Structural and effective connectivity of lexical-semantic and naming networks in patients with chronic aphasiaMeier, Erin 24 October 2018 (has links)
Given the difficulty in predicting outcomes in persons with stroke-induced aphasia (PWA), neuroimaging-based biomarkers of recovery could provide invaluable predictive power to stroke models. However, the neural patterns that constitute beneficial neural organization of language in PWA remain debated. Thus, in this work, we propose a novel network theory of aphasia recovery and test our overarching hypothesis, i.e., that task-specific language processing in PWA requires the dynamic engagement of intact tissue within a bilateral network of anatomically-segregated but functionally and structurally connected language-specific and domain-general brain regions. We first present two studies in which we examined left frontotemporal connectivity during different language tasks (i.e., picture naming and semantic feature verification). Results suggest that PWA heavily rely on left middle frontal gyrus (LMFG)-driven connectivity for tasks requiring lexical-semantic processing and semantic control whereas controls prefer models with input to either LMFG or left inferior frontal gyrus (LIFG). Both studies also revealed several significant associations between spared tissue, connectivity and language skills in PWA. In the third study, we examined bilateral frontotemporoparietal connectivity and tested a lesion- and connectivity-based hierarchical model of chronic aphasia recovery. Between-group comparisons showed controls exhibited stronger left intra-hemispheric task-modulated connectivity than did PWA. Connectivity and language deficit patterns most closely matched predictions for patients with primarily anterior damage whereas connectivity results for patients with other lesion types were best explained by the nature of the semantic task. In the last study, we investigated the utility of lesion classification based on gray matter (GM) only versus combined GM plus white matter (WM) metrics. Results suggest GM only classification was sufficient for characterizing aphasia and anomia severity but the GM+WM classification better predicted naming treatment outcomes. We also found that fractional anisotropy of left WM association tracts predicted baseline naming and treatment outcomes independent of total lesion volume. Finally, results of a preliminary multimodal prediction analysis suggest that combined structural and functional metrics reflecting the integrity of regions and connections comprise optimal predictive models of behavior in PWA. To conclude this dissertation, we discuss how multimodal network models of aphasia recovery can guide future investigations. / 2020-10-23T00:00:00Z
Zaghlool, Shaza B.
03 April 2014
Dynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing strategy for a given cognitive task. The logical network topology of the model is specified by a combination of prior knowledge and statistical analysis of the neuro-imaging signals. Parameters of this a-priori model are then estimated and competing models are compared to determine the most likely model given experimental data. Inter-subject analysis using DCM is complicated by differences in model topology, which can vary across subjects due to errors in the first-level statistical analysis of fMRI data or variations in cognitive processing. This requires considerable judgment on the part of the experimenter to decide on the validity of assumptions used in the modeling and statistical analysis; in particular, the dropping of subjects with insufficient activity in a region of the model and ignoring activation not included in the model. This manual data filtering is required so that the fMRI model's network size is consistent across subjects. This thesis proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various data set sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool. / Ph. D.
28 April 2016
While games can be an innovative and a highly promising approach to education, creating effective educational games is a challenge. It requires effectively integrating educational content with game attributes and aligning cognitive and affective outcomes, which can be in conflict with each other. Intelligent Tutoring Systems (ITS), on the other hand, have proven to be effective learning environments that are conducive to strong learning outcomes. Direct comparisons between tutoring systems and educational games have found digital tutors to be more effective at producing learning gains. However, tutoring systems have had difficulties in maintaining students€™ interest and engagement for long periods of time, which limits their ability to generate learning in the long-term. Given the complementary benefits of games and digital tutors, there has been considerable effort to combine these two fields. This dissertation undertakes and analyzes three different ways of integrating Intelligent Tutoring Systems and digital games. We created three game-like systems with cognition, metacognition and affect as their primary target and mode of intervention. Monkey's Revenge is a game-like math tutor that offers cognitive tutoring in a game-like environment. The Learning Dashboard is a game-like metacognitive support tool for students using Mathspring, an ITS. Mosaic comprises a series of mini-math games that pop-up within Mathspring to enhance students' affect. The methodology consisted of multiple randomized controlled studies ran to evaluate each of these three interventions, attempting to understand their effect on students€™ performance, affect and perception of the intervention and the system that embeds it. Further, we used causal modeling to further explore mechanisms of action, the inter-relationships between student€™s incoming characteristics and predispositions, their mechanisms of interaction with the tutor, and the ultimate learning outcomes and perceptions of the learning experience.
04 May 2011
Educational games intend to make learning more enjoyable, but at the potential cost of compromising learning efficiency. Therefore, instead of creating educational games, we create learning environment with game-like elements: the elements of games that are engaging. Our approach is to assess each game-like element in terms of benefits such as enhancing engagement as well as its costs such as sensory or working memory overload, with a goal of maximizing both engagement and learning. We developed different four versions of a math tutor with different degree of being game-like such as adding narrative and visual feedback. Based on a study with 297 students, we found that students reported more satisfaction with more 'game-like' tutor but we were not able to detect any conclusive difference in learning among the different tutors. We collected student data of various types such as their attitude and enjoyment via surveys, performance within tutor via logging, and learning as measured by a pre/post-test. We created a causal model using software TETRAD and contrast the causal modeling approach to the results we achieve with traditional approaches such as correlation matrix and multiple regression. Relative to traditional approaches, we found that causal modeling did a better job at detecting and representing spurious association, and direct and indirect effects within variables. Causal model, augmented with domain knowledge about likely causal relationships, resulted in much more plausible and interpretable model. We propose a framework for blending exploratory results from causal modeling with randomized controlled studies to validate hypotheses.
Phonemic awareness is the ability to attend to the formal, phonetic or phonemic, aspects of spoken language. Skill in analysis of speech sounds and synthesis of phonetic segments into real words has often been found to correlate with success in reading acquisition. The nature of this relationship was investigated by postulating a causal model for the effect of phonemic awareness in kindergarten on reading and spelling skill in the first school years. The quantitative implications of this model were estimated with path-analysis in a kindergarten - grade 3 passive observational study. In order to experimentally test the effect of phonemic awareness a 8 week training program in kindergarten was evaluated using a quasi- experimental design in field settings. The effects of this program were evaluated in kindergarten, in grade 1 and in grade 2. Methodological problems in evaluation research were discussed. The results from the quasi- experimental study was further elucidated applying structural equation modeling with latent variables (LISREL). Clear effects of the training program were found on phonemic awareness tasks in grade 1 and on spelling in grade 2. More subtle effects were found on reading and spelling of simple words in grade 1. No effect was found on rapid silent word decoding. The LISREL analysis was interpreted in favour of a model with phonemic awareness effecting phonological processing which in turn is essential for the early reading development. The results were interpreted as supporting an interactive-compensatory limited capacity model of reading. Phonemic awareness helps the child to understand the alphabetical principle and ensures the development of an effective system for representing written language. Trained children find it easier to learn spelling-sound relations. / <p>Diss. (sammanfattning) Umeå : Univ., härtill 5 uppsatser.</p> / digitalisering@umu
Use of environmental variables to infer gene flow and population structure in the gopher tortoise (Gopherus polyphemus) and predict the seroprevalence of an emerging infectious diseaseClostio, Rachel Wallace 05 August 2010 (has links)
Understanding worldwide declines in reptiles due to factors such as habitat loss and emerging infectious disease has become an increasingly important focus in conservation biology. Here, I use novel approaches from the field of landscape genetics to combine spatial genetic data with landscape data at both regional and local spatial scales to explore natural and anthropogenic landscape features that shape population structure and gene flow in a federally threatened reptile, Gopherus polyphemus. I also utilize approaches from the field of spatial epidemiology to examine the extent to which environmental variables can be used to predict the seroprevalence of an associated pathogen Mycoplasma agassizzi in gopher tortoise populations. Using mitochondrial data, I find evidence of a historical barrier to gene flow that appears to coincide with the Apalachicola River. I also discover low genetic diversity and evidence of population bottlenecks in the western portion of the range. My evaluation at the regional scale shows that dispersal is limited by geographic distance, areas of low elevation and major roads ways. A finescale study reveals no evidence of spatial genetic structure within a 14 x 35 km area. However, soil type is significantly correlated with pairwise genetic distances between individuals, suggesting that this variable influences fine-scale population structure in the gopher tortoise. In addition to soil, high density canopy cover is an important factor impeding gene flow at the local level for females, while land cover type explains some of the genetic variance between males. Finally, temperature and precipitation appear to be important predictors of the seroprevalence of the pathogen Mycoplasma agassizii in gopher tortoises. The probability of an individual testing seropositive for exposure to this disease increased with high temperature and low precipitation values. The methods presented in this dissertation evaluate novel approaches for assessing the influence of environmental variables on population structure, dispersal and disease occurrence and could be applied in future studies of other threatened and endangered taxa.
THE ORGANIZATION OF FUNCTIONAL AND EFFECTIVE CONNECTIVITY OF RESTING-STATE BRAIN NETWORKS IN ADOLESCENTS WITH AND WITHOUT NEURODEVELOPMENTAL AND/OR INTERNALIZING DISORDERSRickels, Audreyana Cleo Jagger 01 May 2019 (has links)
The development of functional connectivity is often described as changing from local to distributed connections which give rise to the functional brain networks observed in adulthood. In contrast to the well-explored pattern found in functional connectivity, no research has been published describing effective connectivity development. Also, there is a plethora of literature describing functional connectivity patterns in a variety of neurodevelopmental and internalizing disorders, but there is little consistency in the connectivity patterns discovered for each disorder. Hence, this study aimed to describe functional and effective resting-state connectivity during adolescent development in a typically developing adolescent (TDA) group (n = 128) and to determine how adolescents with comorbid neurodevelopmental disorders (CND) (n = 46) differed. This was accomplished through functional and effective connectivity analysis within and between four networks: the Default Mode Network (DMN), the Salience Network (SN), the Dorsal Attention Network (DAN), and the Frontal Parietal Control Network (FPCN). The results from this study indicate that within-network connectivity decreased across age in the TDA group, which is in opposition to previous work which suggests strengthening within-network connectivity. The CND group displayed hyper-connectivity compared to the TDA group in between-network connectivity with no effect of age. The effective connectivity in the TDA group displayed decreasing connectivity within networks with increasing age, a novel effect not previously reported in the literature. The CND group’s effective connectivity was overall hyper-connected (for within- and between-networks). The functional connectivity patterns in the TDA group suggest that functional connectivity has subtle developmental change during adolescence. Further, the CND group consistently displayed hyper-connectivity in functional and effective connectivity. The CND group, and perhaps similar comorbid groups, may have less efficient networks which could contribute to their disorder(s).
Rose, David, Livengood, Jonathan, Sytsma, Justin, Machery, Edouard
01 October 2012
Chandra Sripada's (2010) Deep Self Concordance Account aims to explain various asymmetries in people's judgments of intentional action. On this account, people distinguish between an agent's active and deep self; attitude attributions to the agent's deep self are then presumed to play a causal role in people's intentionality ascriptions. Two judgments are supposed to play a role in these attributions-a judgment that specifies the attitude at issue and one that indicates that the attitude is robust (Sripada & Konrath, 2011). In this article, we show that the Deep Self Concordance Account, as it is currently articulated, is unacceptable.
Investigation of Discrepancies in Brain Effective Connectivity Between Healthy Control and Epileptic Patient Groups: A Resting-State fMRI StudyMahalingam, Neeraja 11 July 2019 (has links)
No description available.
Operational Risk Management - Implementing a Bayesian Network for Foreign Exchange and Money Market Settlement / Operationale Risiko Managment Implementierung eines Bayesian Network für Foreign Exchange and Money Market Settlement Process.Adusei-Poku, Kwabena 26 August 2005 (has links)
No description available.
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