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Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. A major challenge in tuning these models is the large number of parameters involved. Traditional methods, such as one-by-one sensitivity analyses, are time-consuming, subjective, and often return only a single set of parameter values, focusing on reproducing averaged data rather than capturing the full variability of experimental measurements. In this study, we applied simulation-based inference (SBI) using neural posterior estimation (NPE) to tune an FE model of the human middle ear. The training dataset consisted of 10,000 FE simulations of stapes velocity, ear-canal (EC) input impedance, and absorbance, paired with seven FE parameter values randomly sampled within plausible ranges. The neural network learned the association between parameters and simulation outcomes, returning the probability distribution of parameter values that can reproduce experimental data. Our approach successfully identified parameter sets that reproduced three experimental datasets simultaneously. By accounting for experimental noise and variability during training, the method provided a probability distribution of parameters, representing all valid combinations that could fit the data, rather than tuning to averaged values. The network demonstrated robustness to noise and exhibited an efficient learning curve due to the large training dataset. SBI offers an objective alternative to laborious sensitivity analyses, providing probability distributions for each parameter and uncovering interactions between them. This method can be applied to any biological FE model, and we demonstrated its effectiveness using a middle-ear model. Importantly, it holds promise for objective differential diagnosis of conductive hearing loss by providing insight into the mechanical properties of the middle ear.

Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M  = 73.8 years; SD  = 9.0). Of these, 68 patients ( M  = 73.9 years; SD  = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p  < .001; β = .274; P  = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P  = .035) those with greater functional coupling in interhemispheric connections declined more (β = -.402; P  = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior-posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis . We present a machine learning method that can identify a range of network models that generate activity patterns matching experimental data and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-trained using model simulations-to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics., Competing Interests: PG, JL, MD, MN, KÖ, GB, CC, WP, SH, TV, DG, JM No competing interests declared, (© 2020, Gonçalves et al.)

In this paper we present a new estimation procedure named MF-IVL for VAR systems in the case of mixed-frequency data, where the data maybe, e.g., stock or flow data. The main idea of this new procedure is to project the slow components on the present and past fast ones in order to create instrumental variables. This procedure is shown to be generically consistent. Our claim is that the procedure is fast and more accurate when compared to the extended Yule-Walker procedure. A comparison of these two procedures is given by simulation., (© The Author(s) 2018.)

This article studies the sensitivity of Granger causality to the addition of noise, the introduction of subsampling, and the application of causal invertible filters to weakly stationary processes. Using canonical spectral factors and Wold decompositions, we give general conditions under which additive noise or filtering distorts Granger-causal properties by inducing (spurious) Granger causality, as well as conditions under which it does not. For the errors-in-variables case, we give a continuity result, which implies that: a 'small' noise-to-signal ratio entails 'small' distortions in Granger causality. On filtering, we give general necessary and sufficient conditions under which 'spurious' causal relations between (vector) time series are not induced by linear transformations of the variables involved. This also yields transformations (or filters) which can eliminate Granger causality from one vector to another one. In a number of cases, we clarify results in the existing literature, with a number of calculations streamlining some existing approaches., (© 2018 The Authors. Journal of Time Series Analysis published by John Wiley & Sons Ltd.)

Alzheimer's Disease (AD) can take different courses: some patients remain relatively stable while others decline rapidly within a given period of time. Losing more than 3 Mini-Mental State Examination (MMSE) points in one year is classified as rapid cognitive decline (RCD). This study used neuropsychological test scores and quantitative EEG (QEEG) markers obtained at a baseline examination to identify if an AD patient will be suffering from RCD. Data from 68 AD patients of the multi-centric cohort study PRODEM-Austria were applied. 15 of the patients were classified into the RCD group. RCD versus non-RCD support vector machine (SVM) classifiers using QEEG markers as predictors obtained 72.1% and 77.9% accuracy ratings based on leave-one-out validation. Adding neuropsychological test scores of Boston Naming Test improved the classifier to 80.9% accuracy, 80% sensitivity, and 81.1% specificity. These results indicate that QEEG markers together with neuropsychological test scores can be used as RCD predictors.

We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

Objective: To investigate which single quantitative electro-encephalographic (QEEG) marker or which combination of markers correlates best with Alzheimer's disease (AD) severity as measured by the Mini-Mental State Examination (MMSE)., Methods: We compared quantitative EEG markers for slowing (relative band powers), synchrony (coherence, canonical correlation, Granger causality) and complexity (auto-mutual information, Shannon/Tsallis entropy) in 118 AD patients from the multi-centric study PRODEM Austria. Signal spectra were determined using an indirect spectral estimator. Analyses were adjusted for age, sex, duration of dementia, and level of education., Results: For the whole group (39 possible, 79 probable AD cases) MMSE scores explained 33% of the variations in relative theta power during face encoding, and 31% of auto-mutual information in resting state with eyes closed. MMSE scores explained also 25% of the overall QEEG factor. This factor was thus subordinate to individual markers. In probable AD, QEEG coefficients of determination were always higher than in the whole group, where MMSE scores explained 51% of the variations in relative theta power., Conclusions: Selected QEEG markers show strong associations with AD severity. Both cognitive and resting state should be used for QEEG assessments., Significance: Our data indicate theta power measured during face-name encoding to be most closely related to AD severity., (Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Background: Quantitative electroencephalogram (qEEG) recorded during cognitive tasks has been shown to differentiate between patients with Alzheimer's disease (AD) and healthy individuals. However, the association between various qEEG markers recorded during mnestic paradigms and clinical measures of AD has not been studied in detail., Objective: To evaluate if 'cognitive' qEEG is a useful diagnostic option, particularly if memory paradigms are used as cognitive stimulators., Methods: This study is part of the Prospective Registry on Dementia in Austria (PRODEM), a multicenter dementia research project. A cohort of 79 probable AD patients was included in a cross-sectional analysis. qEEG recordings performed in resting states were compared with recordings during cognitively active states. Cognition was evoked with a face-name paradigm and a paired-associate word list task, respectively. Relative band powers, coherence and auto-mutual information were computed as functions of MMSE scores for the memory paradigms and during rest. Analyses were adjusted for the co-variables age, sex, duration of dementia and educational level., Results: MMSE scores explained 36-51% of the variances of qEEG-markers. Face-name encoding with eyes open was superior to resting state with eyes closed in relative theta and beta1 power as well as coherence, whereas relative alpha power and auto-mutual information yielded more significant results during resting state with eyes closed. The face-name task yielded stronger correlations with MMSE scores than the verbal memory task., Conclusion: qEEG alterations recorded during mnestic activity, particularly face-name encoding showed the highest association with the MMSE and may serve as a clinically valuable marker for disease severity., (Copyright © 2014 Elsevier B.V. All rights reserved.)

We analyzed the relation between Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (qEEG) markers that were derived from canonical correlation analysis. This allowed an investigation of EEG synchrony between groups of EEG channels. In this study, we applied the data from 79 participants in the multi-centric cohort study PRODEM-Austria with probable AD. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. A quadratic regression model was used to describe the relation between MMSE and the qEEG synchrony markers. This relation was most significant in the δ and θ frequency bands in resting state, and between left-hemispheric central, temporal and parietal channel groups during the cognitive task. Here, the MMSE explained up to 40% of the qEEG marker's variation. QEEG markers showed an ambiguous trend, i.e. an increase of EEG synchrony in the initial stage of AD (MMSE>20) and a decrease in later stages. This effect could be caused by compensatory brain mechanisms. We conclude that the proposed qEEG markers are closely related to AD severity. Despite the ambiguous trend and the resulting diagnostic ambiguity, the qEEG markers could provide aid in the diagnostics of early-stage AD.

In this paper, we present a novel method for the identification of synchronization effects in multichannel electrocorticograms (ECoG). Based on autoregressive modeling, we define a dependency measure termed extrinsic-to-intrinsic power ratio (EIPR) which quantifies directed coupling effects in the time domain. Hereby, a dynamic input channel selection algorithm assures the estimation of the model parameters despite the strong spatial correlation among the high number of involved ECoG channels. We compare EIPR to the partial directed coherence, show its ability to indicate Granger causality and successfully validate a signal model. Applying EIPR to ictal ECoG data of patients suffering from temporal lobe epilepsy allows us to identify the electrodes of the seizure onset zone. The results obtained by the proposed method are in good accordance with the clinical findings.

Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures., (Copyright © 2013 Elsevier B.V. All rights reserved.)

High-frequency oscillations (HFOs) are a reliable indicator for the epileptic seizure onset zone (SOZ) in ECoG recordings. We propose a novel method for the automatic detection of ictal HFOs in the ripple band (80-250 Hz) based on CFAR matched sub-space filtering. This allows to track the early propagation of ictal HFOs, revealing initial and follow-up epileptic activity on the electrodes. We apply this methodology to two seizures from one patient suffering from focal epilepsy. The electrodes identified are in very good accordance with the visual HFO analysis by clinicians. Furthermore the electrodes with initial HFO activity are correlated well with the SOZ (conventional v-activity).

This paper deals with autoregressive (AR) models of singular spectra, whose corresponding transfer function matrices can be expressed in a stable AR matrix fraction description [Formula: see text] with [Formula: see text] a tall constant matrix of full column rank and with the determinantal zeros of [Formula: see text] all stable, i.e. in [Formula: see text]. To obtain a parsimonious AR model, a canonical form is derived and a number of advantageous properties are demonstrated. First, the maximum lag of the canonical AR model is shown to be minimal in the equivalence class of AR models of the same transfer function matrix. Second, the canonical form model is shown to display a nesting property under natural conditions. Finally, an upper bound is provided for the total number of real parameters in the obtained canonical AR model, which demonstrates that the total number of real parameters grows linearly with the number of rows in [Formula: see text].

This paper presents a systematic study on the properties of blocked linear systems that have resulted from blocking discrete-time linear time invariant systems. The main idea is to explore the relationship between the blocked and the unblocked systems. Existing results are reviewed and a number of important new results are derived. Focus is given particularly on the zero properties of the blocked system as no such study has been found in the literature.

In this paper we propose a novel segmentation method based on the relative frequency contributions of ictal multichannel ECoG data. Segments with predominant [[see text]]-activity are classified as epileptic. The seizure onset zone is determined by the temporal delay of the epileptic [[see text]]-activity (4-9Hz) on the different channels. We apply this methodology to three seizures of one patient suffering from focal epilepsy. The resulting segments reflect the visual characteristics of the ictal ECoG well. The seizure onset zone identified by the proposed method is in very good accordance with the clinical findings.

In this paper we assess a dependency measure for multivariate time series termed Extrinsic-to-Intrinsic-Power-Ratio (EIPR) using two different signal models. In a comparison with Partial Directed Coherence (PDC) we show that both measures correctly identify imposed couplings, but that limitations of the PDC do not affect EIPR. Moreover, EIPR is successfully used for the localization of the seizure onset zone in ECoG recordings from two epilepsy patients, given the exact seizure onset time. The electrodes identified by the proposed method are in excellent accordance with the clinical findings.

The paper consists of two main parts. The first part is concerned with different aspects of mathematical modeling of material flow systems for the linear static case. The problems considered are the description of the model class, data reconciliation, identification of subsystems and the analysis of system properties relevant e.g. for simulation. In the second part an application of the modeling tools proposed in the first part to a study on the metabolism of phosphorus in an Austrian region is given.

Material Flow Analysis (MFA) has become an important instrument in environmental science and pollution research. In this paper, we look at the MFA problem as a particularly structured system identification problem. Special emphasis is given to the linear, static case, where we describe a procedure for reconciliating the flow measurements and for estimating the unmeasured flows and the transfer coefficients by taking into account a priori restrictions such as balance equations.

The redox potential depending on the pH-value and the concentration of nitrite was examined in the fasting gastric juice samples of 132 anamnestically healthy, young, voluntary test-persons. It turned out that small pH-values (acid) are associated with high redox potentials and big pH-values (neutral) with smaller redox potentials. Therefore the hydrogen ion pressure (rH-value) resulting from these two quantities generally is situated in the indifferent range. Reducing properties of the whole juice can only occasionally be found, and apparently only then, if a neutral juice is acidified. Oxidizing properties have to be classified as exceptional cases. It cannot absolutely be traced on behalf of the findings, that can be raised with natural juices, if and when answered affirmatively, which effects the redox potential has on the formation of bacterial nitrite.