Applications of multi-variate analysis of variance (MANOVA) to multi-electrode array electrophysiology data

Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

Verification of Nuclear Magnetic Resonance Characterization of Traditional Homeopathically Manufactured Metal (Cuprum metallicum) and Plant (Gelsemium sempervirens) Medicines and Controls

BACKGROUND

 Nuclear magnetic resonance (NMR) proton relaxation is sensitive to the dynamics of the water molecule, H₂O, through the interaction of the spin of the proton (₁H) with external magnetic and electromagnetic fields. NMR relaxation times describe how quickly the spin of ₁H, forced in a direction by an external electromagnetic field, returns to a normal resting position. As a result, such measurements allow us potentially to describe higher structuring of water in homeopathic medicines.

OBJECTIVE

 The purpose of the present study was to verify whether specific NMR relaxation times could be measured in full lines of cH dynamizations of a metal (copper) and of a plant substance (Gelsemium sempervirens), compared with a solvent control, a potentized lactose control and a control prepared by simple dilution, in three production lines. It is aimed at verification of a previous publication (2017) on two new manufacturing lines of the same starting material and controls.

MATERIALS AND METHODS

 To monitor dilution and potentization processes, measurements of ₁H spin-lattice T1 and spin-spin T2 relaxation times were used. T1 and T2 relaxation times were measured at 25°C with a spin analyser working at a frequency of 20 MHz. To account for its possible role as a confounding factor, free oxygen was also measured in all samples, using a MicroOptode meter.

RESULTS

 When the values of the three production lines were pooled, a statistically significant discrimination of NMR relaxation times between the medicines and their controls was confirmed. We found for copper cH and Gelsemium sempervirens cH a highly significant influence of the starting material (p = 0.008), a highly significant influence of level of dilution (p < 0.001), and a significant influence of the O₂ concentration (p = 0.04).

CONCLUSIONS

 We have evidence of an obvious retention of a specific magnetic resonance signal when a substance (lactose, copper, Gelsemium) is diluted/potentized in pure water. This means that homeopathic solutions cannot be considered to be pure water. O₂ is a covariant and not an explanatory variable: this factor itself is too weak to explain the NMR signal specificities in potentized samples. Homeopathic dilutions may thus have a specific material configuration governed not only by the potentized substance but also by the chemical nature of the containers, the chemical nature of dissolved gases and even by the electromagnetic environment. This sensitivity of homeopathically prepared medicines to electromagnetic fields may be amplified by the processes routinely applied during their preparation; because it occurs only when a dynamization has been performed, we may call this phenomenon "dynamic pharmacy".

Suprathreshold stochastic resonance in neural processing tuned by correlation

Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.

Controlling precise movement with stochastic signals

In a noisy system, such as the nervous system, can movements be precisely controlled as experimentally demonstrated? We point out that the existing theory of motor control fails to provide viable solutions. However, by adopting a generalized approach to the nonconvex optimization problem with the Young measure theory, we show that a precise movement control is possible even with stochastic control signals. Numerical results clearly demonstrate that a considerable significant improvement of movement precisions is achieved. Our generalized approach proposes a new way to solve optimization problems in biological systems when a precise control is needed.

Granger causality vs. dynamic Bayesian network inference: a comparative study

Background

In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.

Results

In this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better.

Conclusion

When the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better.

Uncovering interactions in the frequency domain

Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multi-electrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey.

A novel approach to detect hot-spots in large-scale multivariate data

Background

Progressive advances in the measurement of complex multifactorial components of biological processes involving both spatial and temporal domains have made it difficult to identify the variables (genes, proteins, neurons etc.) significantly changed activities in response to a stimulus within large data sets using conventional statistical approaches. The set of all changed variables is termed hot-spots. The detection of such hot spots is considered to be an NP hard problem, but by first establishing its theoretical foundation we have been able to develop an algorithm that provides a solution.

Results

Our results show that a first-order phase transition is observable whose critical point separates the hot-spot set from the remaining variables. Its application is also found to be more successful than existing approaches in identifying statistically significant hot-spots both with simulated data sets and in real large-scale multivariate data sets from gene arrays, electrophysiological recording and functional magnetic resonance imaging experiments.

Conclusion

In summary, this new statistical algorithm should provide a powerful new analytical tool to extract the maximum information from complex biological multivariate data.

Behavioural and neurophysiological evidence for face identity and face emotion processing in animals

Visual cues from faces provide important social information relating to individual identity, sexual attraction and emotional state. Behavioural and neurophysiological studies on both monkeys and sheep have shown that specialized skills and neural systems for processing these complex cues to guide behaviour have evolved in a number of mammals and are not present exclusively in humans. Indeed, there are remarkable similarities in the ways that faces are processed by the brain in humans and other mammalian species. While human studies with brain imaging and gross neurophysiological recording approaches have revealed global aspects of the face-processing network, they cannot investigate how information is encoded by specific neural networks. Single neuron electrophysiological recording approaches in both monkeys and sheep have, however, provided some insights into the neural encoding principles involved and, particularly, the presence of a remarkable degree of high-level encoding even at the level of a specific face. Recent developments that allow simultaneous recordings to be made from many hundreds of individual neurons are also beginning to reveal evidence for global aspects of a population-based code. This review will summarize what we have learned so far from these animal-based studies about the way the mammalian brain processes the faces and the emotions they can communicate, as well as associated capacities such as how identity and emotion cues are dissociated and how face imagery might be generated. It will also try to highlight what questions and advances in knowledge still challenge us in order to provide a complete understanding of just how brain networks perform this complex and important social recognition task.

Spike sorting based upon machine learning algorithms (SOMA)

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

Decoding spike train ensembles: tracking a moving stimulus

We consider the issue of how to read out the information from nonstationary spike train ensembles. Based on the theory of censored data in statistics, we propose a 'censored' maximum-likelihood estimator (CMLE) for decoding the input in an unbiased way when the spike activity is observed over time windows of finite length. Compared with a rate-based, moment estimator, the CMLE is proved consistently more efficient, particularly with nonstationary inputs. Using our approach, we show that a dynamical input to a group of neurons can be inferred accurately and with high temporal resolution (50 ms) using as few as about one spike per neuron within each decoding window. By applying our theoretical results to a population coding setting, we then demonstrate that a spiking neural network can encode spatial information in such a way to allow fast and precise tracking of a moving target.

Microtechnology: meet neurobiology

The field of neuroscience has always been attractive to engineers. Neurons and their connections, like tiny circuit elements, process and transmit information in a dramatic way that is intimately curious to researchers in the computer science and engineering fields. Of particular interest has been the recent push in applying microtechnology to the field of neuroscience. This review is meant to provide an overview of some of the subtle nuances of the nervous system and outline recent advances in lab on a chip applications in neurobiology. It also aims to highlight some of the challenges the field faces in the hopes of encouraging new engineering researchers to collaborate with neurobiologists to help advance our basic understanding of the nervous system and create novel applications based on neuroengineering principles.

Detecting correlation changes in electrophysiological data

A correlation multi-variate analysis of variance (MANOVA) test to statistically analyze changing patterns of multi-electrode array (MEA) electrophysiology data is developed. The approach enables us not only to detect significant mean changes, but also significant correlation changes in response to external stimuli. Furthermore, a method to single out hot-spot variables in the MEA data both for the mean and correlation is provided. Our methods have been validated using both simulated spike data and recordings from sheep inferotemporal cortex.

Detecting time-dependent coherence between non-stationary electrophysiological signals--a combined statistical and time-frequency approach

Various time-frequency methods have been used to study time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence estimate using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. The approach is based on averaging over repeat trials. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time-frequency based coherence. In contrast to some recent studies, we find that CWT based coherence estimates do not supersede STFT based estimates. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. Tests are presented to investigate the time and frequency discrimination capabilities of the two approaches. The methods are applied to two experimental data sets: electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in a healthy subject, and local field potential (LFP) and surface EMG recordings during resting tremor in a Parkinsonian patient. Supporting software is available at and .

A nonparametric approach to extract information from interspike interval data

In this work we develop an approach to extracting information from neural spike trains. Using the expectation-maximization (EM) algorithm, interspike interval data from experiments and simulations are fitted by mixtures of distributions, including Gamma, inverse Gaussian, log-normal, and the distribution of the interspike intervals of the leaky integrate-and-fire model. In terms of the Kolmogorov-Smirnov test for goodness-of-fit, our approach is proved successful (P>0.05) in fitting benchmark data for which a classical parametric approach has been shown to fail before. In addition, we present a novel method to fit mixture models to censored data, and discuss two examples of the application of such a method, which correspond to the case of multiple-trial and multielectrode array data. A MATLAB implementation of the algorithm is available for download from .