Corrigendum: Complexity changes in functional state dynamics suggest focal connectivity reductions

[This corrects the article DOI: 10.3389/fnhum.2022.958706.].

Complexity changes in functional state dynamics suggest focal connectivity reductions

The past two decades have seen an explosion in the methods and directions of neuroscience research. Along with many others, complexity research has rapidly gained traction as both an independent research field and a valuable subdiscipline in computational neuroscience. In the past decade alone, several studies have suggested that psychiatric disorders affect the spatiotemporal complexity of both global and region-specific brain activity (Liu et al., 2013; Adhikari et al., 2017; Li et al., 2018). However, many of these studies have not accounted for the distributed nature of cognition in either the global or regional complexity estimates, which may lead to erroneous interpretations of both global and region-specific entropy estimates. To alleviate this concern, we propose a novel method for estimating complexity. This method relies upon projecting dynamic functional connectivity into a low-dimensional space which captures the distributed nature of brain activity. Dimension-specific entropy may be estimated within this space, which in turn allows for a rapid estimate of global signal complexity. Testing this method on a recently acquired obsessive-compulsive disorder dataset reveals substantial increases in the complexity of both global and dimension-specific activity versus healthy controls, suggesting that obsessive-compulsive patients may experience increased disorder in cognition. To probe the potential causes of this alteration, we estimate subject-level effective connectivity via a Hopf oscillator-based model dynamic model, the results of which suggest that obsessive-compulsive patients may experience abnormally high connectivity across a broad network in the cortex. These findings are broadly in line with results from previous studies, suggesting that this method is both robust and sensitive to group-level complexity alterations.

Neural Entrainment Meets Behavior: The Stability Index as a Neural Outcome Measure of Auditory-Motor Coupling

Understanding rhythmic behavior in the context of coupled auditory and motor systems has been of interest to neurological rehabilitation, in particular, to facilitate walking. Recent work based on behavioral measures revealed an entrainment effect of auditory rhythms on motor rhythms. In this study, we propose a method to compute the neural component of such a process from an electroencephalographic (EEG) signal. A simple auditory-motor synchronization paradigm was used, where 28 healthy participants were instructed to synchronize their finger-tapping with a metronome. The computation of the neural outcome measure was carried out in two blocks. In the first block, we used Generalized Eigendecomposition (GED) to reduce the data dimensionality to the component which maximally entrained to the metronome frequency. The scalp topography pointed at brain activity over contralateral sensorimotor regions. In the second block, we computed instantaneous frequency from the analytic signal of the extracted component. This returned a time-varying measure of frequency fluctuations, whose standard deviation provided our "stability index" as a neural outcome measure of auditory-motor coupling. Finally, the proposed neural measure was validated by conducting a correlation analysis with a set of behavioral outcomes from the synchronization task: resultant vector length, relative phase angle, mean asynchrony, and tempo matching. Significant moderate negative correlations were found with the first three measures, suggesting that the stability index provided a quantifiable neural outcome measure of entrainment, with selectivity towards phase-correction mechanisms. We address further adoption of the proposed approach, especially with populations where sensorimotor abilities are compromised by an underlying pathological condition. The impact of using stability index can potentially be used as an outcome measure to assess rehabilitation protocols, and possibly provide further insight into neuropathological models of auditory-motor coupling.

Large-Scale and Multiscale Networks in the Rodent Brain during Novelty Exploration

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, because of limited data availability and to the difficulty of analyzing rich multivariate datasets. Here, we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential (LFP) activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (∼4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.

Estimation and model selection in general spatial dynamic panel data models

Commonly used methods for estimating parameters of a spatial dynamic panel data model include the two-stage least squares, quasi-maximum likelihood, and generalized moments. In this paper, we present an approach that uses the eigenvalues and eigenvectors of a spatial weight matrix to directly construct consistent least-squares estimators of parameters of a general spatial dynamic panel data model. The proposed methodology is conceptually simple and efficient and can be easily implemented. We show that the proposed parameter estimators are consistent and asymptotically normally distributed under mild conditions. We demonstrate the superior performance of our approach via extensive simulation studies. We also provide a real data example.

Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.

Relating the Structure of Noise Correlations in Macaque Primary Visual Cortex to Decoder Performance

Noise correlations in neuronal responses can have a strong influence on the information available in large populations. In addition, the structure of noise correlations may have a great impact on the utility of different algorithms to extract this information that may depend on the specific algorithm, and hence may affect our understanding of population codes in the brain. Thus, a better understanding of the structure of noise correlations and their interplay with different readout algorithms is required. Here we use eigendecomposition to investigate the structure of noise correlations in populations of about 50–100 simultaneously recorded neurons in the primary visual cortex of anesthetized monkeys, and we relate this structure to the performance of two common decoders: the population vector and the optimal linear estimator. Our analysis reveals a non-trivial correlation structure, in which the eigenvalue spectrum is composed of several distinct large eigenvalues that represent different shared modes of fluctuation extending over most of the population, and a semi-continuous tail. The largest eigenvalue represents a uniform collective mode of fluctuation. The second and third eigenvalues typically show either a clear functional (i.e., dependent on the preferred orientation of the neurons) or spatial structure (i.e., dependent on the physical position of the neurons). We find that the number of shared modes increases with the population size, being roughly 10% of that size. Furthermore, we find that the noise in each of these collective modes grows linearly with the population. This linear growth of correlated noise power can have limiting effects on the utility of averaging neuronal responses across large populations, depending on the readout. Specifically, the collective modes of fluctuation limit the accuracy of the population vector but not of the optimal linear estimator.

Optical coherence tomography angiography-based capillary velocimetry

Challenge persists in the field of optical coherence tomography (OCT) when it is required to quantify capillary blood flow within tissue beds in vivo. We propose a useful approach to statistically estimate the mean capillary flow velocity using a model-based statistical method of eigendecomposition (ED) analysis of the complex OCT signals obtained with the OCT angiography (OCTA) scanning protocol. ED-based analysis is achieved by the covariance matrix of the ensemble complex OCT signals, upon which the eigenvalues and eigenvectors that represent the subsets of the signal makeup are calculated. From this analysis, the signals due to moving particles can be isolated by employing an adaptive regression filter to remove the eigencomponents that represent static tissue signals. The mean frequency (MF) of moving particles can be estimated by the first lag-one autocorrelation of the corresponding eigenvectors. Three important parameters are introduced, including the blood flow signal power representing the presence of blood flow (i.e., OCTA signals), the MF indicating the mean velocity of blood flow, and the frequency bandwidth describing the temporal flow heterogeneity within a scanned tissue volume. The proposed approach is tested using scattering phantoms, in which microfluidic channels are used to simulate the functional capillary vessels that are perfused with the scattering intralipid solution. The results indicate a linear relationship between the MF and mean flow velocity. In vivo animal experiments are also conducted by imaging mouse brain with distal middle cerebral artery ligation to test the capability of the method to image the changes in capillary flows in response to an ischemic insult, demonstrating the practical usefulness of the proposed method for providing important quantifiable information about capillary tissue beds in the investigations of neurological conditions in vivo.