Engagement of multifocal neural circuits during recall of autobiographical happy events

Happy emotional states have not been extensively explored in functional magnetic resonance imaging studies using autobiographic recall paradigms. We investigated the brain circuitry engaged during induction of happiness by standardized script-driven autobiographical recall in 11 healthy subjects (6 males), aged 32.4 ± 7.2 years, without physical or psychiatric disorders, selected according to their ability to vividly recall personal experiences. Blood oxygen level-dependent (BOLD) changes were recorded during auditory presentation of personal scripts of happiness, neutral content and negative emotional content (irritability). The same uniform structure was used for the cueing narratives of both emotionally salient and neutral conditions, in order to decrease the variability of findings. In the happiness relative to the neutral condition, there was an increased BOLD signal in the left dorsal prefrontal cortex and anterior insula, thalamus bilaterally, left hypothalamus, left anterior cingulate gyrus, and midportions of the left middle temporal gyrus (P < 0.05, corrected for multiple comparisons). Relative to the irritability condition, the happiness condition showed increased activity in the left insula, thalamus and hypothalamus, and in anterior and midportions of the inferior and middle temporal gyri bilaterally (P < 0.05, corrected), varying in size between 13 and 64 voxels. Findings of happiness-related increased activity in prefrontal and subcortical regions extend the results of previous functional imaging studies of autobiographical recall. The BOLD signal changes identified reflect general aspects of emotional processing, emotional control, and the processing of sensory and bodily signals associated with internally generated feelings of happiness. These results reinforce the notion that happiness induction engages a wide network of brain regions.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

Ultrasonic Viscosity Measurement Using the Shear-Wave Reflection Coefficient with a Novel Signal Processing Technique

Real-time viscosity measurement remains a necessity for highly automated industry. To resolve this problem, many studies have been carried out using an ultrasonic shear wave reflectance method. This method is based on the determination of the complex reflection coefficient`s magnitude and phase at the solid-liquid interface. Although magnitude is a stable quantity and its measurement is relatively simple and precise, phase measurement is a difficult task because of strong temperature dependence. A simplified method that uses only the magnitude of the reflection coefficient and that is valid under the Newtonian regimen has been proposed by some authors, but the obtained viscosity values do not match conventional viscometry measurements. In this work, a mode conversion measurement cell was used to measure glycerin viscosity as a function of temperature (15 to 25 degrees C) and corn syrup-water mixtures as a function of concentration (70 to 100 wt% of corn syrup). Tests were carried out at 1 MHz. A novel signal processing technique that calculates the reflection coefficient magnitude in a frequency band, instead of a single frequency, was studied. The effects of the bandwidth on magnitude and viscosity were analyzed and the results were compared with the values predicted by the Newtonian liquid model. The frequency band technique improved the magnitude results. The obtained viscosity values came close to those measured by the rotational viscometer with percentage errors up to 14%, whereas errors up to 96% were found for the single frequency method.

Morphosyntactic and syntactic priming: an investigation of underlying processing mechanisms and the effects of Parkinson’s disease

There is now considerable evidence to suggest that non-demented people with Parkinson's disease (PD) experience difficulties using the morphosyntactic aspects of language. It remains unclear, however, at precisely which point in the processing of morphosyntax, these difficulties emerge. The major objective of the present study was to examine the impact of PD on the processes involved in accessing morphosyntactic information in the lexicon. Nineteen people with PD and 19 matched control subjects participated in the study which employed on-line word recognition tasks to examine morphosyntactic priming for local grammatical dependencies that occur both within (e.g. is going) and across (e.g. she gives) phrasal boundaries (Experiments 1 and 2, respectively). The control group evidenced robust morphosyntactic priming effects that were consistent with the involvement of both pre- (Experiment 1) and post-lexical (Experiment 2) processing routines. Whilst the participants with PD also recorded priming for dependencies within phrasal boundaries (Experiment 1), priming effects were observed over an abnormally brief time course. Further, in contrast to the controls, the PD group failed to record morphosyntactic priming for constructions that crossed phrasal boundaries (Experiment 2). The results demonstrate that attentionally mediated mechanisms operating at both the pre- and post-lexical stages of processing are able to contribute to morphosyntactic priming effects. In addition, the findings support the notion that, whilst people with PD are able to access morphosyntactic information in a normal manner, the time frame in which this information remains available for processing is altered. Deficits may also be experienced at the post-lexical integrational stage of processing.

An advanced method for eliminating impacts from frequency deviations in power system signal processing

Frequency deviation is a common problem for power system signal processing. Many power system measurements are carried out in a fixed sampling rate assuming the system operates in its nominal frequency (50 or 60 Hz). However, the actual frequency may deviate from the normal value from time to time due to various reasons such as disturbances and subsequent system transients. Measurement of signals based on a fixed sampling rate may introduce errors under such situations. In order to achieve high precision signal measurement appropriate algorithms need to be employed to reduce the impact from frequency deviation in the power system data acquisition process. This paper proposes an advanced algorithm to enhance Fourier transform for power system signal processing. The algorithm is able to effectively correct frequency deviation under fixed sampling rate. Accurate measurement of power system signals is essential for the secure and reliable operation of power systems. The algorithm is readily applicable to such occasions where signal processing is affected by frequency deviation. Both mathematical proof and numerical simulation are given in this paper to illustrate robustness and effectiveness of the proposed algorithm. Crown Copyright (C) 2003 Published by Elsevier Science B.V. All rights reserved.

ECG denoising based on adaptive signal processing technique

An Electrocardiogram (ECG) monitoring system deals with several challenges related with noise sources. The main goal of this text was the study of Adaptive Signal Processing Algorithms for ECG noise reduction when applied to real signals. This document presents an adaptive ltering technique based on Least Mean Square (LMS) algorithm to remove the artefacts caused by electromyography (EMG) and power line noise into ECG signal. For this experiments it was used real noise signals, mainly to observe the di erence between real noise and simulated noise sources. It was obtained very good results due to the ability of noise removing that can be reached with this technique. A recolha de sinais electrocardiogr a cos (ECG) sofre de diversos problemas relacionados com ru dos. O objectivo deste trabalho foi o estudo de algoritmos adaptativos para processamento digital de sinal, para redu c~ao de ru do em sinais ECG reais. Este texto apresenta uma t ecnica de redu c~ao de ru do baseada no algoritmo Least Mean Square (LMS) para remo c~ao de ru dos causados quer pela actividade muscular (EMG) quer por ru dos causados pela rede de energia el ectrica. Para as experiencias foram utilizados ru dos reais, principalmente para aferir a diferen ca de performance do algoritmo entre os sinais reais e os simulados. Foram conseguidos bons resultados, essencialmente devido as excelentes caracter sticas que esta t ecnica tem para remover ru dos.

Fractional discrete-time signal processing: scale conversion and linear prediction

Nonlinear Dynamics, Vol. 29

Fractional signal processing: scale conversion

In Proceedings of the “ECCTD '01 - European Conference on Circuit Theory and Design, Espoo, Finland, August 2001

Estimation of signal subspace on hyperspectral data

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.

Clinical and epidemiological aspects of HTLV-II infection in São Paulo, Brazil: presence of Tropical Spastic Paraparesis/HTLV-Associated Myelopathy (TSP/HAM) simile diagnosis in HIV-1-co-infected subjects

In this study, the epidemiological and clinical features observed in solely HTLV-II-infected individuals were compared to those in patients co-infected with HIV-1. A total of 380 subjects attended at the HTLV Out-Patient Clinic in the Institute of Infectious Diseases "Emilio Ribas" (IIER), São Paulo, Brazil, were evaluated every 3-6 months for the last seven years by infectious disease specialists and neurologists. Using a testing algorithm that employs the enzyme immuno assay, Western Blot and polymerase chain reaction, it was found that 201 (53%) were HTLV-I positive and 50 (13%) were infected with HTLV-II. Thirty-seven (74%) of the HTLV-II reactors were co-infected with HIV-1. Of the 13 (26%) solely HTLV-II-infected subjects, urinary tract infection was diagnosed in three (23%), one case of skin vasculitis (8%) and two cases of lumbar pain and erectile dysfunction (15%), but none myelopathy case was observed. Among 37 co-infected with HIV-1, four cases (10%) presented with tropical spastic paraparesis/HTLV-associated myelopathy (TSP/HAM) simile. Two patients showed paraparesis as the initial symptom, two cases first presented with vesical and erectile disturbances, peripheral neuropathies were observed in other five patients (13%), and seven (19%) patients showed some neurological signal or symptoms, most of them with lumbar pain (five cases). The results obtained suggest that neurological manifestations may be more frequent in HTLV-II/HIV-1-infected subjects than those infected with HTLV-II only.

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

INTERCELLULAR AND SYSTEM-LEVEL ASPECTS OF THE METABOLIC INTERACTIONS BETWEEN NEURONS AND GLIA

Quand on parle de l'acide lactique (aussi connu sous le nom de lactate) une des premières choses qui vient à l'esprit, c'est son implication en cas d'intense activité musculaire. Sa production pendant une activité physique prolongée est associée avec la sensation de fatigue. Il n'est donc pas étonnant que cette molécule ait été longtemps considérée comme un résidu du métabolisme, possiblement toxique et donc à éliminer. En fait, il a été découvert que le lactate joue un rôle prépondérant dans le métabolisme grâce à son fort potentiel énergétique. Le cerveau, en particulier les neurones qui le composent, est un organe très gourmand en énergie. Récemment, il a été démontré que les astrocytes, cellules du cerveau faisant partie de la famille des cellules gliales, utilisent le glucose pour produire du lactate comme source d'énergie et le distribue aux neurones de manière adaptée à leur activité. Cette découverte a renouvelé l'intérêt scientifique pour le lactate. Aujourd'hui, plusieurs études ont démontré l'implication du lactate dans d'autres fonctions de la physiologie cérébrale. Dans le cadre de notre étude, nous nous sommes intéressés au rapport entre neurones et astrocytes avec une attention particulière pour le rôle du lactate. Nous avons découvert que le lactate possède la capacité de modifier la communication entre les neurones. Nous avons aussi décrypté le mécanisme grâce auquel le lactate agit, qui est basé sur un récepteur présent à la surface des neurones. Cette étude montre une fonction jusque-là insoupçonnée du lactate qui a un fort impact sur la compréhension de la relation entre neurones et astrocytes. - Relatively to its volume, the brain uses a large amount of glucose as energy source. Furthermore, a tight link exists between the level of synaptic activity and the consumption of energy equivalents. Astrocytes have been shown to play a central role in the regulation of this so-called neurometabolic coupling. They are thought to deliver the metabolic substrate lactate to neurons in register to glutamatergic activity. The astrocytic uptake of glutamate, released in the synaptic cleft, is the trigger signal that activates an intracellular cascade of events that leads to the production and release of lactate from astrocytes. The main goal of this thesis work was to obtain detailed information on the metabolic and functional interplay between neurons and astrocytes, in particular on the influence of lactate besides its metabolic effects. To gain access to both spatial and temporal aspects of these dynamic interactions, we used optical microscopy associated with specific fluorescent indicators, as well as electrophysiology. In the first part of this thesis, we show that lactate decreases spontaneous neuronal, activity in a concentration-dependent manner and independently of its metabolism. We further identified a receptor-mediated pathway underlying this modulatory action of lactate. This finding constituted a novel mechanism for the modulation of neuronal transmission by lactate. In the second part, we have undergone a characterization of a new pharmacological tool, a high affinity glutamate transporter inhibitor. The finality of this study was to investigate the detailed pharmacological properties of the compound to optimize its use as a suppressor of glutamate signal from neuron to astrocytes. In conclusion, both studies have implications not only for the understanding of the metabolic cooperation between neurons and astrocytes, but also in the context of the glial modulation of neuronal activity. - Par rapport à son volume, le cerveau utilise une quantité massive de glucose comme source d'énergie. De plus, la consommation d'équivalents énergétiques est étroitement liée au niveau d'activité synaptique. Il a été montré que dans ce couplage neurométabolique, un rôle central est joué par les astrocytes. Ces cellules fournissent le lactate, un substrat métabolique, aux neurones de manière adaptée à leur activité glutamatergique. Plus précisément, le glutamate libéré dans la fente synaptique par les neurones, est récupéré par les astrocytes et déclenche ainsi une cascade d'événements intracellulaires qui conduit à la production et libération de lactate. Les travaux de cette thèse ont visé à étudier la relation métabolique et fonctionnelle entre neurones et astrocytes, avec une attention particulière pour des rôles que pourrait avoir le lactate au-delà de sa fonction métabolique. Pour étudier les aspects spatio-temporels de ces interactions dynamiques, nous avons utilisé à la fois la microscopie optique associée à des indicateurs fluorescents spécifiques, ainsi que l'électrophysiologie. Dans la première partie de cette thèse, nous montrons que le lactate diminue l'activité neuronale spontanée de façon concentration-dépendante et indépendamment de son métabolisme. Nous avons identifié l'implication d'un récepteur neuronal au lactate qui sous-tend ce mécanisme de régulation. La découverte de cette signalisation via le lactate constitue un mode d'interaction supplémentaire et nouveau entre neurones et astrocytes. Dans la deuxième partie, nous avons caractérisé un outil pharmacologique, un inhibiteur des transporteurs du glutamate à haute affinité. Le but de cette étude était d'obtenir un agent pharmacologique capable d'interrompre spécifiquement le signal médié par le glutamate entre neurones et astrocytes pouvant permettre de mieux comprendre leur relation. En conclusion, ces études ont une implication non seulement pour la compréhension de la coopération entre neurones et astrocytes mais aussi dans le contexte de la modulation de l'activité neuronale par les cellules gliales.

Connections between signal processing and complex analysis

We describe one of the research lines of the Grup de Teoria de Funcions de la UAB UB, which deals with sampling and interpolation problems in signal analysis and their connections with complex function theory.

A knowledge based signal processing approach to tonic identification in indian classical music

In this paper, we describe several techniques for detecting tonic pitch value in Indian classical music. In Indian music, the raga is the basic melodic framework and it is built on the tonic. Tonic detection is therefore fundamental for any melodic analysis in Indian classical music. This workexplores detection of tonic by processing the pitch histograms of Indian classic music. Processing of pitch histograms using group delay functions and its ability to amplify certain traits of Indian music in the pitch histogram, is discussed. Three different strategies to detect tonic, namely, the concert method, the template matching and segmented histogram method are proposed. The concert method exploits the fact that the tonic is constant over a piece/concert.templatematchingmethod and segmented histogrammethodsuse the properties: (i) the tonic is always present in the background, (ii) some notes are less inflected and dominant, to detect the tonic of individual pieces. All the three methods yield good results for Carnatic music (90−100% accuracy), while for Hindustanimusic, the templatemethod works best, provided the v¯adi samv¯adi notes for a given piece are known (85%).

A Fast Gradient Approximation for Nonlinear Blind Signal Processing

When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation), complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsic complexity, the global algorithm is much more slow and hence not useful for our purpose.