Cuban Neuroscience Center

Havana, Cuba

Cuban Neuroscience Center

Havana, Cuba
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Lage-Castellanos A.,Cuban Neuroscience Center | Martinez-Montes E.,Cuban Neuroscience Center | Hernandez-Cabrera J.A.,University of La Laguna | Galan L.,Cuban Neuroscience Center
Statistics in Medicine | Year: 2010

Current analysis of event-related potentials (ERP) data is usually based on the a priori selection of channels and time windows of interest for studying the differences between experimental conditions in the spatio-temporal domain. In this work we put forward a new strategy designed for situations when there is not a priori information about 'when' and 'where' these differences appear in the spatio-temporal domain, simultaneously testing numerous hypotheses, which increase the risk of false positives. This issue is known as the problem of multiple comparisons and has been managed with methods that control the false discovery rate (FDR), such as permutation test and FDR methods. Although the former has been previously applied, to our knowledge, the FDR methods have not been introduced in the ERP data analysis. Here we compare the performance (on simulated and real data) of permutation test and two FDR methods (Benjamini and Hochberg (BH) and local-fdr, by Efron). All these methods have been shown to be valid for dealing with the problem of multiple comparisons in the ERP analysis, avoiding the ad hoc selection of channels and/or time windows. FDR methods are a good alternative to the common and computationally more expensive permutation test. The BH method for independent tests gave the best overall performance regarding the balance between type I and type II errors. The local-fdr method is preferable for high dimensional (multichannel) problems where most of the tests conform to the empirical null hypothesis. Differences among the methods according to assumptions, null distributions and dimensionality of the problem are also discussed. Copyright © 2009 John Wiley & Sons, Ltd.

Friese U.,University of Hamburg | Friese U.,University of Osnabrück | Koster M.,University of Osnabrück | Hassler U.,University of Osnabrück | And 3 more authors.
NeuroImage | Year: 2013

Although previous studies have established that successful memory encoding is associated with increased synchronization of theta-band and gamma-band oscillations, it is unclear if there is a functional relationship between oscillations in these frequency bands. Using scalp-recorded EEG in healthy human participants, we demonstrate that cross-frequency coupling between frontal theta phase and posterior gamma power is enhanced during the encoding of visual stimuli which participants later on remember versus items which participants subsequently forget ("subsequent memory effect," SME). Conventional wavelet analyses and source localizations revealed SMEs in spectral power of theta-, alpha-, and gamma-band. Successful compared to unsuccessful encoding was reflected in increased theta-band activity in right frontal cortex as well as increased gamma-band activity in parietal-occipital regions. Moreover, decreased alpha-band activity in prefrontal and occipital cortex was also related to successful encoding. Overall, these findings support the idea that during the formation of new memories frontal cortex regions interact with cortical representations in posterior areas. © 2012 Elsevier Inc..

Garcia-Penton L.,Basque Center on Cognition | Perez Fernandez A.,Basque Center on Cognition | Iturria-Medina Y.,Cuban Neuroscience Center | Gillon-Dowens M.,The Interdisciplinary Center | And 2 more authors.
NeuroImage | Year: 2014

How the brain deals with more than one language and whether we need different or extra brain language sub-networks to support more than one language remain unanswered questions. Here, we investigate structural brain network differences between early bilinguals and monolinguals. Using diffusion-weighted MRI (DW-MRI) tractography techniques and a network-based statistic (NBS) procedure, we found two structural sub-networks more connected by white matter (WM) tracts in bilinguals than in monolinguals; confirming WM brain plasticity in bilinguals. One of these sub-networks comprises left frontal and parietal/temporal regions, while the other comprises left occipital and parietal/temporal regions and also the right superior frontal gyrus. Most of these regions have been related to language processing and monitoring; suggesting that bilinguals develop specialized language sub-networks to deal with the two languages. Additionally, a complex network analysis showed that these sub-networks are more graph-efficient in bilinguals than monolinguals and this increase seems to be at the expense of a whole-network graph-efficiency decrease. © 2013 Elsevier Inc.

Moutoussis M.,University College London | Trujillo-Barreto N.J.,Cuban Neuroscience Center | El-Deredy W.,University of Manchester | Dolan R.J.,University College London | Friston K.J.,University College London
Frontiers in Human Neuroscience | Year: 2014

Introduction: We propose that active Bayesian inference-a general framework for decision-making-can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. Results: (1) Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to "mentalizing" in the psychological literature, is based upon the outcomes of interpersonal exchanges. (2) We show how some well-known social-psychological phenomena (e.g., self-serving biases) can be explained in terms of active interpersonal inference. (3) Mentalizing naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one's own qualities and preferences. Conclusion: We inaugurate a Bayes optimal framework for modeling intersubject variability in mentalizing during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalizing is distorted. © 2014 Moutoussis, Trujillo-Barreto, El-Deredy, Dolan and Friston.

Olier I.,University of Manchester | Trujillo-Barreto N.J.,Cuban Neuroscience Center | El-Deredy W.,University of Manchester
NeuroImage | Year: 2013

We introduce a new generative model of the Encephalography (EEG/MEG) data, the inversion of which allows for inferring the locations and temporal evolution of the underlying sources as well as their dynamical interactions. The proposed Switching Mesostate Space Model (SMSM) builds on the multi-scale generative model for EEG/MEG by Daunizeau and Friston (2007). SMSM inherits the assumptions that (1) bioelectromagnetic activity is generated by a set of distributed sources, (2) the dynamics of these sources can be modelled as random fluctuations about a small number of mesostates, and (3) the number of mesostates engaged by a cognitive task is small. Additionally, four generalising assumptions are now included: (4) the mesostates interact according to a full Dynamical Causal Network (DCN) that can be estimated; (5) the dynamics of the mesostates can switch between multiple approximately linear operating regimes; (6) each operating regime remains stable over finite periods of time (temporal clusters); and (7) the total number of times the mesostates' dynamics can switch is small. The proposed model adds, therefore, a level of flexibility by accommodating complex brain processes that cannot be characterised by purely linear and stationary Gaussian dynamics. Importantly, the SMSM furnishes a new interpretation of the EEG/MEG data in which the source activity may have multiple discrete modes of behaviour, each with approximately linear dynamics. This is modelled by assuming that the connection strengths of the underlying mesoscopic DCN are time-dependent but piecewise constant, i.e. they can undergo discrete changes over time. A Variational Bayes inversion scheme is derived to estimate all the parameters of the model by maximising a (Negative Free Energy) lower bound on the model evidence. This bound is used to select among different model choices that are defined by the number of mesostates as well as by the number of stationary linear regimes. The full model is compared to a simplified version that uses no dynamical assumptions as well as to a standard EEG inversion technique. The comparison is carried out using an extensive set of simulations, and the application of SMSM to a real data set is also demonstrated. Our results show that for experimental situations in which we have some a priori belief that there are multiple approximately linear dynamical regimes, the proposed SMSM provides a natural modelling tool. © 2013 Elsevier Inc.

Koenig T.,University of Bern | Melie-Garcia L.,Cuban Neuroscience Center
Brain Topography | Year: 2010

We present a simple and effective method to test whether an event consistently activates a set of brain electric sources across repeated measurements of event-related scalp field data. These repeated measurements can be single trials, single subject ERPs, or ERPs from different studies. The method considers all sensors simultaneously, but can be applied separately to each time frame or frequency band of the data. This allows limiting the analysis to time periods and frequency bands where there is positive evidence of a consistent relation between the event and some brain electric sources. The test may therefore avoid false conclusions about the data resulting from an inadequate selection of the analysis window and bandpass filter, and permit the exploration of alternate hypotheses when group/condition differences are observed in evoked field data. The test will be called topographic consistency test (TCT). The statistical inference is based on simple randomization techniques. Apart form the methodological introduction, the paper contains a series of simulations testing the statistical power of the method as function of number of sensors and observations, a sample analysis of EEG potentials related to self-initiated finger movements, and Matlab source code to facilitate the implementation. Furthermore a series of measures to control for multiple testing are introduced and applied to the sample data. © 2010 Springer Science+Business Media, LLC.

Saupe K.,University of Leipzig | Widmann A.,University of Leipzig | Trujillo-Barreto N.J.,Cuban Neuroscience Center | Schroger E.,University of Leipzig
International Journal of Psychophysiology | Year: 2013

The auditory processing of self-generated sounds is characterized by an attenuated vertex N1-component of the event-related potential (ERP) compared to the responses elicited by externally generated sounds. Typically, a motor condition where sounds are actively produced by button presses is compared with a passive listening condition. While this effect is usually interpreted as reflection of an internal forward model system, the impact of attention and arousal on the so called self-generation effect has not been systematically controlled in these studies: Is the auditory stimulation more attended during the active task compared to passive listening, e.g., caused by a higher arousal level? Or is it rather attended less and attention is drawn away from the task-irrelevant stimulation to the motor task? Accordingly, the self-generation effects reported in the literature can easily be over- or underestimated. In the present study we disentangled attention from the self-generation effect by introducing an active listening condition, in which attention is focused to the same feature as in the self-generation condition - the stimulus onset-to-onset interval. We observed a classical 'self-generation effect', i.e. attenuated amplitudes for self-generated compared to passive listened sounds at frontocentral electrodes. As expected this effect was overlapped by attention effects in space and time. However, topographical and tomographical analyses allowed us to clearly disentangle both effects. Our results argue for the existence of a genuine self-generation effect, but emphasize the problem of possible over- or underestimation caused by attentional confounds. © 2013 Elsevier B.V.

Sanabria-Diaz G.,Cuban Neuroscience Center | Martinez-Montes E.,Cuban Neuroscience Center | Melie-Garcia L.,Cuban Neuroscience Center
PLoS ONE | Year: 2013

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network's attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI. © 2013 Sanabria-Diaz et al.

Valdes-Sosa P.A.,Cuban Neuroscience Center
Malaysian Journal of Medical Sciences | Year: 2012

Brain disorders account for more than 34% of the global burden of disease, crippling nations by decreasing their "mental capital"-with greater effect in developing countries. Early detection is the key to their management, but establishing such programmes seems nearly impossible due to the high prevalence of the dysfunctions as compared with the high cost of neuroimaging devices. Thus, at frst sight, the research of the Decade of the Brain and the international Human Brain Mapping Project might seem to be condemned to beneft only a small elite. Cuba has shown that is not so by using neurotechnology for the last 3 decades to implement stratifed active screening programmes for brain disorders at the population level. This experience has shown that, by the transformation of health indicators, an appropriate use of technology can be integrated with attention to the population at the primary levels of both health care and education. An essential component of neurotechnology is neuroinformatics, which-like its counterpart bioinformatics-combines databases, analysis tools, and theoretical models to craft tools for early disease diagnosis and management. Much work remains to be done and will depend critically on south-south cooperation to solve problems for countries with similar situations. © Penerbit Universiti Sains Malaysia.

Scharinger M.,Max Planck Institute for Human Cognitive and Brain Sciences | Bendixen A.,University of Leipzig | Trujillo-Barreto N.J.,Cuban Neuroscience Center | Obleser J.,Max Planck Institute for Human Cognitive and Brain Sciences
PLoS ONE | Year: 2012

The precise neural mechanisms underlying speech sound representations are still a matter of debate. Proponents of 'sparse representations' assume that on the level of speech sounds, only contrastive or otherwise not predictable information is stored in long-term memory. Here, in a passive oddball paradigm, we challenge the neural foundations of such a 'sparse' representation; we use words that differ only in their penultimate consonant ("coronal" [t] vs. "dorsal" [k] place of articulation) and for example distinguish between the German nouns Latz ([lats]; bib) and Lachs ([laks]; salmon). Changes from standard [t] to deviant [k] and vice versa elicited a discernible Mismatch Negativity (MMN) response. Crucially, however, the MMN for the deviant [lats] was stronger than the MMN for the deviant [laks]. Source localization showed this difference to be due to enhanced brain activity in right superior temporal cortex. These findings reflect a difference in phonological 'sparsity': Coronal [t] segments, but not dorsal [k] segments, are based on more sparse representations and elicit less specific neural predictions; sensory deviations from this prediction are more readily 'tolerated' and accordingly trigger weaker MMNs. The results foster the neurocomputational reality of 'representationally sparse' models of speech perception that are compatible with more general predictive mechanisms in auditory perception. © 2012 Scharinger et al.

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