Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB

This tutorial provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). This involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. We walk through this approach in detail, using data from a published fMRI experiment that characterised individual differences in hemispheric lateralization in a semantic processing task. The preliminary subject specific DCM analysis is covered in detail in a companion paper. This tutorial is accompanied by the example dataset and step-by-step instructions to reproduce the analyses

Subcutaneous Connective Tissue Reaction to a New Nano Zinc-Oxide Eugenol Sealer in Rat Model

Introduction: The aim of this animal study was to evaluate the histological response of the new nano zinc-oxide eugenol (NZOE) sealer in comparison with Pulp Canal Sealer (ZOE based) and AH-26 (epoxy resin sealer). Methods and Materials: A total of 27 Wistar rats were used. Four polyethylene tubes were implanted in the back of each rat (three tubes containing the test materials and an empty tube as a control). Then, 9 animals were sacrificed at each interval of 15, 30 and 60 days, and the implants were removed with the surrounding tissues.Samples were evaluated for the presence of inflammatory cell (mononuclear cell), vascular changes, fibrous tissue formation and present of giant cell. Comparisons between groups and time-periods were performed using the Kruskal-Wallis and Mann-Whitney U non-parametric tests. The level of significance was set at 0.05. Results: No significant difference was observed in tissue reactions and biocompatibility pattern of three sealers during 3 experimental periods (P<0.05). In all groups the tissue behavior showed tendency to decrease the irritation effect over time. Conclusion: The new nano zinc-oxide eugenol sealer has histocompatibility properties comparable to conventional commercial sealers.Keywords: Biocompatibility; Nanoparticle; Tissue Reaction; Zinc-Oxide Eugeno

Neurovascular coupling: insights from multi-modal dynamic causal modelling of fMRI and MEG

This technical note presents a framework for investigating the underlying mechanisms of neurovascular coupling in the human brain using multi-modal magnetoencephalography (MEG) and functional magnetic resonance (fMRI) neuroimaging data. This amounts to estimating the evidence for several biologically informed models of neurovascular coupling using variational Bayesian methods and selecting the most plausible explanation using Bayesian model comparison. First, fMRI data is used to localise active neuronal sources. The coordinates of neuronal sources are then used as priors in the specification of a DCM for MEG, in order to estimate the underlying generators of the electrophysiological responses. The ensuing estimates of neuronal parameters are used to generate neuronal drive functions, which model the pre or post synaptic responses to each experimental condition in the fMRI paradigm. These functions form the input to a model of neurovascular coupling, the parameters of which are estimated from the fMRI data. This establishes a Bayesian fusion technique that characterises the BOLD response - asking, for example, whether instantaneous or delayed pre or post synaptic signals mediate haemodynamic responses. Bayesian model comparison is used to identify the most plausible hypotheses about the causes of the multimodal data. We illustrate this procedure by comparing a set of models of a single-subject auditory fMRI and MEG dataset. Our exemplar analysis suggests that the origin of the BOLD signal is mediated instantaneously by intrinsic neuronal dynamics and that neurovascular coupling mechanisms are region-specific. The code and example dataset associated with this technical note are available through the statistical parametric mapping (SPM) software package

Prognosis of Mechanical Ventilation in Very Low Birth Weight Neonates: A Single-Center Study in Tehran.

Background and Aims: Approximately 4–7 percent of all live births are led to a very low birth weight (VLBW) situation where the morbidity and mortality rate are very high. A large number of VLBW newborns in intensive care unit (ICU) require mechanical ventilation due to various conditions. To reduce mortality in this group, identification of risk factors is important. This study aimed to determine the prognosis of mechanical ventilation in VLBW neonates at Mahdiye hospital in Tehran. Materials and Methods: This study is a prospective cohort study. VLBW neonates who consecutively were put on mechanical ventilation during the study period were enrolled.  Then, the enrolled neonates were divided into two groups: neonates who died after implementing the ventilator were in group-I and neonates who survived after receiving mechanical ventilation were in group-II. Demographic, clinical and paraclinical variables were gathered to find out the predictors of mortality of ventilated neonates. The data were analyzed by SPSS software version 21. Results: During the study period, a total of 177 neonates were ventilated due to different causes. 56% were male with a male to female ratio 1.27:1. Mean birth weight and gestational age were 1024.8 ± 247.5 grams and 27.9±2.2 weeks respectively. Out of 177 mechanically ventilated VLBW neonates enrolled for this study, 53% died. Significant factors determining mortality rate were mean weight, mean gestational age, pulmonary hemorrhage, advance resuscitation and duration of hospital stay (p<0.05). APGAR score, gender, Pneumothorax, IVH>II, Sepsis and Maternal Disease were not significantly associated with mortality in VLBW neonates requiring mechanical ventilation (P>0.05). Conclusion: This study showed that among the analyzed factors weight <1000gm, gestation <28weeks, pulmonary hemorrhage and complications during ventilation were the most significant predictors of mortality in ventilated VLBW neonates in the intensive care unit

A tutorial on group effective connectivity analysis, part 1: first level analysis with DCM for fMRI

Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this tutorial, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the first part of the tutorial (current paper), we focus on issues specific to DCM for fMRI, unpacking the relevant theory and highlighting practical considerations. In particular, we clarify the assumptions (i.e., priors) used in DCM for fMRI and how to interpret the model parameters. This tutorial is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality

Neurophysiological consequences of synapse loss in progressive supranuclear palsy

Synaptic loss occurs early in many neurodegenerative diseases and contributes to cognitive impairment even in the absence of gross atrophy. Currently, for human disease there are few formal models to explain how cortical networks underlying cognition are affected by synaptic loss. We advocate that biophysical models of neurophysiology offer both a bridge from clinical to preclinical models of pathology, and quantitative assays for experimental medicine. Such biophysical models can also disclose hidden neuronal dynamics generating neurophysiological observations like electro- and magneto-encephalography. Here, we augment a biophysically informed mesoscale model of human cortical function by inclusion of synaptic density estimates as captured by [11C]UCB-J positron emission tomography, and provide insights into how regional synapse loss affects neurophysiology. We use the primary tauopathy of progressive supranuclear palsy (Richardson's syndrome) as an exemplar condition, with high clinicopathological correlations. Progressive supranuclear palsy causes a marked change in cortical neurophysiology in the presence of mild cortical atrophy and is associated with a decline in cognitive functions associated with the frontal lobe. Using parametric empirical Bayesian inversion of a conductance-based canonical microcircuit model of magnetoencephalography data, we show that the inclusion of regional synaptic density-as a subject-specific prior on laminar specific neuronal populations-markedly increases model evidence. Specifically, model comparison suggests that a reduction in synaptic density in inferior frontal cortex affects superficial and granular layer glutamatergic excitation. This predicted individual differences in behaviour, demonstrating the link between synaptic loss, neurophysiology, and cognitive deficits. The method we demonstrate is not restricted to progressive supranuclear palsy or the effects of synaptic loss: such pathology-enriched dynamic causal models can be used to assess the mechanisms of other neurological disorders, with diverse non-invasive measures of pathology, and is suitable to test the effects of experimental pharmacology

Patient-specific neural mass modelling of focal seizures

© 2016 Amirhossein JafarianPatient-specific computational modelling of epileptic seizures may make the models more useful for diagnosis, management and treatment of epilepsy. In this thesis, patient-specific models from electroencephalogram data from patients are presented. The models can potentially be used as a starting point in the design of a seizure control system based on electrical stimulation. In this thesis, generalised versions of the Jansen and Rit neural mass model to emulate underlying generators of seizures are presented. This model is generalised to slow-fast neural mass models to replicate deterministic biological mechanisms of seizure initiation and termination. To resemble stochastic mechanisms of seizure initiation, a Duffing neural mass model is developed by introducing perturbations to the linear time invariant dynamics model of the synapse of a typical neural population in the Jansen and Rit model. Parameter identification of a slow-fast neural mass model is explored using a genetic algorithm and unscented Kalman filter. Using the genetic algorithm, we show that parameter estimation can lead to quantitative reproduction of the pattern of seizure initiation in EEG data. The unscented Kalman filter is employed to enhance the parameter estimation of the slow-fast neural mass model using genetic algorithm-based identification. Identification of the Duffing neural mass model of seizures is performed by simultaneous usage of the unscented Kalman filter and genetic algorithm to optimise the likelihood function with respect to the parameters to be estimated. The results shows that these methods are useful for parameter identification of neural mass models of focal seizures. Patient-specific modelling of an animal model of focal seizures is explored by fitting the slow-fast and Duffing neural mass models to animal EEG data. The unscented Kalman filter is employed to infer hidden dynamics of neural populations for EEG data. For a given EEG data, a model likelihood function is employed for model selection

A novel method for multichannel spectral factorization

A novel algorithm for the spectral factorization (SF) of a para-Hermitian polynomial matrix (PPM) is presented. This utilizes a series of paraunitary transformations to reconstruct the spectral factors of the PPM from the spectral factors of its eigenvalue polynomial matrix (EPM). The EPM is a diagonal polynomial matrix (DPM) obtained by applying a set of simple shift and rotation operators to the PPM using the second order best rotation (SBR2) algorithm. The spectral factors of the EPM are calculated by factorizing each of its scalar elements independently. In this paper, it is shown how to generate a series of paraunitary matrices which then map the factors of the EPM to the required spectral factors of the original PPM. The method basically introduces a sequence of stable operators which provide a direct connection between the one-dimensional spectral factorization problem and the multidimensional case