Frequency Domain Connectivity Identification: An Application of Partial Directed Coherence in fMRI

Functional magnetic resonance imaging (fMRI) has become an important tool in Neuroscience due to its noninvasive and high spatial resolution properties compared to other methods like PET or EEG. Characterization of the neural connectivity has been the aim of several cognitive researches, as the interactions among cortical areas lie at the heart of many brain dysfunctions and mental disorders. Several methods like correlation analysis, structural equation modeling, and dynamic causal models have been proposed to quantify connectivity strength. An important concept related to connectivity modeling is Granger causality, which is one of the most popular definitions for the measure of directional dependence between time series. In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap. PDC is a frequency domain counterpart of Granger causality and has become a very prominent tool in EEG studies. The achieved frequency decomposition of connectivity is useful in separating interactions from neural modules from those originating in scanner noise, breath, and heart beating. Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity. Hum Brain Mapp 30:452-461, 2009. (C) 2007 Wiley-Liss, Inc.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Abnormal Visual Activation in Parkinson`s Disease Patients

Among nonmotor symptoms observed in Parkinson`s disease (PD) dysfunction in the visual system, including hallucinations, has a significant impact in their quality of life. To further explore the visual system in PD patients we designed two fMRI experiments comparing 18 healthy volunteers with 16 PD patients without visual complaints in two visual fMRI paradigms: the flickering checkerboard task and a facial perception paradigm. PD patients displayed a decreased activity in the primary visual cortex (Broadmann area 17) bilaterally as compared to healthy volunteers during flickering checkerboard task and increased activity in fusiform gyms (Broadmann area 37) during facial perception paradigm. Our findings confirm the notion that PD patients show significant changes in the visual cortex system even before the visual symptoms are clinically evident. Further studies are necessary to evaluate the contribution of these abnormalities to the development visual symptoms in PD. (C) 2010 Movement Disorder Society

Abnormally increased effective connectivity between parahippocampal gyrus and ventromedial prefrontal regions during emotion labeling in bipolar disorder

Emotional liability and mood dysregulation characterize bipolar disorder (BID), yet no study has examined effective connectivity between parahippocampal gyrus and prefrontal cortical regions in ventromedial and dorsal/lateral neural systems subserving mood regulation in BD. Participants comprised 46 individuals (age range: 18-56 years): 21 with a DSM-IV diagnosis of BID, type I currently remitted; and 25 age- and gender-matched healthy controls (HC). Participants performed an event-related functional magnetic resonance imaging paradigm, viewing mild and intense happy and neutral faces. We employed dynamic causal modeling (I)CM) to identify significant alterations in effective connectivity between BD and HC. Bayes model selection was used to determine the best model. The right parahippocampal gyrus (PHG) and right subgenual cingulate gyrus (sgCG) were included as representative regions of the ventromedial neural system. The right dorsolateral prefrontal cortex (DLPFC) region was included as representative of the dorsal/lateral neural system. Right PHG-sgCG effective connectivity was significantly greater in BD than HC, reflecting more rapid, forward PHG-sgCG signaling in BD than HC. There was no between-group difference in sgCG-DLPFC effective connectivity. In BD, abnormally increased right PHG-sgCG effective connectivity and reduced right PHG activity to emotional stimuli suggest a dysfunctional ventromedial neural system implicated in early stimulus appraisal, encoding and automatic regulation of emotion that may represent a pathophysiological functional neural mechanism for mood dysregulation in BD. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Prefrontal cortical and striatal activity to happy and fear faces in bipolar disorder is associated with comorbid substance abuse and eating disorder

Background: The spectrum approach was used to examine contributions of comorbid symptom dimensions of substance abuse and eating disorder to abnormal prefrontal-cortical and subcortical-striatal activity to happy and fear faces previously demonstrated in bipolar disorder (BD). Method: Fourteen remitted BD-type I and sixteen healthy individuals viewed neutral, mild and intense happy and fear faces in two event-related fMRI experiments. All individuals completed Substance-Use and Eating-Disorder Spectrum measures. Region-of-Interest analyses for bilateral prefrontal and subcortical-striatal regions were performed. Results: BD individuals scored significantly higher on these spectrum measures than healthy individuals (p<0.05), and were distinguished by activity in prefrontal and subcortical-striatal regions. BD relative to healthy individuals showed reduced dorsal prefrontal-cortical activity to all faces. Only BD individuals showed greater subcortical-striatal activity to happy and neutral faces. In BD individuals, negative correlations were shown between substance use severity and right PFC activity to intense happy faces (p<0.04), and between substance use severity and right caudate nucleus activity to neutral faces (p<0.03). Positive correlations were shown between eating disorder and right ventral putamen activity to intense happy (p<0.02) and neutral faces (p<0.03). Exploratory analyses revealed few significant relationships between illness variables and medication upon neural activity in BID individuals. Limitations: Small sample size of predominantly medicated BD individuals. Conclusion: This study is the first to report relationships between comorbid symptom dimensions of substance abuse and eating disorder and prefrontal-cortical and subcortical-striatal activity to facial expressions in BD. Our findings suggest that these comorbid features may contribute to observed patterns of functional abnormalities in neural systems underlying mood regulation in BD. (C) 2009 Elsevier B.V. All rights reserved.

Abnormal Amygdala-Prefrontal Effective Connectivity to Happy Faces Differentiates Bipolar from Major Depression

Background: Bipolar disorder is frequently misdiagnosed as major depressive disorder, delaying appropriate treatment and worsening outcome for many bipolar individuals. Emotion dysregulation is a core feature of bipolar disorder. Measures of dysfunction in neural systems supporting emotion regulation might therefore help discriminate bipolar from major depressive disorder. Methods: Thirty-one depressed individuals-15 bipolar depressed (BD) and 16 major depressed (MDD), DSM-IV diagnostic criteria, ages 18-55 years, matched for age, age of illness onset, illness duration, and depression severity-and 16 age- and gender-matched healthy control subjects performed two event-related paradigms: labeling the emotional intensity of happy and sad faces, respectively. We employed dynamic causal modeling to examine significant among-group alterations in effective connectivity (EC) between right- and left-sided neural regions supporting emotion regulation: amygdala and orbitomedial prefrontal cortex (OMPFC). Results: During classification of happy faces, we found profound and asymmetrical differences in EC between the OMPFC and amygdala. Left-sided differences involved top-down connections and discriminated between depressed and control subjects. Furthermore, greater medication load was associated with an amelioration of this abnormal top-down EC. Conversely, on the right side the abnormality was in bottom-up EC that was specific to bipolar disorder. These effects replicated when we considered only female subjects. Conclusions: Abnormal, left-sided, top-down OMPFC-amygdala and right-sided, bottom-up, amygdala-OMPFC EC during happy labeling distinguish BD and MDD, suggesting different pathophysiological mechanisms associated with the two types of depression.

Escitalopram prolonged fear induced by simulated public speaking and released hypothalamic-pituitary-adrenal axis activation

Simulated public speaking (SPS) test is sensitive to drugs that interfere with serotonin-mediated neurotransmission and is supposed to recruit neural systems involved in panic disorder. The study was aimed at evaluating the effects of escitalopram, the most selective serotonin-selective reuptake inhibitor available, in SPS. Healthy males received, in a double-blind, randomized design, placebo (n = 12), 10 (n = 17) or 20 (n = 14) mg of escitalopram 2 hours before the test. Behavioural, autonomic and neuroendocrine measures were assessed. Both doses of escitalopram did not produce any effect before or during the speech but prolonged the fear induced by SPS. The test itself did not significantly change cortisol and prolactin levels but under the higher dose of escitalopram, cortisol and prolactin increased immediately after SPS. This fear-enhancing effect of escitalopram agrees with previously reported results with less selective serotonin reuptake inhibitors and the receptor antagonist ritanserin, indicating that serotonin inhibits the fear of speaking in public.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Towards modular and coordinated manufacturing systems oriented to services

Nowadays, there is a trend for industry reorganization in geographically dispersed systems, carried out of their activities with autonomy. These systems must maintain coordinated relationship among themselves in order to assure an expected performance of the overall system. Thus, a manufacturing system is proposed, based on ""web services"" to assure an effective orchestration of services in order to produce final products. In addition, it considers special functions, such as teleoperation and remote monitoring, users` online request, among others. Considering the proposed system as discrete event system (DES), techniques derived from Petri nets (PN), including the Production Flow Schema (PFS), can be used in a PFS/PN approach for modeling. The system is approached in different levels of abstraction: a conceptual model which is obtained by applying the PFS technique and a functional model which is obtained by applying PN. Finally, a particular example of the proposed system is presented.

Central chemoreceptors and neural mechanisms of cardiorespiratory control

The arterial partial pressure (P CO2) of carbon dioxide is virtually constant because of the close match between the metabolic production of this gas and its excretion via breathing. Blood gas homeostasis does not rely solely on changes in lung ventilation, but also to a considerable extent on circulatory adjustments that regulate the transport of CO2 from its sites of production to the lungs. The neural mechanisms that coordinate circulatory and ventilatory changes to achieve blood gas homeostasis are the subject of this review. Emphasis will be placed on the control of sympathetic outflow by central chemoreceptors. High levels of CO2 exert an excitatory effect on sympathetic outflow that is mediated by specialized chemoreceptors such as the neurons located in the retrotrapezoid region. In addition, high CO2 causes an aversive awareness in conscious animals, activating wake-promoting pathways such as the noradrenergic neurons. These neuronal groups, which may also be directly activated by brain acidification, have projections that contribute to the CO2-induced rise in breathing and sympathetic outflow. However, since the level of activity of the retrotrapezoid nucleus is regulated by converging inputs from wake-promoting systems, behavior-specific inputs from higher centers and by chemical drive, the main focus of the present manuscript is to review the contribution of central chemoreceptors to the control of autonomic and respiratory mechanisms.

Stability region bifurcations of nonlinear autonomous dynamical systems: Type-zero saddle-node bifurcations

The behavior of stability regions of nonlinear autonomous dynamical systems subjected to parameter variation is studied in this paper. In particular, the behavior of stability regions and stability boundaries when the system undergoes a type-zero sadle-node bifurcation on the stability boundary is investigated in this paper. It is shown that the stability regions suffer drastic changes with parameter variation if type-zero saddle-node bifurcations occur on the stability boundary. A complete characterization of these changes in the neighborhood of a type-zero saddle-node bifurcation value is presented in this paper. Copyright (C) 2010 John Wiley & Sons, Ltd.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Neural network model for designing automotive devices using SMD LED

The advantages offered by the electronic component LED (Light Emitting Diode) have resulted in a quick and extensive application of this device in the replacement of incandescent lights. In this combined application, however, the relationship between the design variables and the desired effect or result is very complex and renders it difficult to model using conventional techniques. This paper consists of the development of a technique using artificial neural networks that makes it possible to obtain the luminous intensity values of brake lights using SMD (Surface Mounted Device) LEDs from design data. This technique can be utilized to design any automotive device that uses groups of SMD LEDs. The results of industrial applications using SMD LED are presented to validate the proposed technique.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.

Neural network-based H(infinity) control for fully actuated and underactuated cooperative manipulators

This paper develops H(infinity) control designs based on neural networks for fully actuated and underactuated cooperative manipulators. The neural networks proposed in this paper only adapt the uncertain dynamics of the robot manipulators. They work as a complement of the nominal model. The H(infinity) performance index includes the position errors as well the squeeze force errors between the manipulator end-effectors and the object, which represents a complete disturbance rejection scenario. For the underactuated case, the squeeze force control problem is more difficult to solve due to the loss of some degrees of manipulator actuation. Results obtained from an actual cooperative manipulator, which is able to work as a fully actuated and an underactuated manipulator, are presented. (C) 2008 Elsevier Ltd. All rights reserved.