Isolated effective coherence (iCoh): causal information flow excluding indirect paths

A problem of great interest in real world systems, where multiple time series measurements are available, is the estimation of the intra-system causal relations. For instance, electric cortical signals are used for studying functional connectivity between brain areas, their directionality, the direct or indirect nature of the connections, and the spectral characteristics (e.g. which oscillations are preferentially transmitted). The earliest spectral measure of causality was Akaike's (1968) seminal work on the noise contribution ratio, reflecting direct and indirect connections. Later, a major breakthrough was the partial directed coherence of Baccala and Sameshima (2001) for direct connections. The simple aim of this study consists of two parts: (1) To expose a major problem with the partial directed coherence, where it is shown that it is affected by irrelevant connections to such an extent that it can misrepresent the frequency response, thus defeating the main purpose for which the measure was developed, and (2) To provide a solution to this problem, namely the "isolated effective coherence", which consists of estimating the partial coherence under a multivariate auto-regressive model, followed by setting all irrelevant associations to zero, other than the particular directional association of interest. Simple, realistic, toy examples illustrate the severity of the problem with the partial directed coherence, and the solution achieved by the isolated effective coherence. For the sake of reproducible research, the software code implementing the methods discussed here (using lazarus free-pascal "www.lazarus.freepascal.org"), including the test data as text files, are freely available at: https://sites.google.com/site/pascualmarqui/home/icoh-isolated-effective-coherenceComment: 2014-02-21 pre-print, technical report, KEY Institute for Brain-Mind Research, University of Zurich, et a

Neural Connectivity with Hidden Gaussian Graphical State-Model

The noninvasive procedures for neural connectivity are under questioning. Theoretical models sustain that the electromagnetic field registered at external sensors is elicited by currents at neural space. Nevertheless, what we observe at the sensor space is a superposition of projected fields, from the whole gray-matter. This is the reason for a major pitfall of noninvasive Electrophysiology methods: distorted reconstruction of neural activity and its connectivity or leakage. It has been proven that current methods produce incorrect connectomes. Somewhat related to the incorrect connectivity modelling, they disregard either Systems Theory and Bayesian Information Theory. We introduce a new formalism that attains for it, Hidden Gaussian Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey. Open source packages are freely available online, to reproduce the results presented in this paper and to analyze external MEEG databases

Innovations orthogonalization: a solution to the major pitfalls of EEG/MEG "leakage correction"

The problem of interest here is the study of brain functional and effective connectivity based on non-invasive EEG-MEG inverse solution time series. These signals generally have low spatial resolution, such that an estimated signal at any one site is an instantaneous linear mixture of the true, actual, unobserved signals across all cortical sites. False connectivity can result from analysis of these low-resolution signals. Recent efforts toward "unmixing" have been developed, under the name of "leakage correction". One recent noteworthy approach is that by Colclough et al (2015 NeuroImage, 117:439-448), which forces the inverse solution signals to have zero cross-correlation at lag zero. One goal is to show that Colclough's method produces false human connectomes under very broad conditions. The second major goal is to develop a new solution, that appropriately "unmixes" the inverse solution signals, based on innovations orthogonalization. The new method first fits a multivariate autoregression to the inverse solution signals, giving the mixed innovations. Second, the mixed innovations are orthogonalized. Third, the mixed and orthogonalized innovations allow the estimation of the "unmixing" matrix, which is then finally used to "unmix" the inverse solution signals. It is shown that under very broad conditions, the new method produces proper human connectomes, even when the signals are not generated by an autoregressive model.Comment: preprint, technical report, under license "Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)", https://creativecommons.org/licenses/by-nc-nd/4.0

Spectral homogeneity cross frequencies can be a quality metric for the large-scale resting EEG preprocessing

The brain projects require the collection of massive electrophysiological data, aiming to the longitudinal, sectional, or populational neuroscience studies. Quality metrics automatically label the data after centralized preprocessing. However, although the waveforms-based metrics are partially useful, they may be unreliable by neglecting the spectral profiles. Here, we detected the phenomenon of parallel log spectra (PaLOS) that the scalp EEG power in the log scale were parallel to each other from 10% of 2549 HBN EEG. This phenomenon was reproduced in 8% of 412 PMDT EEG from 4 databases. We designed the PaLOS index (PaLOSi) to indicate this phenomenon by decomposing the cross-spectra at different frequencies into the common principal component spaces. We found that the PaLOS biophysically implied a prominently dominant dipole in the source space which was implausible for the resting EEG. And it may be practically resulted from excessive preprocessing. Compared with the 1966 normative EEG cross-spectra, the HBN and the PMDT EEG with PaLOS presented generally much higher electrode pairwise coherences and higher similarity of coherence-based network patterns, which went against the known frequency dependent characteristic of coherence networks. We suggest the PaLOSi should lay in the range of 0.4-0.7 for large resting EEG quality assurance

Spectral homogeneity cross frequencies can be a quality metric for the large-scale resting EEG preprocessing

The brain projects require the collection of massive electrophysiological data, aiming to the longitudinal, sectional, or populational neuroscience studies. Quality metrics automatically label the data after centralized preprocessing. However, although the waveforms-based metrics are partially useful, they may be unreliable by neglecting the spectral profiles. Here, we detected the phenomenon of parallel log spectra (PaLOS) that the scalp EEG power in the log scale were parallel to each other from 10% of 2549 HBN EEG. This phenomenon was reproduced in 8% of 412 PMDT EEG from 4 databases. We designed the PaLOS index (PaLOSi) to indicate this phenomenon by decomposing the cross-spectra at different frequencies into the common principal component spaces. We found that the PaLOS biophysically implied a prominently dominant dipole in the source space which was implausible for the resting EEG. And it may be practically resulted from excessive preprocessing. Compared with the 1966 normative EEG cross-spectra, the HBN and the PMDT EEG with PaLOS presented generally much higher electrode pairwise coherences and higher similarity of coherence-based network patterns, which went against the known frequency dependent characteristic of coherence networks. We suggest the PaLOSi should lay in the range of 0.4-0.7 for large resting EEG quality assurance

Overcome the stigma to dementia, a challenge to the Cuban society

Introduction: Worldwide Alzheimer Disease Organization in the year 2012 carried out a campaign to overcome the stigma to dementia. The stigma constitutes the biggest obstacle to identify health's problems of, to find its solutions and to use in a more efficient way the health's services. Objective: To show it repercussion of the stigma to the patient with dementia, the family and the society. Material and Methods: It was carried out a search of the published literature in the period comprised from July 2010 until March 2016. The information was obtained through Infomed information network, we used the following databases, Pubmed, Ebsco, Medline, Sciencedirect, Clinicalkey and Scielo. The data obtained was analyzed and commented. Development: The stigma interferes in that people with dementia have a successful social life, obtain employments and to have the possibility to live near other people. It has a big relationship with the age and the loss of the mental functions. The areas of bigger impact of the stigma are: the labor sphere, vehicles driving, the possibility to give consent for the medical procedure that have influence in a premature loss of dignity and autonomy. Conclusions: To overcome the stigma that today exists to dementia it is necessary to offer a bigger acceptance and support to the patients and family, to increase care's quality for people with cognitive deterioration, to educate the population, to create a favorable environment, to create social network of support and to call the for participation of all the sectors of the society. Keywords: social stigma, dementia, social discrimination, health plan, family, society, Cuba.</span

Predicting aging-related decline in physical performance with sparse electrophysiological source imaging

Objective: We introduce a methodology for selecting biomarkers from activation and connectivity derived from Electrophysiological Source Imaging (ESI). Specifically, we pursue the selection of stable biomarkers associated with cognitive decline based on source activation and connectivity patterns of resting-state EEG theta rhythm, used as predictors of physical performance decline in aging individuals measured by a Gait Speed (GS) slowing. Methods: Our two-step methodology involves estimating ESI using flexible sparse-smooth-nonnegative models, from which activation ESI (aESI) and connectivity ESI (cESI) features are derived. The Stable Sparse Classifier method then selects potential biomarkers related to GS changes. Results and Conclusions: Our predictive models using aESI outperform traditional methods such as the LORETA family. The models combining aESI and cESI features provide the best prediction of GS changes. Potential biomarkers from activation/connectivity patterns involve orbitofrontal and temporal cortical regions. Significance: The proposed methodology contributes to the understanding of activation and connectivity of GS-related ESI and provides features that are potential biomarkers of GS slowing. Given the known relationship between GS decline and cognitive impairment, this preliminary work opens novel paths to predict the progression of healthy and pathological aging and might allow an ESI-based evaluation of rehabilitation programs

Hierarchical Event Descriptor library schema for EEG data annotation

Standardizing terminology to describe electrophysiological events can improve both clinical care and computational research. Sharing data enriched by such standardized terminology can support advances in neuroscientific data exploration, from single-subject to mega-analysis. Machine readability of electrophysiological event annotations is essential for performing such analyses efficiently across software tools and packages. Hierarchical Event Descriptors (HED) provide a framework for describing events in neuroscience experiments. HED library schemas extend the standard HED schema vocabulary to include specialized vocabularies, such as standardized clinical terms for electrophysiological events. The Standardized Computer-based Organized Reporting of EEG (SCORE) defines terms for annotating EEG events, including artifacts. This study makes SCORE machine-readable by incorporating it into a HED library schema. We demonstrate the use of the HED-SCORE library schema to annotate events in example EEG data stored in Brain Imaging Data Structure (BIDS) format. Clinicians and researchers worldwide can now use the HED-SCORE library schema to annotate and then compute on electrophysiological data obtained from the human brain.Comment: 22 pages, 5 figure

Unintentional interpersonal synchronization represented as a reciprocal visuo-postural feedback system

People's behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction-two individuals influencing one another-or in one direction-one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another's head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR), the measure of influenc