhållning och gruppkognition. Detta följs sedan av hur grupper och grupprestationer kan studeras ytterligare med ett neurovetenskapligt perspektiv, för att avslutas med problematik och möjligheter för kognitiv neurovetenskap inom forskningsfältet.

The main purpose of this dissertation project was to assess the behavioral and neural correlates of Episodic Memory as a function of the APOE genotype in a healthy young adult sample. To accomplish this, 98 subjects completed behavioral tasks assessing visual memory, working memory, episodic memory, and attention. Subjects also completed questionnaires evaluating IQ, years of education, drug use, personality, and emotional traits. These subjects were also genotyped for the APOE gene, resulting in 29 APOE-ε4 carriers (subjects who had at least one ε4 allele) and 69 Non APOE-ε4 carriers (having no ε4 alleles). No differences were found between genotypic groups on any demographic characteristics, behavioral measures, or personality traits. From this larger pool of 98 subjects, a subset of 22 subjects (10 APOE-ε4, 12 Non APOE-ε4) completed additional behavioral tasks while undergoing functional magnetic resonance imaging. While being scanned, subjects were asked to learn word pairs during an encoding phase, make metamemory evaluations on their ability to later remember each word pair during a judgment of learning (JOL) task, and try to discriminate between original and recombined word pairs during a final recognition phase. Interspersed between these tasks was a rest task meant to elicit activity within the Default Network. No differences in memory or metamemory performance were found on the behavioral tasks administered during imaging based on genotype. In contrast, marked differences in brain activation were found between APOE-ε4 carriers and Non APOE-ε4 carriers across the various imaging tasks. During encoding, APOE-ε4 carriers were found to have greater activation than Non APOE-ε4 carriers in the dorsal anterior portion of the left superior temporal gyrus, cingulate gyrus, and anterior middle frontal gyrus. This same pattern - greater APOE-ε4 carrier activation as compared to Non APOE-ε4 carriers - was present in the parahippocampal gyrus and posterior middle temporal gyrus during the judgment of learning metamemory task. During the recognition task, greater activation was found for Non APOE-ε4 carriers versus APOE-ε4 carriers in the left parahippocampal gyrus, SPL, and right anterior superior frontal gyrus. During the rest task, greater activation was seen in APOE-ε4 carriers versus Non APOE-ε4 carriers in the left inferior frontal gyrus, whereas the converse comparison resulted in medial anterior cingulate activation. The lack of behavioral differences suggests that in a healthy young adult sample, as was used in the present study, there are not yet detectable behavioral differences as a function of APOE genotype. The greater neural activity seen in APOE-ε4 carriers during the encoding and judgment of learning tasks is likely to reflect neural compensation: young adult APOE-ε4 carriers compensate for declines in cognitive efficiency with greater neural activity such that this greater neural activity improves behavioral performance, particularly in memory domains (Buckner, Andrews-Hanna, & Schacter, 2008; Han & Bondi, 2008; Levy et al., 2004; Trivedi et al., 2008). The relatively lower levels of activation in APOE-ε4 carriers during the recognition task may reflect stronger memory traces for studied items as a result of greater frontal and medial temporal lobe activity during the encoding and judgment of learning tasks in the APOE-ε4 carriers (Kirwan, Wixted, & Squire, 2008; Mondadoori et al., 2007; Squire, Wixted, & Clark, 2007). In the present sample, a lack of behavioral differences accompanied by neural disparity may signal the precursors of Alzheimer's disease, highlighting the progressive deteriorating influence of the APOE-ε4 allele. The aberrant pattern of default network activity seen in APOE-ε4 carriers underlies this influence as this genotype is proposed to preferentially contribute to the causes of Alzheimer's disease in areas common to the Default Network and Episodic Memory (Buckner et al., 2008). The present results strengthen previous findings illustrating a connection between the brain activity underlying memory processes, the default network, and the APOE genotype.

In this study, we used resting-state functional magnetic resonance imaging (fMRI) scans from subjects with early mild cognitive impairment (EMCI) and control subjects to study functional network connectivity. The scans were acquired by the Alzheimer's Disease Neuroscience Initiative (ADNI). We used genetic data from the ADNI database to further subdivide the EMCI and control groups into genotype groups with or without the ApoE4 allele. ROI-to-ROI resting-state functional connectivity was measured using Freesurfer and the Functional Connectivity Toolbox for Matlab (CONN). In our analysis, we compared whole-brain ROI connectivity strength and ROI-to-ROI functional network connectivity strength between EMCI, control and genotype subject groups. We found that the ROI network properties were disrupted in EMCI and ApoE4-containing groups. Notably, we show that (1) EMCI disrupts functional connectivity strength in many areas; (2) the ApoE4 allele disrupts functional connectivity strength in similar areas to EMCI; and (3) the differences in functional connectivity between groups shows a multifactor contribution to functional network dysfunction along the trajectory leading to dementia.

Fluent speech production requires rapid coordination among respiratory, laryngeal, and articulatory processes and is mediated by multiple neural systems (Bohland & Guenther, 2006). Stuttering is a fluency disorder characterized by core deficits in speech motor planning. Previous research indicates people who stutter (PWS) exhibit deficits in speech motor sequence learning and are slower and less accurate over practice relative to fluent speakers (Ludlow, Siren, & Zikira, 2004; Namasivayam & VanLieshout, 2004; Smits-Bandstra & De Nil, 2007; Smits-Bandstra, De Nil, & Saint-Cyr, 2006). Furthermore, the neural bases of impaired speech motor sequence learning in PWS are not well understood. We present a study in which PWS (n=18) and persons with fluent speech (PFS) (n=17) were taught phonotactically illegal (e.g. gbesb) and phonotactically legal (e.g. blerk) speech motor sequences over two practice sessions. Functional magnetic resonance imaging (fMRI) was used to investigate brain regions underlying the production of learned illegal syllables and novel illegal syllables. With practice, subjects produced syllables more accurately, which is indicative of motor sequence learning. Our findings suggest a speech motor performance deficit in PWS. Furthermore, these findings indicate speech motor sequence learning relies on a speech motor sequence learning network.

INTRODUCTION: With the advent of advanced neuroimaging, strides have been made towards better understanding the cognitive elements necessary for task processing. Resting state functional connectivity assessments using functional magnetic resonance imaging has allowed patient assessments of underlying neural networks in patient populations with variable constraints. Drug addiction, a chronically relapsing disorder, presents many variable constraints. Cellular and molecular changes in neural reward pathway of drug addicted patient populations have advanced, but circuit-level alterations with reward deficits are yet to be completely understood. Resting state functional connectivity investigations in patient populations that use illicit drugs are seen to have repercussions on neural networks. OBJECTIVE: Assess and compare reward-network resting state functional connectivity investigations in patient populations with illicit drug use. METHODS: A meta-analysis of several resting state functional connectivity studies. Patient populations for each study contained an experimental group of drug users with a group of non-drug using controls to assess changes in resting state functional connectivity of the reward network. Studies utilized Diagnostic and Statistical Manuel of Mental disorders, 4th edition, as the basis of diagnosing drug dependence and abuse. A 3 Tesla MRI scanner was utilized to assess the reward pathway of the drug abuse in all experiments with the exception of one group using a 4 Tesla scanner. Band-pass temporal filtering from roughly 0.01 Hz to 0.1 Hz on residual signals was used to obtain low-frequency fluctuations needed for resting state connectivity analyses. Correlation maps were created by computing the correlation coefficients between the blood oxygen level dependent time course from the seed regions and from all other brain voxels. Regions of interest were chosen based on data from databases or previous studies. RESULTS: Four papers found widespread reductions in the connectivity of multiple reward pathway components. Results of these studies are consistent with perspectives suggesting that transition from drug use to addiction is driven by reduced functioning of reward systems and concurrently increased activation of anti-reward systems. Two studies suggested an increase in reward pathway of drug use, suggesting enhanced connectivity within reward and motivation circuits may be interpreted in the perspective of altered incentive salience for drugs and drug-associated stimuli. CONCLUSION: At early stage of experimental data in this field, data interpretation necessitates caution. Small sample sizes, heterogeneous subject groups and variable experimental paradigms may have lead to opposing findings. With certainty, chronic drug use was found to alter reward pathway in patient populations.

Examining the effect of repeating stimuli on brain activity is important for theories of perception, learning and memory. Functional magnetic resonance imaging (fMRI) is a non-invasive way to examine repetition-related effects in the human brain. However the Blood-Oxygenation Level-Dependent (BOLD) signal measured by fMRI is far removed from the electrical activity recorded from single cells in animal studies of repetition effects. Despite that, there have been many claims about the neural mechanisms associated with fMRI repetition effects. However, none of these claims has adequately considered the temporal and spatial resolution limitations of fMRI. In this thesis, I tackle these limitations by combining simulations and modelling in order to infer repetition-related changes at the neural level. I start by considering temporal limitations in terms of the various types of general linear model (GLM) that have used to deconvolve single-trial BOLD estimates. Through simulations, I demonstrate that different GLMs are best depending on the relative size of trial-variance versus scan-variance, and the coherence of those variabilities across voxels. To address the spatial limitations, I identify six univariate and multivariate properties of repetition effects measured by event-related fMRI in regions of interest (ROI), including how repetition affects the ability to classify two classes of stimuli. To link these properties to underlying neural mechanisms, I create twelve models, inspired by single-cell studies. Using a grid search across model parameters, I find that only one model (“local scaling”) can account for all six fMRI properties simultaneously. I then validate this result on an independent dataset that involves a different stimulus set, protocol and ROI. Finally, I investigate classification of initial versus repeated presentations, regardless of the stimulus class. This work provides a better understanding of the neural correlates of stimulus repetition effects, as well as illustrating the importance of formal modelling.