The functional neurophysiological sequelae associated with high frequency dream recallers

Background: Dreaming is a universal experience, yet there is considerable inter-individual variability with regard to dream recall frequency (DRF). Research on DRF has been prolific leading to the development of various models delineating possible processes involved in dream recall. One such model is the 'arousal-retrieval' model positing that intra-sleep wakefulness is required for dream traces to be encoded into long-term storage, essentially proposing increased DRF as a product of a better memory for dreams. Results from recent studies support this model by demonstrating longer periods of intra-sleep wakefulness in high frequency recallers (HFRs) compared to low frequency recallers (LFRs). Furthermore, results showed heightened brain reactivity, as well as increased regional cerebral blood flow in areas in the brain associated with dream production. These results are indicative of the existence of a functional neurophysiological trait innate to HRs, while also supporting the premise that apart from a better memory for dreams, HRs also may produce more dreams. Awakenings from rapid eye movement (REM) sleep yield the highest dream recall rates, rendering REM sleep as a reasonable starting point for studying rates of dream production. Furthermore, increased dream production during REM sleep might also affect related processes, for example, leading to enhanced overnight emotional memory consolidation and emotion regulation. Hypotheses: The current study investigated the functional neurophysiological sequelae associated with HFRs in a design where HFRs are compared to LFRs. Hypotheses include: (1) HFRs will score significantly higher on certain personality dimensions; (2) HFRs will experience significantly more awakenings, as well as longer periods of intra-sleep wakefulness; (3) HFRs will have significantly higher rates of dream production as measured by the frequency of eye movements (REM density) during REM sleep; and (4) increased dream production during REM sleep will lead to enhanced overnight emotional memory consolidation and emotion regulation in HFRs. Methods: The study consisted of two groups of healthy young adults: high frequency recallers (n = 19) and low frequency recallers (n = 17) who underwent polysomnographic recordings on two non-consecutive nights. Memory tasks and affective questionnaires were completed before and after a night of sleep. Results: (1) HFRs scored significantly higher on the 'agreeableness' personality dimension and on the Boundary Questionnaire; (2) HFRs experienced significantly more awakenings, especially from stage 2 non-rem (NREM) sleep, as well as significantly longer periods of intra-sleep wakefulness; (3) no significant between-group differences with regard to REM density, nor (4) overnight emotional memory consolidation and emotion regulation were found. Conclusion: Results support, firstly, the proposition that certain personality traits, differences in sleep architecture, and increased DRF are an expression of a functional neurophysiological arrangement innate to HFRs. Secondly, the findings suggest that NREM sleep, as opposed to REM sleep, is important in relation to DRF in this specific population. This is the first study to not only replicate existing findings, but to also contribute to the extant literature by illuminating additional characteristics and features associated with HFRs

Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals

Tesis por compendio[ES] En la actualidad, el desarrollo y aplicación de algoritmos para el reconocimiento de patrones que mejoren los niveles de rendimiento, detección y procesamiento de datos en diferentes áreas del conocimiento resulta un tema de gran interés. En este contexto, y específicamente en relación con la aplicación de estos algoritmos en el monitoreo y diagnóstico de máquinas eléctricas, el uso de señales de flujo es una alternativa muy interesante para detectar las diferentes fallas. Asimismo, y en relación con el uso de señales biomédicas, es de gran interés extraer características relevantes en las señales de actigrafía para la identificación de patrones que pueden estar asociados con una patología específica. En esta tesis, se han desarrollado y aplicado algoritmos basados en el procesamiento estadístico y espectral de señales, para la detección y diagnóstico de fallas en máquinas eléctricas, así como su aplicación al tratamiento de señales de actigrafía. Con el desarrollo de los algoritmos propuestos, se pretende tener un sistema dinámico de indicación e identificación para detectar la falla o la patología asociada que no depende de parámetros o información externa que pueda condicionar los resultados, sólo de la información primaria que inicialmente presenta la señal a tratar (como la periodicidad, amplitud, frecuencia y fase de la muestra). A partir del uso de los algoritmos desarrollados para la detección y diagnóstico de fallas en máquinas eléctricas, basados en el procesamiento estadístico y espectral de señales, se pretende avanzar, en relación con los modelos actualmente existentes, en la identificación de fallas mediante el uso de señales de flujo. Además, y por otro lado, mediante el uso de estadísticas de orden superior, para la extracción de anomalías en las señales de actigrafía, se han encontrado parámetros alternativos para la identificación de procesos que pueden estar relacionados con patologías específicas.[CA] En l'actualitat, el desenvolupament i aplicació d'algoritmes per al reconeixement de patrons que milloren els nivells de rendiment, detecció i processament de dades en diferents àrees del coneixement és un tema de gran interés. En aquest context, i específicament en relació amb l'aplicació d'aquests algoritmes a la monitorització i diagnòstic de màquines elèctriques, l'ús de senyals de flux és una alternativa molt interessant per tal de detectar les diferents avaries. Així mateix, i en relació amb l'ús de senyals biomèdics, és de gran interés extraure característiques rellevants en els senyals d'actigrafia per a la identificació de patrons que poden estar associats amb una patologia específica. En aquesta tesi, s'han desenvolupat i aplicat algoritmes basats en el processament estadístic i espectral de senyals per a la detecció i diagnòstic d'avaries en màquines elèctriques, així com la seua aplicació al tractament de senyals d'actigrafia. Amb el desenvolupament dels algoritmes proposats, es pretén obtindre un sistema dinàmic d'indicació i identificació per a detectar l'avaria o la patologia associada, el qual no depenga de paràmetres o informació externa que puga condicionar els resultats, només de la informació primària que inicialment presenta el senyal a tractar (com la periodicitat, amplitud, freqüència i fase de la mostra). A partir de l'ús dels algoritmes desenvolupats per a la detecció i diagnòstic d'avaries en màquines elèctriques, basats en el processament estadístic i espectral de senyals, es pretén avançar, en relació amb els models actualment existents, en la identificació de avaries mitjançant l'ús de senyals de flux. A més, i d'altra banda, mitjançant l'ús d'estadístics d'ordre superior, per a l'extracció d'anomalies en els senyals d'actigrafía, s'han trobat paràmetres alternatius per a la identificació de processos que poden estar relacionats amb patologies específiques.[EN] Nowadays, the development and application of algorithms for pattern recognition that improve the levels of performance, detection and data processing in different areas of knowledge is a topic of great interest. In this context, and specifically in relation to the application of these algorithms to the monitoring and diagnosis of electrical machines, the use of stray flux signals is a very interesting alternative to detect the different faults. Likewise, and in relation to the use of biomedical signals, it is of great interest to extract relevant features in actigraphy signals for the identification of patterns that may be associated with a specific pathology. In this thesis, algorithms based on statistical and spectral signal processing have been developed and applied to the detection and diagnosis of failures in electrical machines, as well as to the treatment of actigraphy signals. With the development of the proposed algorithms, it is intended to have a dynamic indication and identification system for detecting the failure or associated pathology that does not depend on parameters or external information that may condition the results, but only rely on the primary information that initially presents the signal to be treated (such as the periodicity, amplitude, frequency and phase of the sample). From the use of the algorithms developed for the detection and diagnosis of failures in electrical machines, based on the statistical and spectral signal processing, it is intended to advance, in relation to the models currently existing, in the identification of failures through the use of stray flux signals. In addition, and on the other hand, through the use of higher order statistics for the extraction of anomalies in actigraphy signals, alternative parameters have been found for the identification of processes that may be related to specific pathologies.Iglesias Martínez, ME. (2020). Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/145603TESISCompendi

Brain Dynamics as Confirmatory Biomarker of Dementia with Lewy Bodies Versus Alzheimer’s Disease - an Electrophysiological Study

PhD ThesisIntroduction Dementia with Lewy bodies (DLB), Parkinson’s disease dementia (PDD) and Alzheimer’s disease dementia (AD) are associated with different pathologies. Nevertheless, symptomatic overlap between these conditions may lead to misdiagnosis. Resting-state functional connectivity features in DLB as assessed with electroencephalography (EEG) are emerging as diagnostic biomarkers. However, their pathological significance is still questioned. This study aims to further investigate this aspect and to infer functional and structural sources of EEG abnormalities in DLB. Methods Graph theory analysis was first performed to assess EEG network differences between healthy controls (HC) and dementia groups. Source localisation and Network Based Statistics (NBS) were used to infer EEG cortical network and dominant frequency (DF) alterations in DLB compared with AD. Further analysis aimed to assess the subnetwork associated with visual hallucination (VH) symptom in DLB and PDD, i.e. LBD, compared with not-hallucinating (NVH) patients. Finally, probabilistic tractography was performed on diffusion tensor imaging (DTI) data between cortical regions, thalamus, and basal forebrain (NBM). Correlation between structural and functional connectivity was tested. Results EEG α-band (7-13.5 Hz) network features were affected in LBD compared with HC, whilst DLB β-band network (14-20.5 Hz) was weaker and more segregated when compared with AD. This scenario replicated in the source domain. DF was significantly lower in DLB compared with AD, and positively correlated with structural connectivity strength between NBM and the cortex. Functional visual ventral network connectivity and cholinergic projections towards the cortex were affected in VH compared with NVH, and significantly correlated in NVH. Conclusions Functional connectivity as assessed with EEG is more affected in DLB compared with AD. Moreover, the visual ventral network is functionally altered in VH compared with NVH. Results from structural analysis provide empirical evidence on the role of cholinergic dysfunctions in DLB and PDD pathology and corresponding functional correlates