The present study investigates the neural substrates underlying cognitive processing in schizophrenia (Sz) patients. To this end, an auditory 3-stimulus oddball paradigm was used to identify P3a and P3b components, elicited by rare-distractor and rare-target tones, respectively. Event-related potentials (ERP) were recorded from 31 Sz patients and 38 healthy controls. The P3a and P3b brain-source generators were identified by time-averaging of low-resolution brain electromagnetic tomography (LORETA) current density images. In contrast with the commonly used fixed window of interest (WOI), we proposed to apply an adaptive WOI, which takes into account subjects’ P300 latency variability. Our results showed different P3a and P3b source activation patterns in both groups. P3b sources included frontal, parietal and limbic lobes, whereas P3a response generators were localized over bilateral frontal and superior temporal regions. These areas have been related to the discrimination of auditory stimulus and to the inhibition (P3a) or the initiation (P3b) of motor response in a cognitive task. In addition, differences in source localization between Sz and control groups were observed. Sz patients showed lower P3b source activity in bilateral frontal structures and the cingulate. P3a generators were less widespread for Sz patients than for controls in right superior, medial and middle frontal gyrus. Our findings suggest that target and distractor processing involves distinct attentional subsystems, both being altered in Sz. Hence, the study of neuroelectric brain information can provide further insights to understand cognitive processes and underlying mechanisms in Sz.
Giménez, S.; Romero, S.; Alonso, J.F.; Mañanas, M.A.; Pujol, A.; Baxarias, P.; Antonijoan, R. M. Journal of clinical monitoring and computing p. 1-8 DOI: 10.1007/s10877-015-9805-5 Date of publication: 2015-11-14 Journal article
Las señales corticales del cerebro permiten una visión más clara de la actividad neuronal que el estudio clásico de las señales registradas en el cuero cabelludo y son de gran interés en el estudio de enfermedades neurodegenerativas como la enfermedad de Alzheimer. Con las señales adquiridas sobre el córtex prefrontal y somatosensorial de ratones con la mutación 3xTg-AD, se han estudiado los estados espontáneos UP y DOWN de las oscilaciones lentas del cerebro anestesiado. En primer lugar, se han aplicado técnicas de filtrado para eliminar interferencias y seleccionar la banda de interés entre 0.1 y 200 Hz, en la cual se aplicaron medidas de análisis espectral. Tras un
filtrado adicional entre 30 y 100 Hz para seleccionar la banda gamma asociada a la actividad UP y DOWN, se calcularon variables de conectividad. Entre los resultados obtenidos, se escogieron aquellas variables que presentaban una mayor significación estadística mediante las pruebas T-Student y Wilcoxon. Finalmente, con estas variables se diseñaron dos procedimientos de clasificación por análisis discriminante lineal (Stepwise y Leave-one-out) que consiguen diferenciar entre sujetos enfermos de Alzheimer y sujetos control con un 88% y un 82% de precisión, respectivamente.
Um dos problemas que apresenta o registro de electroencefalogramas (EEG) está relacionado com a referência usada durante a aquisição dos dados. O presente artigo analisa o comportamento de três referências, o promédio mastoides (RPM), o promédio común (RPC) e o método de estandardização do eletrodo de referência (REST), usadas no registro de EEG num estudo farmacológico realizado com alprazolam (medicamento psicoativo). Os resultados foram analisados no dominio frequencial para avaliar a ação do fármaco e através da
informação mútua cruzada para investigar a conetividade cerebral. Nenhuma das referências apresenta melhores resultados em todos os casos estudados, REST tem boa atuação nas variáveis espectrais e de conectividade enquanto que as outras duas trabalham melhor só em um dos casos.
Alonso, J.F.; Romero, S.; Mañanas, M.A.; Rojas, M.; Riba, J.; Barbanoj, M.J. Medical and biological engineering and computing Vol. 53, num. 10, p. 1011-1023 DOI: 10.1007/s11517-015-1315-6 Date of publication: 2015-10-01 Journal article
The identification of the brain regions involved in the neuropharmacological action is a potential procedure for drug development. These regions are commonly determined by the voxels showing significant statistical differences after comparing placebo-induced effects with drug-elicited effects. LORETA is an electroencephalography (EEG) source imaging technique frequently used to identify brain structures affected by the drug. The aim of the present study was to evaluate different methods for the correction of multiple comparisons in the LORETA maps. These methods which have been commonly used in neuroimaging and also simulated studies have been applied on a real case of pharmaco-EEG study where the effects of increasing benzodiazepine doses on the central nervous system measured by LORETA were investigated. Data consisted of EEG recordings obtained from nine volunteers who received single oral doses of alprazolam 0.25, 0.5, and 1 mg, and placebo in a randomized crossover double-blind design. The identification of active regions was highly dependent on the selected multiple test correction procedure. The combined criteria approach known as cluster mass was useful to reveal that increasing drug doses led to higher intensity and spread of the pharmacologically induced changes in intracerebral current density.
Ochoa, J. F.; Ruiz, M.; Valle, D.; Duque, J.; Tobón, C.; Alonso, J.F.; Hernández, A.M.; Mañanas, M.A. Annual International Conference of the IEEE Engineering in Medicine and Biology Society p. 7442-7445 DOI: 10.1109/EMBC.2015.7320112 Presentation's date: 2015-09 Presentation of work at congresses
Alzheimer's disease is the most prevalent cause of dementia. Mild Cognitive Impairment (MCI) is defined as a grey area between intact cognitive functioning and clinical dementia. Electroencephalography (EEG) has been used to identify biomarkers in dementia. Currently, there is a great interest in translating the study from raw signals to signal generators, trying to keep the relationship with neurophysiology. In the current study, EEG recordings during an encoding task were acquired in MCI subjects and healthy controls. Data was decomposed using group Independent Component Analysis (gICA) and the most neuronal components were analyzed using Phase Intertrial Coherence (PIC) and Phase shift Intertrial Coherence (PsIC). MCI subjects exhibited an increase of PIC in the theta band, while controls showed increase in PsIC in the alpha band. Correlation between PIC and PsIC and clinical scales were also found. Those findings indicate that the methodology proposed based in gICA can help to extract information from EEG recordings with neurophysiological meaning.
Background: Psychedelics induce intense modifications in the sensorium, the sense of "self," and the experience of reality. Despite advances in our understanding of the molecular and cellular level mechanisms of these drugs, knowledge of their actions on global brain dynamics is still incomplete. Recent imaging studies have found changes in functional coupling between frontal and parietal brain structures, suggesting a modification in information flow between brain regions during acute effects.; Methods: Here we assessed the psychedelic-induced changes in directionality of information flow during the acute effects of a psychedelic in humans. We measured modifications in connectivity of brain oscillations using transfer entropy, a nonlinear measure of directed functional connectivity based on information theory. Ten healthy male volunteers with prior experience with psychedelics participated in 2 experimental sessions. They received a placebo or a dose of ayahuasca, a psychedelic preparation containing the serotonergic 5-HT2A agonist N,N-dimethyltryptamine.; Results: The analysis showed significant changes in the coupling of brain oscillations between anterior and posterior recording sites. Transfer entropy analysis showed that frontal sources decreased their influence over central, parietal, and occipital sites. Conversely, sources in posterior locations increased their influence over signals measured at anterior locations. Exploratory correlations found that anterior-to-posterior transfer entropy decreases were correlated with the intensity of subjective effects, while the imbalance between anterior-to-posterior and posterior-to-anterior transfer entropy correlated with the degree of incapacitation experienced.; Conclusions: These results suggest that psychedelics induce a temporary disruption of neural hierarchies by reducing top-down control and increasing bottom-up information transfer in the human brain.
Objective. One of the principal drawbacks of magnetoencephalography (MEG) is its high sensitivity to metallic artifacts, which come from implanted intracranial electrodes and dental ferromagnetic prosthesis and produce a high distortion that masks cerebral activity. The aim of this study was to develop an automatic algorithm based on blind source separation (BSS) techniques to remove metallic artifacts from MEG signals. Approach. Three methods were evaluated: AMUSE, a second-order technique; and INFOMAX and FastICA, both based on high-order statistics. Simulated signals consisting of real artifact-free data mixed with real metallic artifacts were generated to objectively evaluate the effectiveness of BSS and the subsequent interference reduction. A completely automatic detection of metallic-related components was proposed, exploiting the known characteristics of the metallic interference: regularity and low frequency content. Main results. The automatic procedure was applied to the simulated datasets and the three methods exhibited different performances. Results indicated that AMUSE preserved and consequently recovered more brain activity than INFOMAX and FastICA. Normalized mean squared error for AMUSE decomposition remained below 2%, allowing an effective removal of artifactual components. Significance. To date, the performance of automatic artifact reduction has not been evaluated in MEG recordings. The proposed methodology is based on an automatic algorithm that provides an effective interference removal. This approach can be applied to any MEG dataset affected by metallic artifacts as a processing step, allowing further analysis of unusable or poor quality data.
Alonso, J.F.; Romero, S.; Ballester, M. R.; Antonijoan, R. M.; Mañanas, M.A. Physiological measurement Vol. 36, p. 1351-1365 DOI: 10.1088/0967-3334/36/7/1351 Date of publication: 2015-05-27 Journal article
The biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this paper was to assess stress using EEG-based variables obtained from univariate analysis and functional connectivity evaluation. Two different stressors, the Stroop test and sleep deprivation, were applied to 30 volunteers to find common EEG patterns related to stress effects. Results showed a decrease of the high alpha power (11 to 12 Hz), an increase in the high beta band (23 to 36 Hz, considered a busy brain indicator), and a decrease in the approximate entropy. Moreover, connectivity showed that the high beta coherence and the interhemispheric nonlinear couplings, measured by the cross mutual information function, increased significantly for both stressors, suggesting that useful stress indexes may be obtained from EEG-based features.
Alonso, J.F.; Sabater, A.; Romero, S.; Mañanas, M.A.; Riba, J. A Dialogue with the Cerebral Cortex: Cortical Function and Interfacing p. 1-4 DOI: 10.3389/conf.fnsys.2015.06.00008 Presentation's date: 2015-04-29 Presentation of work at congresses
Electroencephalographic analysis techniques have become a very useful tool to assess brain activity and interactions between cerebral regions, that is, the so-called cerebral connectivity analysis. The effects of some drugs have, so far, been studied using spectral analysis and, to a lesser extent, some linear and nonlinear connectivity techniques. New indexes have recently been designed based on assumptions that make them more robust against volume conduction effects that could yield to spurious connectivity results.
These new indexes such as the imaginary coherence (IC) [Nolte et al., 2004], the phase-lag index (PLI) [Stam et al., 2007] and the weighted phase-lag index (WPLI) [Vinck et al., 2011] have proven very useful in several fields, for example in characterizing electroencephalographic (EEG) and magnetoencephalographic (MEG) activity of Alzheimer’s Disease patients compared to healthy controls.
However, these techniques have not been applied to study the effect of drugs on the brain. The main purpose of the current work was to assess the suitability and effectiveness of these innovative indexes to study the brain connectivity under psychoactive drug treatment, and concretely, the effects of a single dose of alprazolam, a short-acting drug of the benzodiazepine family.
Alprazolam is extensively prescribed for the treatment of anxiety and panic disorders, and peak plasma concentrations are obtained between 0.5 and 2 hours after intake [Greenblat and Wright, 1993]. Being a benzodiazepines, alprazolam induces an enhancement of the inhibitory pathways through their activity on the GABA A receptor complex, favouring the entrance to chloride ions into the neurons [Haefely, 1990]. Due to the enhancement of the inhibitory pathways a weakening or even an impairment of functional connectivity could be hypothesized
Alcalá, M.; Alonso, J.F.; Giménez, S.; Romero, S.; Mañanas, M.A. Congreso Anual de la Sociedad Española de Ingeniería Biomédica p. 794-797 Presentation's date: 2014-11-27 Presentation of work at congresses
En el presente trabajo se estudian los efectos de la privación del sueño sobre las señales electroencefalográficas de voluntarios sanos mediante la transferencia de entropía (TE), una técnica no paramétrica que permite detectar relaciones direccionales entre variables y que no ha sido utilizada antes para el estudio de la vigilia prolongada.
Los resultados obtenidos concuerdan con la potencia en la banda ¿, un marcador reconocido relacionado con la presión de sueño.
Los efectos máximos de la privación del sueño se producen a partir de las 24 horas sin dormir. Durante este periodo, se producen incrementos de TE localizados en la zona anterior mientras que los decrementos principalmente se originan en la zona occipital y afectan globalmente al resto del cerebro. El promedio de estos decrementos muestra una fuerte correlación con la potencia ¿, por lo que se podría considerar como otro marcador fiable de la presión de sueño que, además, proporciona información sobre la localización de los cambios producidos, indicando una disminución de la transferencia de información entre las regiones cerebrales.
En el presente trabajo se estudian los efectos de la privación del sueño sobre las señales electroencefalográficas de voluntarios sanos mediante la transferencia de entropía (TE), una técnica no paramétrica que permite detectar relaciones direccionales entre variables y que no ha sido utilizada antes para el estudio de la vigilia prolongada.
Los resultados obtenidos concuerdan con la potencia en la banda ϴ, un marcador reconocido relacionado con la presión de sueño.
Los efectos máximos de la privación del sueño se producen a partir de las 24 horas sin dormir. Durante este periodo, se producen incrementos de TE localizados en la zona anterior mientras que los decrementos principalmente se originan en la zona occipital y afectan globalmente al resto del cerebro. El promedio de estos decrementos muestra una fuerte correlación con la potencia ϴ, por lo que se podría considerar como otro marcador fiable de la presión de sueño que, además, proporciona información sobre la localización de los cambios producidos, indicando una disminución de la transferencia de información entre las regiones cerebrales.
Rojas, M.; Alonso, J.F.; Chaler, J.; Mañanas, M.A. Annual International Conference of the IEEE Engineering in Medicine and Biology Society p. 5005-5008 DOI: 10.1109/EMBC.2013.6610672 Presentation's date: 2013-07 Presentation of work at congresses
Isokinetic exercises have been extensively used in order to analyze muscle imbalances and changes associated with fatigue. It is known that such changes are difficult to assess from EMG signals during dynamic contractions, especially, using linear signal processing tools. The aim of this work was to use nonlinear prediction in order to analyze muscle couplings and interactions in this context and to assess the load-sharing of different muscles during fatigue. Results show promising for detecting interaction strategies between muscles and even for the interaction between muscles and the output torque during endurance tests.
Migliorelli, C.; Romero, S.; Alonso, J.F.; Nowak, R.; Russi, A.; Mañanas, M.A. Annual International Conference of the IEEE Engineering in Medicine and Biology Society p. 5970-5973 DOI: 10.1109/EMBC.2013.6610912 Presentation's date: 2013-07 Presentation of work at congresses
Magnetoencephalography is a technique that can noninvasively measure the brain signal. There are many advantages of using this technique rather than similar procedures such as the EEG for the evaluation of medical diseases. However, one of its main problems is its high sensitivity to sources causing metallic distortion of the signal, and the removal of this type of artifacts remains unsolved. In this study a technique for reducing metallic interference was presented. This algorithm was based on AMUSE, a second order blind source separation method, and a procedure for choosing the artifactual independent components was also presented. The results showed that the elimination of these artifacts would be possible by means of the application of this AMUSE-based interference reduction procedure
sEMG signal has been widely used in different applications in kinesiology and rehabilitation as well as in the control of human-machine interfaces. In general, the signals are recorded with bipolar electrodes located in different muscles. However, such configuration may disregard some aspects of the spatial distribution of the potentials like location of innervation zones and the manifestation of inhomogineties in the control of the muscular fibers. On the other hand, the spatial distribution of motor unit action potentials has recently been assessed with activation maps obtained from High Density EMG signals (HD-EMG), these lasts recorded with arrays of closely spaced electrodes. The main objective of this work is to analyze patterns in the activation maps, associating them with four movement directions at the elbow joint and with different strengths of those tasks. Although the activation pattern can be assessed with bipolar electrodes, HD-EMG maps could enable the extraction of features that depend on the spatial distribution of the potentials and on the load-sharing between muscles, in order to have a better differentiation between tasks and effort levels.
sEMG signal has been widely used in different applications in kinesiology and rehabilitation
as well as in the control of human-machine interfaces. In general, the signals are recorded
with bipolar electrodes located in different muscles. However, such configuration may
disregard some aspects of the spatial distribution of the potentials like location of innervation
zones and the manifestation of inhomogineties in the control of the muscular fibers. On the
other hand, the spatial distribution of motor unit action potentials has recently been assessed
with activation maps obtained from High Density EMG signals (HD-EMG), these lasts
recorded with arrays of closely spaced electrodes. The main objective of this work is to
analyze patterns in the activation maps, associating them with four movement directions at
the elbow joint and with different strengths of those tasks. Although the activation pattern can
be assessed with bipolar electrodes, HD-EMG maps could enable the extraction of features
that depend on the spatial distribution of the potentials and on the load-sharing between
muscles, in order to have a better differentiation between tasks and effort levels.
An experimental protocol consisting of isometric contractions at three levels of effort during
flexion, extension, supination and pronation at the elbow joint was designed and HD-EMG signals were recorded with 2D electrode arrays on different upper-limb muscles. Techniques
for the identification and interpolation of artifacts are explained, as well as a method for the
segmentation of the activation areas. In addition, variables related to the intensity and spatial
distribution of the maps were obtained, as well as variables associated to signal power of
traditional single bipolar recordings. Finally, statistical tests were applied in order to assess
differences between information extracted from single bipolar signals or from HD-EMG
maps and to analyze differences due to type of task and effort level.
Significant differences were observed between EMG signal power obtained from single
bipolar configuration and HD-EMG and better results regarding the identification of tasks
and effort levels were obtained with the latter. Additionally, average maps for a population of
12 subjects were obtained and differences in the co-activation pattern of muscles were found
not only from variables related to the intensity of the maps but also to their spatial
Intensity and spatial distribution of HD-EMG maps could be useful in applications where the
identification of movement intention and its strength is needed, for example in robotic-aided
therapies or for devices like powered- prostheses or orthoses. Finally, additional data
transformations or other features are necessary in order to improve the performance of tasks identification.
Rojas, M.; Mañanas, M.A.; Alonso, J.F.; Merletti, R. Journal of electromyography and kinesiology Vol. 23, num. 1, p. 33-42 DOI: 10.1016/j.jelekin.2012.06.009 Date of publication: 2012-07 Journal article
Identification of motion intention and muscle activation strategy is necessary to control human–machine
interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based
training programs. Pattern recognition from sEMG signals has been extensively investigated in the last
decades, however, most of the studies did not take into account different strengths and EMG distributions
associated to the intended task. The identification of such quantities could be beneficial for the training of
the subject or the control of assistive devices. Recent studies have shown the need to improve patternrecognition
classification by reducing sensitivity to changes in the exerted strength, muscle-electrode
shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can
provide much more information than electrode pairs for inferring not only motion intention but also
the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution
of motor unit action potentials within a single muscle because of it.
The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated
with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b)
the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar
HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original
classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for
each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity
and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial
distribution-based features by themselves are not sufficient to classify the type of task or the effort level
with an acceptable accuracy; however, when calculated with the ‘‘isolated masses’’ method proposed in
this study and combined with intensity-base features, the performance of the classifier is improved. The
classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range
of effort level developed by patients with neuromuscular disorders, showing that intention end effort of
motion can be estimated from HD-EMG maps and applied in rehabilitation.
Rationale Quantitative analysis of electroencephalographic
signals (EEG) and their interpretation constitute a helpful
tool in the assessment of the bioavailability of psychoactive
drugs in the brain. Furthermore, psychotropic drug groups
have typical signatures which relate biochemical mechanisms
with specific EEG changes.
Objectives To analyze the pharmacological effect of a dose
of alprazolam on the connectivity of the brain during
wakefulness by means of linear and nonlinear approaches.
Methods EEG signals were recorded after alprazolam
administration in a placebo-controlled crossover clinical
trial. Nonlinear couplings assessed by means of corrected
cross-conditional entropy were compared to linear couplings
measured with the classical magnitude squared
Results Linear variables evidenced a statistically significant
drug-induced decrease, whereas nonlinear variables showed
significant increases. All changes were highly correlated to
drug plasma concentrations. The spatial distribution of the
observed connectivity changes clearly differed from a
previous study: changes before and after the maximum
drug effect were mainly observed over the anterior half of
the scalp. Additionally, a new variable with very low
computational cost was defined to evaluate nonlinear
coupling. This is particularly interesting when all pairs of
EEG channels are assessed as in this study.
Conclusions Results showed that alprazolam induced
changes in terms of uncoupling between regions of the
scalp, with opposite trends depending on the variables:
decrease in linear ones and increase in nonlinear features.
Maps provided consistent information about the way brain
changed in terms of connectivity being definitely necessary
to evaluate separately linear and nonlinear interactions.
Alonso, J.F.; Mañanas, M.A.; Rojas, M.; Bruce, E. Journal of electromyography and kinesiology Vol. 21, num. 6, p. 1064-1073 DOI: 10.1016/j.jelekin.2011.07.004 Date of publication: 2011-12 Journal article
Most biological systems are complex and consist of several interconnected parts whose links can contain additional information which can be hidden from the observer. As a result of the interactions between elements, emergent properties that cannot be explained by the characteristics of isolated elements can arise.
Current clinical applications record a high number of different signals that contain information about these physiological systems, providing multichannel data whose interactions can be studied by classical reference methods, generally linear, as the correlation analysis and spectral coherence, and other nonlinear methods that are being defined and developed during recent years, such as nonlinear prediction, entropies, mutual information and phase synchronization.
The development, improvement and application of new analytical techniques is a field with obvious social and technological interest, especially when performed by noninvasive techniques, which can improve the processes of rehabilitation and clinical therapy, and also help the development of new diagnostic tools.
In this thesis new indexes have been defined in order to evaluate:
* The coordination of respiratory muscles in healthy subjects and patients with obstructive sleep apnea syndrome (OSAS) during an effort ventilatory protocol.
* The effect on functional connectivity of the brain after administration of a psychoactive drug.
* The changes caused by Alzheimer's disease (AD) in the connectivity of the brain.
Respiratory muscles provide the mechanical energy that supports respiration. The evaluation of interactions between electromyographic (EMG) and mechanomiographic (MMG) signals of different respiratory muscles, genioglossus, sternomastoid and diaphragm, has allowed the discrimination of coordination patterns of OSAS patients with respect to healthy subjects at low, medium and high respiratory effort during while awake.
Analysis and characterization of the electroencephalographic (EEG) and magnetoencephalographic (MEG) signals allows the understanding of brain function to assist in the process of clinical diagnosis of disorders in neurology, psychiatry and pharmacology. In this thesis the interactions within and between different brain regions have been assessed, using new nonlinear indexes which have managed to reflect changes over time in the brain after administration of alprazolam, and to characterize andto differentiate brain connectivity of AD patients with respect to healthy subjects.
La mayoría de sistemas biológicos son sistemas complejos que constan de diversas partes interconectadas cuyos vínculos pueden contener información adicional y oculta al observador. Como resultado de estas interacciones entre elementos surgen
propiedades emergentes, que no pueden explicarse a partir de las características de los elementos aislados.
Las aplicaciones clínicas actuales registran un elevado número de señales diferentes que contienen información sobre estos sistemas fisiológicos, cosa que permite disponer de datos multicanal, cuyas interacciones pueden ser estudiadas mediante métodos clásicos de referencia generalmente lineales, como el análisis de correlación y la coherencia espectral, u otros
métodos no lineales que están siendo definidos y desarrollados durante los últimos años, como la predicción no lineal, las entropías, la información mutua o la sincronización de fase.
El desarrollo, mejora y aplicación de nuevas técnicas de análisis constituye un campo con evidente interés social y tecnológico, en especial cuando se realiza mediante técnicas no invasivas, que puede proporcionar mejoras en los procesos de rehabilitación y terapia clínica, así como contribuir a desarrollar herramientas de ayuda al diagnóstico.
En esta tesis se han definido nuevos índices no lineales que han permitido evaluar:
* La coordinación de los músculos respiratorios en sujetos sanos y pacientes con síndrome de apnea obstructiva del sueño (SAOS) durante un protocolo ventilatorio de esfuerzo.
* El efecto en la conectividad funcional del cerebro tras la administración de un fármaco psicoactivo.
* Los cambios provocados por la enfermedad de Alzheimer (EA) en la conectividad del cerebro.
La musculatura respiratoria proporciona la energía mecánica que soporta la respiración. La evaluación de las interacciones entre señales electromiográficas (EMG) y mecanomiográficas (MMG) de diferentes músculos respiratorios -geniogloso, esternocleidomastoideo y diafragma- ha permitido diferenciar el patrón de coordinación de los pacientes con SAOS de los sujetos sanos a niveles bajos, medios y altos de esfuerzo respiratorio durante vigilia.
El análisis y caracterización de las señales electroencefalográficas (EEG) y magnetoencefalográficas (MEG) permite la comprensión de la función cerebral para ayudar en el proceso de diagnóstico clínico de disfunciones en neurología, psiquiatría y farmacología. En esta tesis se han evaluado las interacciones en y entre diferentes regiones cerebrales mediante nuevos índices no lineales que han conseguido reflejar los cambios producidos a lo largo del tiempo en el cerebro tras la administración del fármaco alprazolam, así como caracterizar y diferenciar la conectividad cerebral de los pacientes con EA con respecto a
Las herramientas utilizadas en las aplicaciones mencionadas se basan en las siguientes técnicas de análisis no lineal:
* La función de información mutua cruzada, el equivalente no lineal de la función de correlación cruzada, que cuantifica la información compartida entre dos variables aleatorias.
* La entropía condicional corregida cruzada, una medida que cuantifica la información restante contenida en una variable aleatoria cuando se conoce totalmente otra variable relacionada, y por lo tanto es una medida complementaria de la información mutua.
* La predicción no lineal basada en modelos localmente lineales, una herramienta matemática que permite deducir la evolución de una serie temporal en función de muestras anteriores.
Los nuevos índices desarrollados han demostrado la necesidad de evaluar las interacciones en los sistemas biológicos y fisiológicos tanto con métodos lineales como no lineales, para obtener una evaluación más completa de la dinámica subyacente y ayudar en los procesos de diagnóstico de patologías y en el procedimiento de evaluación psicofarmacológica.
Rojas, M.; Garcia, M.; Alonso, J.F.; Marin, J.; Mañanas, M.A. Revista iberoamericana de automática e informática industrial Vol. 8, num. 2, p. 35-44 DOI: 10.4995/RIAI.2011.02.06 Date of publication: 2011-04-08 Journal article
Jane, R.; Fiz, J.; Rigau, J.; Alonso, J.F.; Sopena, F.; Solà Soler, J.; Vilasseca, I.; Abad, J.; Montserrat, J. M.; Morera, J. European Respiratory Society Annual Congress Presentation's date: 2010-09 Presentation of work at congresses
Quantitative analysis of human electroencephalogram (EEG) is a valuable method for
evaluating psychopharmacological agents. Although the effects of different drug classes on EEG
spectra are already known, interactions between brain locations remain unclear. In this work, cross
mutual information function and appropriate surrogate data were applied to assess linear and
nonlinear couplings between EEG signals. The main goal was to evaluate the pharmacological effects
of alprazolam on brain connectivity during wakefulness in healthy volunteers using a cross-over,
placebo-controlled design. Eighty-five pairs of EEG leads were selected for the analysis, and connectivity
was evaluated inside anterior, central, and posterior zones of the scalp. Connectivity between these
zones and interhemispheric connectivity were also measured. Results showed that alprazolam induced
significant changes in EEG connectivity in terms of information transfer in comparison with placebo.
Trends were opposite depending on the statistical characteristics: decreases in linear connectivity and
increases in nonlinear couplings. These effects were generally spread over the entire scalp. Linear
changes were negatively correlated, and nonlinear changes were positively correlated with drug
plasma concentrations; the latter showed higher correlation coefficients. The use of both linear
and nonlinear approaches revealed the importance of assessing changes in EEG connectivity as
this can provide interesting information about psychopharmacological effects.
El análisis del electroencefalograma humano constituye una herramienta muy útil para la evaluación de la biodisponibilidad de un fármaco en el cerebro. Se ha aplicado el calculo de entropía condicional a señales electroencefalográficas para la evaluación del acoplamiento cerebral tras la administración de un fármaco. El principal objetivo fue analizar el efecto farmacológico de una dosis de alprazolam en el cerebro durante vigilia. Los resultados preliminares que se presentan en este trabajo muestran diferencias estadísticamente significativas entre los estados correspondientes a placebo y fármaco. Las variables no lineales mostraron una buena correlación con la evolución temporal de efectos esperados.
Alonso, J.F.; Mañanas, M.A.; Romero, S.; Hoyer, D.; Topor, Z.; Bruce, E. Congreso Anual de la Sociedad Española de Ingeniería Biomédica p. 96 Presentation's date: 2007-11-14 Presentation of work at congresses
Alonso, J.F.; Mañanas, M.A.; Romero, S.; Riba, J.; Barbanoj, M.J.; Hoyer, D. Annual International Conference of the IEEE Engineering in Medicine and Biology Society p. 6187-6190 Presentation of work at congresses
Mañanas, M.A.; Alonso, J.F.; Topor, Z.; Bruce, E.; Houtz, P.; Caminal, P. Annual International Conference of the IEEE Engineering in Medicine and Biology Society p. 3203-3206 Presentation of work at congresses