Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts

Studies of the neural basis of intelligence have focused on comparing brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Here, the relation between mean tract‐based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest approach for 10 white‐matter fascicles along which the mTBFA was calculated. The study sample included 83 healthy individuals from the second wave of the Cuban Human Brain Mapping Project, whose WAIS‐III intelligence quotients and indices were obtained. Inspired by the “Watershed model” of intelligence, we employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, as mediated by latent variables summarizing the indices. Regularized MIMIC, used due to the limited sample size, selected relevant mTBFA by means of an elastic net penalty and achieved good fits to the data. Two latent variables were necessary to describe the indices: Fluid intelligence (Perceptual Organization and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Memory indices). Regularized MIMIC revealed effects of the forceps minor tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant effect of age on both latent variables

Mexico City and the biogeochemistry of global urbanization

Mexico City is far advanced in its urban evolution, and cities in currently developing nations may soon follow a similar course. This paper investigates the strengths and weaknesses of infrastructures for the emerging megacities. The major driving force for infrastructure change in Mexico City is concern over air quality. Air chemistry data from recent field campaigns have been used to calculate fluxes in the atmosphere of the Valley of Mexico, for compounds that are important to biogeochemistry including methane (CH4), carbon monoxide (CO), nonmethane hydrocarbons (NMHCs), ammonia (NH3), sulfur dioxide (SO2), nitrogen oxides (NOx and NOy), soot, and dust. Leakage of liquified petroleum gas approached 10% during sampling periods, and automotive pollutant sources in Mexico City were found to match those in developed cities, despite a lower vehicle-to-person ratio of 0.1. Ammonia is released primarily from residential areas, at levels sufficient to titrate pollutant acids into particles across the entire basin. Enhancements of reduced nitrogen and hydrocarbons in the vapor phase skew the distribution of NOy species towards lower average deposition velocities. Partly as a result, downwind nutrient deposition occurs on a similar scale as nitrogen fixation across Central America, and augments marine nitrate upwelling. Dust suspension from unpaved roads and from the bed of Lake Texcoco was found to be comparable to that occurring on the periphery of the Sahara, Arabian, and Gobi deserts. In addition, sodium chloride (NaCl) in the dust may support heterogeneous chlorine oxide (ClOx) chemistry. The insights from our Mexico City analysis have been tentatively applied to the upcoming urbanization of Asia

Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state

Polyfunctional T cell responses in children in early stages of chronic Trypanosoma cruzi infection contrast with monofunctional responses of long-term infected adults

Background: Adults with chronic Trypanosoma cruzi exhibit a poorly functional T cell compartment, characterized by monofunctional (IFN-γ-only secreting) parasite-specific T cells and increased levels of terminally differentiated T cells. It is possible that persistent infection and/or sustained exposure to parasites antigens may lead to a progressive loss of function of the immune T cells. Methodology/Principal Findings: To test this hypothesis, the quality and magnitude of T. cruzi-specific T cell responses were evaluated in T. cruzi-infected children and compared with long-term T. cruzi-infected adults with no evidence of heart failure. The phenotype of CD4+ T cells was also assessed in T. cruzi-infected children and uninfected controls. Simultaneous secretion of IFN-γ and IL-2 measured by ELISPOT assays in response to T. cruzi antigens was prevalent among T. cruzi-infected children. Flow cytometric analysis of co-expression profiles of CD4+ T cells with the ability to produce IFN-γ, TNF-α, or to express the co-stimulatory molecule CD154 in response to T. cruzi showed polyfunctional T cell responses in most T. cruzi-infected children. Monofunctional T cell responses and an absence of CD4+TNF-α+-secreting T cells were observed in T. cruzi-infected adults. A relatively high degree of activation and differentiation of CD4+ T cells was evident in T. cruzi-infected children. Conclusions/Significance: Our observations are compatible with our initial hypothesis that persistent T. cruzi infection promotes eventual exhaustion of immune system, which might contribute to disease progression in long-term infected subjects.Fil: Albareda, María Cecilia. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud. Instituto Nacional de Parasitología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: de Rissio, Ana María. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud. Instituto Nacional de Parasitología; ArgentinaFil: Tomas, Gonzalo. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud. Instituto Nacional de Parasitología; ArgentinaFil: Serjan, Alicia. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Juan A. Fernández"; ArgentinaFil: Alvarez, María Gabriela. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: Viotti, Rodolfo Jorge. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: Fichera, Laura Edith. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud. Instituto Nacional de Parasitología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Esteva, Mónica Inés. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud. Instituto Nacional de Parasitología; ArgentinaFil: Potente, Daniel Fernando. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: Armenti, Alejandro. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: Tarleton, Rick L.. University of Georgia; Estados UnidosFil: Laucella, Susana Adriana. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud. Instituto Nacional de Parasitología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention

LINE-1 Hypomethylation in Cancer Is Highly Variable and Inversely Correlated with Microsatellite Instability

BACKGROUND: Alterations in DNA methylation in cancer include global hypomethylation and gene-specific hypermethylation. It is not clear whether these two epigenetic errors are mechanistically linked or occur independently. This study was performed to determine the relationship between DNA hypomethylation, hypermethylation and microsatellite instability in cancer. METHODOLOGY/PRINCIPAL FINDINGS: We examined 61 cancer cell lines and 60 colorectal carcinomas and their adjacent tissues using LINE-1 bisulfite-PCR as a surrogate for global demethylation. Colorectal carcinomas with sporadic microsatellite instability (MSI), most of which are due to a CpG island methylation phenotype (CIMP) and associated MLH1 promoter methylation, showed in average no difference in LINE-1 methylation between normal adjacent and cancer tissues. Interestingly, some tumor samples in this group showed increase in LINE-1 methylation. In contrast, MSI-showed a significant decrease in LINE-1 methylation between normal adjacent and cancer tissues (P<0.001). Microarray analysis of repetitive element methylation confirmed this observation and showed a high degree of variability in hypomethylation between samples. Additionally, unsupervised hierarchical clustering identified a group of highly hypomethylated tumors, composed mostly of tumors without microsatellite instability. We extended LINE-1 analysis to cancer cell lines from different tissues and found that 50/61 were hypomethylated compared to peripheral blood lymphocytes and normal colon mucosa. Interestingly, these cancer cell lines also exhibited a large variation in demethylation, which was tissue-specific and thus unlikely to be resultant from a stochastic process. CONCLUSION/SIGNIFICANCE: Global hypomethylation is partially reversed in cancers with microsatellite instability and also shows high variability in cancer, which may reflect alternative progression pathways in cancer

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori