Neural Data Analysis Research Articles

Robust and scalable Bayesian analysis of spatial neural tuning function data

A common analytical problem in neuroscience is the interpretation of neural activity with respect to sensory input or behavioral output. This is typically achieved by regressing measured neural activity against known stimuli or behavioral variables to produce a “tuning function” for each neuron. Unfortunately, because this approach handles neurons individually, it cannot take advantage of simultaneous measurements from spatially adjacent neurons that often have similar tuning properties. On the other hand, sharing information between adjacent neurons can errantly degrade estimates of tuning functions across space if there are sharp discontinuities in tuning between nearby neurons. In this paper, we develop a computationally efficient block Gibbs sampler that effectively pools information between neurons to denoise tuning function estimates while simultaneously preserving sharp discontinuities that might exist in the organization of tuning across space. This method is fully Bayesian, and its computational cost per iteration scales sub-quadratically with total parameter dimensionality. We demonstrate the robustness and scalability of this approach by applying it to both real and synthetic datasets. In particular, an application to data from the spinal cord illustrates that the proposed methods can dramatically decrease the experimental time required to accurately estimate tuning functions.

A predictive coding account of bistable perception - a model-based fMRI study.

In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model’s predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants’ perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work provides a theoretical framework that allows for the analysis of behavioural and neural data using a predictive coding perspective on bistable perception. In this, our approach posits a crucial role of prediction error signalling for the resolution of perceptual ambiguities.

Neural spike train similarity algorithm detects differences in temporal patterning of bat echolocation call sequences

Bats emit echolocation sounds in complex temporal sequences that change to accommodate dynamic surroundings. Efforts to quantify how these patterns change have included analysis of inter-pulse intervals, sonar sound groups, and changes in individual signal parameters. No standardized method has been adopted for quantifying whether sequences of echolocation calls are similar or different beyond these individual dimensions. Here, a new method is presented for assessing the similarity in temporal structure between trains of bat echolocation sounds. The spike-train similarity space (SSIMS) algorithm, originally designed for neural data analysis, was applied to determine which features of the environment influence temporal patterning of echolocation sounds emitted by flying big brown bats (Eptesicus fuscus). Using a relational point-process framework, SSIMS was able to discriminate between pulse sequences recorded in different flight environments, as well as to separate flights depending on the bat’s expectation of its surroundings based on previous experience.

Analysis and Modeling of Subthreshold Neural Multi-Electrode Array Data by Statistical Field Theory

Multi-electrode arrays (MEA) are increasingly used to investigate spontaneous neuronal network activity. The recorded signals comprise several distinct components: Apart from artifacts without biological significance, one can distinguish between spikes (action potentials) and subthreshold fluctuations (local fields potentials). Here we aim to develop a theoretical model that allows for a compact and robust characterization of subthreshold fluctuations in terms of a Gaussian statistical field theory in two spatial and one temporal dimension. What is usually referred to as the driving noise in the context of statistical physics is here interpreted as a representation of the neural activity. Spatial and temporal correlations of this activity give valuable information about the connectivity in the neural tissue. We apply our methods on a dataset obtained from MEA-measurements in an acute hippocampal brain slice from a rat. Our main finding is that the empirical correlation functions indeed obey the logarithmic behavior that is a general feature of theoretical models of this kind. We also find a clear correlation between the activity and the occurrence of spikes. Another important insight is the importance of correctly separating out certain artifacts from the data before proceeding with the analysis.

行動下の動物において神経細胞への入出力を解析する : 手の運動制御を例に( 神経データの信号解析)

行動下の動物において神経細胞への入出力を解析する : 手の運動制御を例に( 神経データの信号解析)

Refining the clustering coefficient for analysis of social and neural network data

In this paper we show how a deeper analysis of the clustering coefficient in a network can be used to assess functional connections in the human brain. Our metric of edge clustering centrality considers the frequency at which an edge appears across all local subgraphs that are induced by each vertex and its neighbors. This analysis is tied to a problem from structural graph theory in which we seek the largest subgraph that is a Cartesian product of two complete bipartite graphs \(K_{1,m}\) and \(K_{1,1}\). We investigate this property and compare it to other known edge centrality metrics. Finally, we apply the property of clustering centrality to an analysis of functional MRI data obtained, while healthy participants pantomimed object use or identified objects.

The evaluation of i-SIDRA – a tool for intelligent feedback – in a course on the anatomy of the locomotor system

ObjectiveThis paper presents an empirical study of a formative mobile-based assessment approach that can be used to provide students with intelligent diagnostic feedback to test its educational effectiveness. MethodAn audience response system called SIDRA was integrated with a neural network-based data analysis to generate diagnostic feedback for guided learning. A total of 200 medical students enrolled in a General and Descriptive Anatomy of the Locomotor System course were taught using two different methods. Ninety students in the experimental group used intelligent SIDRA (i-SIDRA), whereas 110 students in the control group received the same training but without employing i-SIDRA. ResultsIn the students’ final exam grades, a statistically significant difference was found between those students that used i-SIDRA as opposed to a traditional teaching methodology (T(162)=2.597; p=0.010). The increase in the number of correct answers during the feedback guided learning process from the first submission to the last submission in four multiple choice question tests was also analyzed. There were average increases of 20.00% (Test1), 11.34% (Test2), 8.88% (Test3) and 13.43% (Test4) in the number of correct answers. In a questionnaire rated on a five-point Likert-type scale, the students expressed satisfaction with the content (M=4.2) and feedback (M=3.5) provided by i-SIDRA and the methodology (M=4.2) used to learn anatomy. ConclusionsThe use of audience response systems enriched with feedback such as i-SIDRA improves medical degree students’ performance as regards anatomy of the locomotor system. The knowledge state diagrams representing students’ behavior allow instructors to study their progress so as to identify what they still need to learn.

Demixed principal component analysis of neural population data.

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Vigilance in the laboratory predicts avoidance in the real world: A dimensional analysis of neural, behavioral, and ecological momentary data in anxious youth

Vigilance and avoidance of threat are observed in anxious adults during laboratory tasks, and are posited to have real-world clinical relevance, but data are mixed in anxious youth. We propose that vigilance-avoidance patterns will become evident in anxious youth through a focus on individual differences and real-world strategic avoidance. Decreased functional connectivity between the amygdala and prefrontal cortex (PFC) could play a mechanistic role in this link. 78 clinically anxious youth completed a dot-probe task to assess vigilance to threat while undergoing fMRI. Real-world avoidance was assessed using Ecological Momentary Assessment (EMA) of self-reported suppression and distraction during negative life events. Vigilance toward threat was positively associated with EMA distraction and suppression. Functional connectivity between a right amygdala seed region and dorsomedial and right dorsolateral PFC regions was inversely related to EMA distraction. Dorsolateral PFC-amygdalar connectivity statistically mediated the relationship between attentional vigilance and real-world distraction. Findings suggest anxious youth showing attentional vigilance toward threat are more likely to use suppression and distraction to regulate negative emotions. Reduced PFC control over limbic reactivity is a possible neural substrate of this pattern. These findings lend ecological validity to laboratory vigilance assessments and suggest PFC-amygdalar connectivity is a neural mechanism bridging laboratory and naturalistic contexts.

The mechanics of state-dependent neural correlations.

Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.

TOOLCONNECT: A powerful toolbox for functional connectivity analysis of in vitro neural networks

Event Abstract Back to Event TOOLCONNECT: A powerful toolbox for functional connectivity analysis of in vitro neural networks Vito P. Pastore1*, Aleksandar Godjoski1, Sergio Martinoia1 and Paolo Massobrio1 1 University of Genova, Department of Informatics, Bioengineering, Robotics, System Engineering , Italy Motivation One of the goal of contemporary neuroscience is to study the interplay between topology, structure, functional-effective connectivity and neuronal dynamics at different level of complexity and on different experimental models (from simple in-vitro networks to whole brain areas). Functional connectivity is defined as the strength of the influence one network member has on another during ongoing behavior [1]. The analysis of multiple neural spike train data recorded from experimental models has gained tremendous relevance recently with the widespread application of Micro-Electrode Arrays (MEAs) [2]. At present, to the best of our knowledge, there is no available dedicated software that puts together a set of different functional connectivity analysis methods. Thus, we developed a user-friendly toolbox [3] in order to provide the researchers community a powerful tool to perform functional connectivity analysis on in-vitro neuronal networks coupled to standard and high-density MEAs, while guaranteeing computational efficiency and high accuracy. Material and Methods We implemented 'ToolConnect' toolbox as a standalone windows Graphical User Interface (GUI) application, using C# and Microsoft Visual Studio with .NET framework 4.5 development environment. The software is designed to be intuitive and straightforward to use. It is based on several windows forms and a friendly and modular GUI through which provides the user with powerful tools to manipulate and analyze data. ToolConnect offers functional connectivity analysis based on two correlation (cross-correlation and partial correlation) and two information theory based methods (transfer entropy and joint entropy). Cross correlation measures the frequency at which one cell fires as a function of time relative to the firing of a spike in another cell [4]. Partial correlation allows to distinguish between direct and indirect connections by removing the portion of the relationship between two spike trains that can be attributed to linear relationships with recorded spike trains from other neurons [5, 6]. Transfer Entropy is an information theoretic measure able to estimate causal relationships between time series taking into account their past activity [7]. Joint Entropy analyzes the cross Inter-Spike-Intervals (cISI): if two neurons are strongly connected the cISI histogram will show a peak and Joint Entropy will be close to zero, otherwise the cISI histogram will be almost flat, and the Joint Entropy will be high. The graphical section offers dedicated interfaces and allows the user to: i) plot the correlograms between each couple of the set of analyzed electrodes (for cross- and partial correlation); ii) manage, thresh and plot the Connectivity Matrix (CM) and the connectivity graph; iii) compute some powerful metrics that allow to extract the main topological features (degree, cluster coefficient, path length). One of the major features of the toolbox is its independence from the acquisition system (e.g. Multi Channel Systems, Qwane Biosciences, 3Brain) and from the MEA layout (number of microelectrodes and spatial organization). Results We designed and developed ToolConnect taking care to satisfy the user-friendliness requirement. According to this, our software has a GUI, which permits also to inexperienced users to perform functional connectivity analysis, to graphically represent the results, hiding the algorithms implementation specifics and software’s code design. Figure 1 shows a screenshot of ToolConnect’s GUI. Figure 2 shows the connectivity graphs obtained from the analysis of cortical networks coupled to the MEA60 and the MEA2100 acquisition systems of Multi Channel Systems (www.multichannelsystems.com; MCS, Reutlingen, Germany) and the BioCam acquisition system of 3Brain Systems. To assess the performances of tool connect we performed functional-connectivity analysis based on the cross-correlation method on hippocampal neuronal networks coupled to the MEA 60 from Multi-Channel Systems (MCS), during spontaneous and stimulus-evoked activity[3]. Discussion To the best of our knowledge, ToolConnect is the first functional connectivity toolbox dedicated to the analysis of multiple spike trains recorded from in-vitro neural networks coupled to MEAs. It provides the user with a complete set of computational and graphical tools of intuitive and straightforward usage through a dedicated and modular GUI. ToolConnect is implemented taking care of the optimization of the resources usage (requested RAM) and the reduction of the computational time. We were able to obtain acceptable performances with computational time lower than 2 minutes (for 10 minutes of recording sampled at 10 kHz). These performances make ToolConnect compatible with high-density recording systems (e.g., the 4096 electrodes of the 3brain system). In this way, it will be possible to perform functional connectivity analysis on neural networks with dimensions of thousands of neurons preserving an acceptable spatial and temporal resolution, hence allowing to obtain realistic and complete information on the dynamics and the topology of such systems.

A Functional Connectivity Analysis Toolbox for Multiple Spike Trains Data: �ToolConnect�

A Functional Connectivity Analysis Toolbox for Multiple Spike Trains Data: �ToolConnect�

Functional changes in inter- and intra-hemispheric cortical processing underlying degraded speech perception

Previous studies suggest that at poorer signal-to-noise ratios (SNRs), auditory cortical event-related potentials are weakened, prolonged, and show a shift in the functional lateralization of cerebral processing from left to right hemisphere. Increased right hemisphere involvement during speech-in-noise (SIN) processing may reflect the recruitment of additional brain resources to aid speech recognition or alternatively, the progressive loss of involvement from left linguistic brain areas as speech becomes more impoverished (i.e., nonspeech-like). To better elucidate the brain basis of SIN perception, we recorded neuroelectric activity in normal hearing listeners to speech sounds presented at various SNRs. Behaviorally, listeners obtained superior SIN performance for speech presented to the right compared to the left ear (i.e., right ear advantage). Source analysis of neural data assessed the relative contribution of region-specific neural generators (linguistic and auditory brain areas) to SIN processing. We found that left inferior frontal brain areas (e.g., Broca's areas) partially disengage at poorer SNRs but responses do not right lateralize with increasing noise. In contrast, auditory sources showed more resilience to noise in left compared to right primary auditory cortex but also a progressive shift in dominance from left to right hemisphere at lower SNRs. Region- and ear-specific correlations revealed that listeners' right ear SIN advantage was predicted by source activity emitted from inferior frontal gyrus (but not primary auditory cortex). Our findings demonstrate changes in the functional asymmetry of cortical speech processing during adverse acoustic conditions and suggest that “cocktail party” listening skills depend on the quality of speech representations in the left cerebral hemisphere rather than compensatory recruitment of right hemisphere mechanisms.

Editorial: Representational states in memory: where do we stand?

Editorial: Representational states in memory: where do we stand?

Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

NeuroCa: integrated framework for systematic analysis of spatiotemporal neuronal activity patterns from large-scale optical recording data.

Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of [Formula: see text] neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements.

Multiscale entropy analysis of resting-state magnetoencephalogram with tensor factorisations in Alzheimer's disease.

Tensor factorisations have proven useful to model amplitude and spectral information of brain recordings. Here, we assess the usefulness of tensor factorisations in the multiway analysis of other brain signal features in the context of complexity measures recently proposed to inspect multiscale dynamics. We consider the "refined composite multiscale entropy" (rcMSE), which computes entropy "profiles" showing levels of physiological complexity over temporal scales for individual signals. We compute the rcMSE of resting-state magnetoencephalogram (MEG) recordings from 36 patients with Alzheimer's disease and 26 control subjects. Instead of traditional simple visual examinations, we organise the entropy profiles as a three-way tensor to inspect relationships across temporal and spatial scales and subjects with multiway data analysis techniques based on PARAFAC and PARAFAC2 factorisations. A PARAFAC2 model with two factors was appropriate to account for the interactions in the entropy tensor between temporal scales and MEG channels for all subjects. Moreover, the PARAFAC2 factors had information related to the subjects' diagnosis, achieving a cross-validated area under the ROC curve of 0.77. This confirms the suitability of tensor factorisations to represent electrophysiological brain data efficiently despite the unsupervised nature of these techniques. This article is part of a Special Issue entitled 'Neural data analysis'.

Fast and Scalable Multi-Way Analysis of Massive Neural Data

Analysis of neural data with multiple modes and high density has recently become a trend with the advances in neuroscience research and practices. There exists a pressing need for an approach to accurately and uniquely capture the features without loss or destruction of the interactions amongst the modes (typically) of space, time, and frequency. Moreover, the approach must be able to quickly analyze the neural data of exponentially growing scales and sizes, in tens or even hundreds of channels, so that timely conclusions and decisions may be made. A salient approach to multi-way data analysis is the parallel factor analysis (PARAFAC) that manifests its effectiveness in the decomposition of the electroencephalography (EEG). However, the conventional PARAFAC is only suited for offline data analysis due to the high complexity, which computes to be $O(n^{2})$ with the increasing data size. In this study, a large-scale PARAFAC method has been developed, which is supported by general-purpose computing on the graphics processing unit (GPGPU). Comparing to the PARAFAC running on conventional CPU-based platform, the new approach dramatically excels by ${>}360$ times in run-time performance, and effectively scales by ${>}400$ times in all dimensions. Moreover, the proposed approach forms the basis of a model for the analysis of electrocochleography (ECoG) recordings obtained from epilepsy patients, which proves to be effective in the epilepsy state detection. The time evolutions of the proposed model are well correlated with the clinical observations. Moreover, the frequency signature is stable and high in the ictal phase. Furthermore, the spatial signature explicitly identifies the propagation of neural activities among various brain regions. The model supports real-time analysis of ECoG in ${>}1{,}000$ channels on an inexpensive and available cyber-infrastructure.

Large-scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU).

Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis. Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22× speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision.

Refracture candidate selection using hybrid simulation with neural network and data analysis techniques

Abstract By now very few analytical models have been developed to select well refracture candidates due to complicated multi-parameter relationships. In this study, we proposed a new method by merging mathematical data analysis with feed forward back propagation neural network utilizing post-fracturing data. The model preference is thereby based on the correlation coefficients of several selected independent variables against production performance. The solution to this expense is a tool that can identify restimulation candidates quickly and economically. We employ two mathematical analysis techniques to filter several independent yet influential parameters as inputs. These parameters are supposed to be primary factors with high impact on potential production improvement. Then we use these well data to train an artificial neural network (ANN) to predict post-fracture production. The errors of the best samples should decrease consistently along with the training samples. A minimal error of the training sets is not necessary because over-fitting of the network could be memorizing rather than generalizing. The testing results showed that there is higher than 80% prediction accuracy, which is good enough for decision making. This methodology gives credible prediction results when it is applied in Zhongyuan oilfield and provides the operators with useful recommendations to make decisions for restimulation.