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

Botão de ajuda para idosos através da plataforma Arduino

Se reporta el diseño e implementación de un sistema capaz de proveer a personas de la tercera edad de una vía rápida para pedir ayuda, de manera remota, al encargado de su cuidado, usando la red celular. Se implementó un sistema, cuya principal funcionalidad es un botón de ayuda, mediante el cual la persona encargada del cuidado del anciano recibirá un mensaje de texto cuando el anciano presione el botón. Reconociendo la poca vinculación de las personas de este grupo etario con la tecnología celular, definir el número al que se enviarán los mensajes (y escribirlos) es transparente para ellos. El sistema cuenta con otras prestaciones de gran valía, un grupo de alarmas automáticas, también vía SMS, definidas para un grupo de sensores incluidos en el sistema, que facilita la toma de decisiones de forma remota a través de un SMS.The design and implementation of a system capable of providing elderly people with a fast track to ask for help, remotely, to the person in charge of their care, using the cellular network is reported t. The main functionality is a help button, through which the person in charge of the elderly care will receive a text message when the elder presses the button. Recognizing the little connection between the people of this age group with cellular technology, defining the number to which the messages will be sent (and writing them) is not their task. The system has other features of great value, as a group of automatic alarms, also operating via SMS, generated by a group of sensors included in the system, which facilitates remote decision making from caregivers.É relatado o desenho e a implementação de um sistema capaz de fornecer aos idosos uma via rápida para pedir ajuda, remotamente, à pessoa responsável pelos cuidados, usando a rede celular. Foi implementado um sistema, cuja principal funcionalidade é um botão de ajuda, através do qual a pessoa encarregada de cuidar dos idosos receberá uma mensagem de texto quando o idoso pressionar o botão. Reconhecendo a pouca conexão das pessoas desta faixa etária com a tecnologia celular, definir o número para o qual as mensagens serão enviadas (e a sua redação) é transparente para eles. O sistema possui outros recursos de grande valor, um grupo de alarmes automáticos, também via SMS, definidos para um grupo de sensores incluídos no sistema, o que facilita a tomada de decisões de forma remota através de um SMS

Botão de ajuda para idosos através da plataforma Arduino

The design and implementation of a system capable of providing elderly people with a fast track to ask for help, remotely, to the person in charge of their care, using the cellular network is reported t. The main functionality is a help button, through which the person in charge of the elderly care will receive a text message when the elder presses the button. Recognizing the little connection between the people of this age group with cellular technology, defining the number to which the messages will be sent (and writing them) is not their task. The system has other features of great value, as a group of automatic alarms, also operating via SMS, generated by a group of sensors included in the system, which facilitates remote decision making from caregivers.Se reporta el diseño e implementación de un sistema capaz de proveer a personas de la tercera edad de una vía rápida para pedir ayuda, de manera remota, al encargado de su cuidado, usando la red celular. Se implementó un sistema, cuya principal funcionalidad es un botón de ayuda, mediante el cual la persona encargada del cuidado del anciano recibirá un mensaje de texto cuando el anciano presione el botón. Reconociendo la poca vinculación de las personas de este grupo etario con la tecnología celular, definir el número al que se enviarán los mensajes (y escribirlos) es transparente para ellos. El sistema cuenta con otras prestaciones de gran valía, un grupo de alarmas automáticas, también vía SMS, definidas para un grupo de sensores incluidos en el sistema, que facilita la toma de decisiones de forma remota a través de un SMS.É relatado o desenho e a implementação de um sistema capaz de fornecer aos idosos uma via rápida para pedir ajuda, remotamente, à pessoa responsável pelos cuidados, usando a rede celular. Foi implementado um sistema, cuja principal funcionalidade é um botão de ajuda, através do qual a pessoa encarregada de cuidar dos idosos receberá uma mensagem de texto quando o idoso pressionar o botão. Reconhecendo a pouca conexão das pessoas desta faixa etária com a tecnologia celular, definir o número para o qual as mensagens serão enviadas (e a sua redação) é transparente para eles. O sistema possui outros recursos de grande valor, um grupo de alarmes automáticos, também via SMS, definidos para um grupo de sensores incluídos no sistema, o que facilita a tomada de decisões de forma remota através de um SMS

Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning

Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10–20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox

Accurate and Efficient Simulation of Very High-Dimensional Neural Mass Models with Distributed-Delay Connectome Tensors

This paper introduces methods and a novel toolbox that efficiently integrates high-dimensional Neural Mass Models (NMMs) specified by two essential components. The first is the set of nonlinear Random Differential Equations (RDEs) of the dynamics of each neural mass. The second is the highly sparse three-dimensional Connectome Tensor (CT) that encodes the strength of the connections and the delays of information transfer along the axons of each connection. To date, simplistic assumptions prevail about delays in the CT, often assumed to be Dirac-delta functions. In reality, delays are distributed due to heterogeneous conduction velocities of the axons connecting neural masses. These distributed-delay CTs are challenging to model. Our approach implements these models by leveraging several innovations. Semi-analytical integration of RDEs is done with the Local Linearization (LL) scheme for each neural mass, ensuring dynamical fidelity to the original continuous-time nonlinear dynamic. This semi-analytic LL integration is highly computationally-efficient. In addition, a tensor representation of the CT facilitates parallel computation. It also seamlessly allows modeling distributed delays CT with any level of complexity or realism. This ease of implementation includes models with distributed-delay CTs. Consequently, our algorithm scales linearly with the number of neural masses and the number of equations they are represented with, contrasting with more traditional methods that scale quadratically at best. To illustrate the toolbox's usefulness, we simulate a single Zetterberg-Jansen and Rit (ZJR) cortical column, a single thalmo-cortical unit, and a toy example comprising 1000 interconnected ZJR columns. These simulations demonstrate the consequences of modifying the CT, especially by introducing distributed delays. The examples illustrate the complexity of explaining EEG oscillations, e.g., split alpha peaks, since they only appear for distinct neural masses. We provide an open-source Script for the toolbox

Harmonized-Multinational qEEG norms (HarMNqEEG)

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings

Braincharts for the human lifespan

Over the past 25 years, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, there are no reference standards against which to anchor measures of individual differences in brain morphology, in contrast to growth charts for traits such as height and weight. Here, we built an interactive online resource (www.brainchart.io) to quantify individual differences in brain structure from any current or future magnetic resonance imaging (MRI) study, against models of expected age-related trends. With the goal of basing these on the largest and most inclusive dataset, we aggregated MRI data spanning 115 days post-conception through 100 postnatal years, totaling 122,123 scans from 100,071 individuals in over 100 studies across 6 continents. When quantified as centile scores relative to the reference models, individual differences show high validity with non-MRI brain growth estimates and high stability across longitudinal assessment. Centile scores helped identify previously unreported brain developmental milestones and demonstrated increased genetic heritability compared to non-centiled MRI phenotypes. Crucially for the study of brain disorders, centile scores provide a standardised and interpretable measure of deviation that reveals new patterns of neuroanatomical differences across neurological and psychiatric disorders emerging during development and ageing. In sum, brain charts for the human lifespan are an essential first step towards robust, standardised quantification of individual variation and for characterizing deviation from age-related trends. Our global collaborative study