103 research outputs found

    Information Technologies for Cognitive Decline

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    Information technology (IT) is used to establish a diagnosis and provide treatment for people with cognitive decline. The condition affects many before it becomes clear that more permanent changes, like dementia, could be noticed. Those who search for information are exposed to lots of information and different technologies which they need to make sense of and eventually use to help themselves. In this research literature and information available on the Internet were systematically analyzed to present methods used for diagnosis and treatment. Methods used for diagnosis are self-testing, sensors, Virtual Reality (VR), and brain imaging. Methods used for treatment are games, websites with information and media, Virtual Reality (VR), sensors, and robots. The resulting concept of knowledge was the basis of an artifact whose main goal was to present the facts to the broad public. This implied that a user-friendly artifact was developed through three iterations using the Design Science framework. A total of nine users and IT usability experts have evaluated the artifact returning the SUS score of 85,83 for users and 87,5 for IT usability experts. Nielsen´s heuristics were assessed by IT usability experts only, returning an average score of 4,28. The general response was positive regarding both the content and the attempt to present methods used in cognitive decline. It reminds to be seen how to bring this knowledge to those who are most affected by the decline.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

    Magnetoencephalography

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    This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

    Hondatze kognitibo arinaren detekzio goiztiarrerako hizketa ezagutza automatikoan oinarrituriko ekarpenak

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    302 p.Alzheimerdun gaixoengan, mintzamena ez ezik, erantzun emozionala ere kaltetu egiten da. Emozioak giza gogoaren arkitekturarekin zerikusia dituzten prozesu kognitiboak dira, eta erabakiak hartzearekin eta oroimenaren kudeaketa edota arretarekin zerikusia dute, eta aldi berean ere, horiek hertsiki lotuta dauden komunikazioarekin. Hortaz, erantzun eta kudeaketa emozionalak ere badira gaitzaren hasierako fase horietan nahasten diren beste komunikazio-elementu batzuk, eta disfluentzia bezala, emozio-erantzuna narriadura kognitiboa neurtzeko adierazlea izan daiteke.Hortaz, zenbait atazaren bidez sortutako ahots-laginen azterketak direla medio, disfluentzia eta emozio-erantzuna jaso daitezke. Hizkuntzarekiko independenteak diren parametroak bildu eta horien hizkeraren nahasmenduak ezaugarritu badaitezke, ekarpena lagungarria izan daiteke diagnostikoa egingo duten espezialistentzat.Lehengaiak ahots-laginak direnez, ingurune kliniko zein etxeko ingurunean egindako ataza desberdinen bidez grabazioak egin eta datu-baseak osatu dira, osasun-guneen irizpide etikoak kontuan hartuta eta. Datu-base horien ikerketaren bidez, galera kognitiboaren garapena neurtu, kuantifikatu, balioztatu eta sailkatu nahi da. Gaitzaren etapa desberdinak hautematen laguntzeko ekarpena egin nahi da, eta horretarako, hizkuntzarekiko independenteak diren parametroen azterketa automatikorako teknika eta metodologiak garatu dira. Mintzamen automatikoaren analisian oinarritutako multi-hurbilketa ez-lineala egin da, zeinak hizketa-analisian erabiltzen diren denborazko serieen konplexutasunaren neurtze kuantitatiboa eman diezaguke

    Hondatze kognitibo arinaren detekzio goiztiarrerako hizketa ezagutza automatikoan oinarrituriko ekarpenak

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    302 p.Alzheimerdun gaixoengan, mintzamena ez ezik, erantzun emozionala ere kaltetu egiten da. Emozioak giza gogoaren arkitekturarekin zerikusia dituzten prozesu kognitiboak dira, eta erabakiak hartzearekin eta oroimenaren kudeaketa edota arretarekin zerikusia dute, eta aldi berean ere, horiek hertsiki lotuta dauden komunikazioarekin. Hortaz, erantzun eta kudeaketa emozionalak ere badira gaitzaren hasierako fase horietan nahasten diren beste komunikazio-elementu batzuk, eta disfluentzia bezala, emozio-erantzuna narriadura kognitiboa neurtzeko adierazlea izan daiteke.Hortaz, zenbait atazaren bidez sortutako ahots-laginen azterketak direla medio, disfluentzia eta emozio-erantzuna jaso daitezke. Hizkuntzarekiko independenteak diren parametroak bildu eta horien hizkeraren nahasmenduak ezaugarritu badaitezke, ekarpena lagungarria izan daiteke diagnostikoa egingo duten espezialistentzat.Lehengaiak ahots-laginak direnez, ingurune kliniko zein etxeko ingurunean egindako ataza desberdinen bidez grabazioak egin eta datu-baseak osatu dira, osasun-guneen irizpide etikoak kontuan hartuta eta. Datu-base horien ikerketaren bidez, galera kognitiboaren garapena neurtu, kuantifikatu, balioztatu eta sailkatu nahi da. Gaitzaren etapa desberdinak hautematen laguntzeko ekarpena egin nahi da, eta horretarako, hizkuntzarekiko independenteak diren parametroen azterketa automatikorako teknika eta metodologiak garatu dira. Mintzamen automatikoaren analisian oinarritutako multi-hurbilketa ez-lineala egin da, zeinak hizketa-analisian erabiltzen diren denborazko serieen konplexutasunaren neurtze kuantitatiboa eman diezaguke

    The Use of EEG-fMRI Features for Characterizing Mental Disorders

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    Determining clinically relevant biomarkers of mental disorders for reliably indicating pathophysiological processes or predicting therapeutic responses remains a major challenge, despite decades of research. Identifying such biomarkers can help patients significantly improve their quality of life and alleviate their suffering. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are non-invasive tools to investigate neurobiological mechanisms underlying mental disorders. Extracting and leveraging informative features from the high temporal resolution EEG and high spatial resolution fMRI may offer a more comprehensive understanding of brain spatial and temporal activities in health and disease. More importantly, this information can lead to a better understanding of the neurobiology of mental illness. This dissertation investigates the analyses and applications of extracting and combining informative features from EEG and fMRI, along with applying machine learning (ML) and computational methods for building biomarkers of mental illnesses. Several methodological challenges in the extraction of informative and reproducible features are also addressed. First, two types of EEG features obtained from resting state EEG-fMRI measurements were extracted: 1) broadband-multichannel EEG dynamical features, called EEG microstates (EEG-ms); and 2) heterogeneous, static EEG features. Using EEG features only, results elucidate that: 1) EEG-ms characteristics and information theoretical properties can successfully differentiate individuals with mood and anxiety disorders from healthy comparison subjects with potential applications for other clinical groups; and 2) heterogeneous static EEG features can successfully predict “brain aging,” noted here as BrainAGE from 468 EEG datasets, achieving a correlation of r=0.61 between predicted age and chronological age. Next, extracted EEG features were leveraged with fMRI to enhance the predictivity of BrainAGE and localizing the associated EEG-ms brain regions. More specifically, static EEG features were combined with resting state fMRI features to construct a multimodal BrainAGE predictor as a case study. Notably, it was found that EEG and fMRI contain a large portion of shared information about age, although each modality has its fingerprint of the aging process. The developed approach is a general purpose and be applied to predict other outcomes from brain imaging data. Similarly, EEG-ms features were integrated with fMRI to localize associated brain regions within fMRI space, revealing functional brain connectivity changes in individuals with mood and anxiety disorders as a case study. As a result, harnessing combined EEG-fMRI methods have enriched our knowledge some mental disorders and broadened our understanding of them with potential applications for other clinical groups and outcomes. Finally, this work evaluated the reproducibility and replication of EEG-ms analysis to address technical issues that have thus far been overlooked in the literature. In conclusion, the presented work describes technical methods developed to study and discover several clinically translatable biomarkers that can be reliably used to characterize various mental disorders

    Event-related EEG analysis : b simple solutions of complex computations

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    PhD ThesisThe value of EEG as a non-invasive technique for studying the time course and frequency composition of neuronal signals is well established. However, to date there is still no gold standard methodology for its analysis. Since the introduction of the technique many methodologies for artefact removal and signal isolation have been developed but their performance is often only assessed, against other methodologies, using simulated data with known and controlled artefacts and limited variance. Furthermore, these studies often only address a single stage in the entire analysis pipeline and do not consider the affect different preprocessing techniques might have upon the effectiveness of different signal analysis methodologies. To address this issue this thesis approaches the assessment of 4 different signal analysis methodologies using real-world-data, from two different stimulus evoked potential studies, and an EEG analysis pipeline that systematically applies and adjusts various preprocessing techniques before subsequent signal analysis. This semi-automated process can be broken down into two stages. Firstly, multiple configurations of a Preprocessing Optimisation Pipeline (POP) were performed to address three main causes of artefactual noise (1) electrical line noise, (2) non-neuronal potentials (low frequency drifts and muscle artefacts), and (3) ocular artefacts (blinks and saccades). Within the final stages of the POP data quality was assessed for each participant and poorly preprocessed participant datasets were excluded from further analysis based upon either a novel maximum baseline variability threshold criterion or a standard minimum epoch number threshold approach. Lastly, the data was passed onto a Signal Analysis Pipeline (SAP) which estimated the amplitude of task-specific signals of interest through one of four methodologies (1) grand average informed peak detection (GA-PD), (2) individual average peak detection (IAPD), (3) independent component analysis informed peak detection (ICA-PD) or (4) component of interest peak detection (COIPD). The effectiveness of each of the different preprocessing and signal analysis strategies were then assessed based upon observing the changes within task-specific outcome statistics

    The relationship between physical exercise and cognition in children with typical development and neurodevelopmental disorders

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    This research project sought to investigate the relationship between physical exercise and cognition in children with and without a neurodevelopmental condition. To achieve this aim, three approaches were undertaken to explore the exercise and cognition relationship. The first approach sought to understand the efficacy of exercise interventions on cognition in individuals with a neurodevelopmental disorder. The second approach was to understand the effectiveness of an exercise activity when compared to a cognitively-engaging tablet game activity on measures of implicit learning and attention in children with and without a neurodevelopmental condition. The third approach was to investigate if psychophysiological measures could account for the cognitive effect observed after exercising in children with and without a neurodevelopmental condition. Taking the approaches together, this research project focused on investigating the efficacy, effect, and mechanism of the exercise-cognition relationship. To investigate the efficacy of the exercise interventions, a meta-analytic review was conducted on 22 studies from the neurodevelopmental literature. The main findings from this meta-analysis revealed an overall small-to-medium effect size of exercise on cognition, supporting the efficacy of applying exercise interventions to young individuals with a neurodevelopmental disorder. Similar to the general population, physical exercise has been demonstrated to improve some but not all cognitive functions, with some individuals demonstrating no change in cognitive function after exercising. In terms of the effects of physical exercise, this project conducted an experimental study comparing a moderate-intensity exercise activity with a tablet game activity for a period of 12 minutes in 35 children aged 6-11 years. Overall, the study found that the effect of exercise was comparable to the tablet activity across the reaction time measures, but not on the accuracy performance of the implicit learning and attention tasks. Overall, exercise activity led to a better accuracy performance on implicit learning and executive attention compared to the tablet activity, particularly in children with a neurodevelopmental condition. The last part of this project was an extension of the experimental study whereby psychophysiological measures were investigated based on a proposed detrended fluctuation analysis (DFA). This investigation found that galvanic skin response (GSR), as indexed by its scaling exponent, was related to whether children revealed a change in cognitive function after receiving the exercise activity, particularly on executive attention. Importantly, this relationship was also able to account for children who did not demonstrate a cognitive effect of exercise. This result was not evident in the electroencephalogram (EEG) measures. This investigation concluded that the effect of exercise on executive attention was dependent on the interplay between an individual’s arousal system, cognitive task demand, and the novelty of the exercise activity. Taking the findings together, this project highlights the importance of individual differences to the exercise and cognition relationship. Specifically, this project demonstrated the feasibility of investigating the scaling exponent, via fractal analysis (e.g., DFA), as an index of individual differences. Additionally, fractal analysis is a valuable tool to assist in further understanding the mechanism underlying the exercise-cognition relationship, particularly on the influence of individual difference
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