Realistic mean field forward predictions for the integration of co-registered EEG/fMRI

Brain activity can be measured non-invasively with functional imaging techniques. Each pixel in such an image represents a neural mass of about 105 to 107 neurons. Mean field models (MFMs) approximate their activity by averaging out neural variability while retaining salient underlying features, like neurotransmitter kinetics. However, MFMs incorporating the regional variability, realistic geometry and connectivity of cortex have so far appeared intractable. This lack of biological realism has led to a focus on gross temporal features of the EEG. We address these impediments and showcase a "proof of principle" forward prediction of co-registered EEG/fMRI for a full-size human cortex in a realistic head model with anatomical connectivity, see figure 1. MFMs usually assume homogeneous neural masses, isotropic long-range connectivity and simplistic signal expression to allow rapid computation with partial differential equations. But these approximations are insufficient in particular for the high spatial resolution obtained with fMRI, since different cortical areas vary in their architectonic and dynamical properties, have complex connectivity, and can contribute non-trivially to the measured signal. Our code instead supports the local variation of model parameters and freely chosen connectivity for many thousand triangulation nodes spanning a cortical surface extracted from structural MRI. This allows the introduction of realistic anatomical and physiological parameters for cortical areas and their connectivity, including both intra- and inter-area connections. Proper cortical folding and conduction through a realistic head model is then added to obtain accurate signal expression for a comparison to experimental data. To showcase the synergy of these computational developments, we predict simultaneously EEG and fMRI BOLD responses by adding an established model for neurovascular coupling and convolving "Balloon-Windkessel" hemodynamics. We also incorporate regional connectivity extracted from the CoCoMac database [1]. Importantly, these extensions can be easily adapted according to future insights and data. Furthermore, while our own simulation is based on one specific MFM [2], the computational framework is general and can be applied to models favored by the user. Finally, we provide a brief outlook on improving the integration of multi-modal imaging data through iterative fits of a single underlying MFM in this realistic simulation framework.

Electrorhythmogenesis and anaesthesia in a physiological mean field theory

We solve eight partial-differential, two-dimensional, nonlinear mean field equations, which describe the dynamics of large populations of cortical neurons. Linearized versions of these equations have been used to generate the strong resonances observed in the human EEG, in particular the α-rhythm (8–), with physiologically plausible parameters. We extend these results here by numerically solving the full equations on a cortex of realistic size, which receives appropriately “colored” noise as extra-cortical input. A brief summary of the numerical methods is provided. As an outlook to future applications, we explain how the effects of GABA-enhancing general anaesthetics can be simulated and present first results.

Separating heart and brain: on the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals

Objective. Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the in vivo assessment of functional activity of the cerebral cortex as well as in the field of brain–computer interface (BCI) research. A common challenge for the utilization of fNIRS in these areas is a stable and reliable investigation of the spatio-temporal hemodynamic patterns. However, the recorded patterns may be influenced and superimposed by signals generated from physiological processes, resulting in an inaccurate estimation of the cortical activity. Up to now only a few studies have investigated these influences, and still less has been attempted to remove/reduce these influences. The present study aims to gain insights into the reduction of physiological rhythms in hemodynamic signals (oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb)). Approach. We introduce the use of three different signal processing approaches (spatial filtering, a common average reference (CAR) method; independent component analysis (ICA); and transfer function (TF) models) to reduce the influence of respiratory and blood pressure (BP) rhythms on the hemodynamic responses. Main results. All approaches produce large reductions in BP and respiration influences on the oxy-Hb signals and, therefore, improve the contrast-to-noise ratio (CNR). In contrast, for deoxy-Hb signals CAR and ICA did not improve the CNR. However, for the TF approach, a CNR-improvement in deoxy-Hb can also be found. Significance. The present study investigates the application of different signal processing approaches to reduce the influences of physiological rhythms on the hemodynamic responses. In addition to the identification of the best signal processing method, we also show the importance of noise reduction in fNIRS data.

Sparse density estimation on multinomial manifold combining local component analysis

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since the constraint on the mixing coefficients of a finite mixture model is on the multinomial manifold, we then use the well-known Riemannian trust-region algorithm to find the set of sparse mixing coefficients. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Ecological niche modeling and principal component analysis of Krameria Loefl. (Krameriaceae)

Krameria plants are found in arid regions of the Americas and present a floral system that attracts oil-collecting bees. Niche modeling and multivariate tools were applied to examine ecological and geographical aspects of the 18 species of this genus, using occurrence data obtained from herbaria and literature. Niche modeling showed the potential areas of occurrence for each species and the analysis of climatic variables suggested that North American species occur mostly in deserted or xeric ecoregions with monthly precipitation below 140 mm and large temperature ranges. South American species are mainly found in deserted ecoregions and subtropical savannas where monthly precipitation often exceeds 150 mm and temperature ranges are smaller. Principal Component Analysis (PCA) performed with values of temperature and precipitation showed that the distribution limits of Krameria species are primarily associated with maximum and minimum temperatures. Modeling of Krameria species proved to be a useful tool for analyzing the influence of the ecological niche variables in the geographical distribution of species, providing new information to guide future investigations. (C) 2011 Elsevier Ltd. All rights reserved.

Chemotaxonomic relationships in Celastraceae inferred from principal component analysis (PCA) and partial least squares (PLS)

This paper describes a chemotaxonomic analysis of a database of triterpenoid compounds from the Celastraceae family using principal component analysis (PCA). The numbers of occurrences of thirty types of triterpene skeleton in different tribes of the family were used as variables. The study shows that PCA applied to chemical data can contribute to an intrafamilial classification of Celastraceae, once some questionable taxa affinity was observed, from chemotaxonomic inferences about genera and they are in agreement with the phylogeny previously proposed. The inclusion of Hippocrateaceae within Celastraceae is supported by the triterpene chemistry.

Alocação dinâmica ótima com momentos de ordem superior para a estratégia de carry trade

O objetivo do presente trabalho é verificar se, ao levar-se em consideração momentos de ordem superior (assimetria e curtose) na alocação de uma carteira de carry trade, há ganhos em relação à alocação tradicional que prioriza somente os dois primeiros momentos (média e variância). A hipótese da pesquisa é que moedas de carry trade apresentam retornos com distribuição não-Normal, e os momentos de ordem superior desta têm uma dinâmica, a qual pode ser modelada através de um modelo da família GARCH, neste caso IC-GARCHSK. Este modelo consiste em uma equação para cada momento condicional dos componentes independentes, explicitamente: o retorno, a variância, a assimetria, e a curtose. Outra hipótese é que um investidor com uma função utilidade do tipo CARA (constant absolute risk aversion), pode tê-la aproximada por uma expansão de Taylor de 4ª ordem. A estratégia do trabalho é modelar a dinâmica dos momentos da série dos logartimos neperianos dos retornos diários de algumas moedas de carry trade através do modelo IC-GARCHSK, e estimar a alocação ótima da carteira dinamicamente, de tal forma que se maximize a função utilidade do investidor. Os resultados mostram que há ganhos sim, ao levar-se em consideração os momentos de ordem superior, uma vez que o custo de oportunidade desta foi menor que o de uma carteira construída somente utilizando como critérios média e variância.

Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

LOPES-DOS-SANTOS, V. , CONDE-OCAZIONEZ, S. ; NICOLELIS, M. A. L. , RIBEIRO, S. T. , TORT, A. B. L. . Neuronal assembly detection and cell membership specification by principal component analysis. Plos One, v. 6, p. e20996, 2011.

Detecting cell assemblies in large neuronal populations

Recent progress in the technology for single unit recordings has given the neuroscientific community theopportunity to record the spiking activity of large neuronal populations. At the same pace, statistical andmathematical tools were developed to deal with high-dimensional datasets typical of such recordings.A major line of research investigates the functional role of subsets of neurons with significant co-firingbehavior: the Hebbian cell assemblies. Here we review three linear methods for the detection of cellassemblies in large neuronal populations that rely on principal and independent component analysis.Based on their performance in spike train simulations, we propose a modified framework that incorpo-rates multiple features of these previous methods. We apply the new framework to actual single unitrecordings and show the existence of cell assemblies in the rat hippocampus, which typically oscillate attheta frequencies and couple to different phases of the underlying field rhythm

Reduction capability of soil humic substances from the Rio Negro basin, Brazil, towards Hg(II) studied by a multimethod approach and principal component analysis (PCA)

This paper characterizes humic substances (HS) extracted from soil samples collected in the Rio Negro basin in the state of Amazonas, Brazil, particularly investigating their reduction capabilities towards Hg(II) in order to elucidate potential mercury cycling/volatilization in this environment. For this reason, a multimethod approach was used, consisting of both instrumental methods (elemental analysis, EPR, solid-state NMR, FIA combined with cold-vapor AAS of Hg(0)) and statistical methods such as principal component analysis (PCA) and a central composite factorial planning method. The HS under study were divided into groups, complexing and reducing ones, owing to different distribution of their functionalities. The main functionalities (cor)related with reduction of Hg(II) were phenolic, carboxylic and amide groups, while the groups related with complexation of Hg(II) were ethers, hydroxyls, aldehydes and ketones. The HS extracted from floodable regions of the Rio Negro basin presented a greater capacity to retain (to complex, to adsorb physically and/or chemically) Hg(II), while nonfloodable regions showed a greater capacity to reduce Hg(II), indicating that HS extracted from different types of regions contribute in different ways to the biogeochemical mercury cycle in the basin of the mid-Rio Negro, AM, Brazil. (c) 2007 Published by Elsevier B.V.

Uma implementação da análise de componentes independentes em plataforma de hardware reconfigurável

Blind Source Separation (BSS) refers to the problem of estimate original signals from observed linear mixtures with no knowledge about the sources or the mixing process. Independent Component Analysis (ICA) is a technique mainly applied to BSS problem and from the algorithms that implement this technique, FastICA is a high performance iterative algorithm of low computacional cost that uses nongaussianity measures based on high order statistics to estimate the original sources. The great number of applications where ICA has been found useful reects the need of the implementation of this technique in hardware and the natural paralelism of FastICA favors the implementation of this algorithm on digital hardware. This work proposes the implementation of FastICA on a reconfigurable hardware platform for the viability of it's use in blind source separation problems, more specifically in a hardware prototype embedded in a Field Programmable Gate Array (FPGA) board for the monitoring of beds in hospital environments. The implementations will be carried out by Simulink models and it's synthesizing will be done through the DSP Builder software from Altera Corporation.

3-D reconstruction by extended depth-of-field in failure analysis - Case study II: Fractal analysis of interlaminar fracture in carbon/epoxy composites

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

3-D reconstruction by extended depth-of-field in failure analysis - Case study I: Qualitative fractographic investigation of fractured bolts in a partial valve

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mean-field model for Josephson oscillation in a Bose-Einstein condensate on an one-dimensional optical trap

Using the axially-symmetric time-dependent Gross-Pitaevskii equation we study the phase coherence in a repulsive Bose-Einstein condensate (BEC) trapped by a harmonic and an one-dimensional optical lattice potential to describe the experiment by Cataliotti et al. on atomic Josephson oscillation [Science 293, 843 (2001)]. The phase coherence is maintained after the BEC is set into oscillation by a small displacement of the magnetic trap along the optical lattice. The phase coherence in the presence of oscillating neutral current across an array of Josephson junctions manifests in an interference pattern formed upon free expansion of the BEC. The numerical response of the system to a large displacement of the magnetic trap is a classical transition from a coherent superfluid to an insulator regime and a subsequent destruction of the interference pattern in agreement With the more recent experiment by Cataliotti et al. [New J. Phys. 5, 71 (2003)].

Mean-field description of a dynamical collapse of a fermionic condensate in a trapped boson-fermion mixture

We suggest a time-dependent dynamical mean-field-hydrodynamic model for the collapse of a trapped boson-fermion condensate and perform numerical simulation based on it to understand some aspects of the experiment by Modugno et al. [Science 297, 2240 (2002)] on the collapse of the fermionic condensate in the K-40-Rb-87 mixture. We show that the mean-field model explains the formation of a stationary boson-fermion condensate at zero temperature with relative sizes compatible with experiment. This model is also found to yield a faithful representation of the collapse dynamics in qualitative agreement with experiment. In particular we consider the collapse of the fermionic condensate associated with (a) an increase of the number of bosonic atoms as in the experiment and (b) an increase of the attractive boson-fermion interaction using a Feshbach resonance. Suggestion for experiments of fermionic collapse using a Feshbach resonance is made.