56 research outputs found
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
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
Country-level gender inequality is associated with structural differences in the brains of women and men
男女間の不平等と脳の性差 --男女間の不平等は脳構造の性差と関連する--. 京都大学プレスリリース. 2023-05-10.Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women’s worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7, 876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women’s brains and provide initial evidence for neuroscience-informed policies for gender equality
Replication Data for: Electroencephalographic Characterization of Subgroups of Children with Learning Disorders
Accompany data of PONE-D-17-03924R
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