scholarly journals Multi-Input, Multi-Output Neuronal Mode Network Approach to Modeling the Encoding Dynamics and Functional Connectivity of Neural Systems

2019 ◽  
Vol 31 (7) ◽  
pp. 1327-1355 ◽  
Author(s):  
Kunling Geng ◽  
Dae C. Shin ◽  
Dong Song ◽  
Robert E. Hampson ◽  
Samuel A. Deadwyler ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Synthetic Data ◽  
Estimation Accuracy ◽  
Neural Systems ◽  
Neural Ensembles ◽  
Number Of Zeros ◽  
Model Interpretation ◽  
Novel Method ◽  
Imbalanced Class ◽  
Analyze Data

This letter proposes a novel method, multi-input, multi-output neuronal mode network (MIMO-NMN), for modeling encoding dynamics and functional connectivity in neural ensembles such as the hippocampus. Compared with conventional approaches such as the Volterra-Wiener model, linear-nonlinear-cascade (LNC) model, and generalized linear model (GLM), the NMN has several advantages in terms of estimation accuracy, model interpretation, and functional connectivity analysis. We point out the limitations of current neural spike modeling methods, especially the estimation biases caused by the imbalanced class problem when the number of zeros is significantly larger than ones in the spike data. We use synthetic data to test the performance of NMN with a comparison of the traditional methods, and the results indicate the NMN approach could reduce the imbalanced class problem and achieve better predictions. Subsequently, we apply the MIMO-NMN method to analyze data from the human hippocampus. The results indicate that the MIMO-NMN method is a promising approach to modeling neural dynamics and analyzing functional connectivity of multi-neuronal data.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

Functional connectivity reveals load dependent neural systems underlying encoding and maintenance in verbal working memory

Neuroscience ◽  
2006 ◽  
Vol 139 (1) ◽  
pp. 317-325 ◽  
Author(s):  
T.S. Woodward ◽  
T.A. Cairo ◽  
C.C. Ruff ◽  
Y. Takane ◽  
M.A. Hunter ◽  
...  
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Verbal Working Memory ◽  
Neural Systems

scholarly journals Sub-Frame Evaluation of Frame Synchronization for Camera Network Using Linearly Oscillating Light Spot

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6148
Author(s):  
Hyuno Kim ◽  
Masatoshi Ishikawa
Keyword(s):  
Visual Information ◽  
Evaluation Method ◽  
Measurement Instrument ◽  
Light Spot ◽  
Estimation Accuracy ◽  
Accurate Data ◽  
Frame Synchronization ◽  
Camera Network ◽  
Novel Method ◽  
Synchronization Accuracy

Precisely evaluating the frame synchronization of the camera network is often required for accurate data fusion from multiple visual information. This paper presents a novel method to estimate the synchronization accuracy by using inherent visual information of linearly oscillating light spot captured in the camera images instead of using luminescence information or depending on external measurement instrument. The suggested method is compared to the conventional evaluation method to prove the feasibility. Our experiment result implies that the estimation accuracy of the frame synchronization can be achieved in sub-millisecond order.

Functional Connectivity Relationships Predict Similarities in Task Activation and Pattern Information during Associative Memory Encoding

2014 ◽  
Vol 26 (5) ◽  
pp. 1085-1099 ◽  
Author(s):  
Maureen Ritchey ◽  
Andrew P. Yonelinas ◽  
Charan Ranganath
Keyword(s):  
Functional Connectivity ◽  
Functional Properties ◽  
Associative Memory ◽  
Large Scale ◽  
Memory Task ◽  
Neural Systems ◽  
General Level ◽  
Specific Information ◽  
Memory Encoding ◽  
Cortical Regions

Neural systems may be characterized by measuring functional interactions in the healthy brain, but it is unclear whether components of systems defined in this way share functional properties. For instance, within the medial temporal lobes (MTL), different subregions show different patterns of cortical connectivity. It is unknown, however, whether these intrinsic connections predict similarities in how these regions respond during memory encoding. Here, we defined brain networks using resting state functional connectivity (RSFC) then quantified the functional similarity of regions within each network during an associative memory encoding task. Results showed that anterior MTL regions affiliated with a network of anterior temporal cortical regions, whereas posterior MTL regions affiliated with a network of posterior medial cortical regions. Importantly, these connectivity relationships also predicted similarities among regions during the associative memory task. Both in terms of task-evoked activation and trial-specific information carried in multivoxel patterns, regions within each network were more similar to one another than were regions in different networks. These findings suggest that functional heterogeneity among MTL subregions may be related to their participation in distinct large-scale cortical systems involved in memory. At a more general level, the results suggest that components of neural systems defined on the basis of RSFC share similar functional properties in terms of recruitment during cognitive tasks and information carried in voxel patterns.

Assessing functional connectivity of neural ensembles using directed information

2012 ◽  
Vol 9 (2) ◽  
pp. 026004 ◽  
Author(s):  
Kelvin So ◽  
Aaron C Koralek ◽  
Karunesh Ganguly ◽  
Michael C Gastpar ◽  
Jose M Carmena
Keyword(s):  
Functional Connectivity ◽  
Neural Ensembles ◽  
Directed Information

scholarly journals Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement

2020 ◽  
Author(s):  
Peiyuan Zhou ◽  
Andrew K.C. Wong
Keyword(s):  
Data Analysis ◽  
Clinical Data ◽  
Pattern Discovery ◽  
Synthetic Data ◽  
General Setting ◽  
Clinical Practices ◽  
Class Distribution ◽  
Clinical Data Analysis ◽  
Imbalanced Class ◽  
To Come

Abstract Background Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest and application in clinical practices. First, the interpretability of the diagnostic/prognostic results will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. Furthermore, from the clinical aspect, when the datasets are imbalanced in diagnostic categories, the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it is desirable to have a method that could produce explicit transparent and interpretable results in decision-making, even for data with imbalanced groups.Methods In order to interpret the clinical patterns and conduct diagnostic prediction of patients, we present our new method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small and succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even for small and rare groups.Results Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover fewer patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches.Conclusions In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discover all patterns implanted in the data, display them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel explainable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.

scholarly journals Estimating anisotropy directly via neural timeseries

2021 ◽  
Author(s):  
Erik D. Fagerholm ◽  
W.M.C. Foulkes ◽  
Yasir Gallero-Salas ◽  
Fritjof Helmchen ◽  
Rosalyn J. Moran ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Neural Systems ◽  
Imaging Data ◽  
Individual Organism ◽  
Model Inversion ◽  
Brain States ◽  
Anisotropic Systems ◽  
Cortical Regions ◽  
Insight Into

An isotropic dynamical system is one that looks the same in every direction, i.e., if we imagine standing somewhere within an isotropic system, we would not be able to differentiate between different lines of sight. Conversely, anisotropy is a measure of the extent to which a system deviates from perfect isotropy, with larger values indicating greater discrepancies between the structure of the system along its axes. Here, we derive the form of a generalised scalable (mechanically similar) discretized field theoretic Lagrangian that allows for levels of anisotropy to be directly estimated via timeseries of arbitrary dimensionality. We generate synthetic data for both isotropic and anisotropic systems and, by using Bayesian model inversion and reduction, show that we can discriminate between the two datasets - thereby demonstrating proof of principle. We then apply this methodology to murine calcium imaging data collected in rest and task states, showing that anisotropy can be estimated directly from different brain states and cortical regions in an empirical in vivo biological setting. We hope that this theoretical foundation, together with the methodology and publicly available MATLAB code, will provide an accessible way for researchers to obtain new insight into the structural organization of neural systems in terms of how scalable neural regions grow - both ontogenetically during the development of an individual organism, as well as phylogenetically across species.

scholarly journals Practical Aspects of Phylogenetic Network Analysis Using PhyloNet

2019 ◽  
Author(s):  
Zhen Cao ◽  
Xinhao Liu ◽  
Huw A. Ogilvie ◽  
Zhi Yan ◽  
Luay Nakhleh
Keyword(s):  
Incomplete Lineage Sorting ◽  
Phylogenetic Network ◽  
Synthetic Data ◽  
Simulated Data ◽  
Single Species ◽  
Phylogenetic Networks ◽  
Lineage Sorting ◽  
Data Set ◽  
Types Of Information ◽  
Analyze Data

AbstractPhylogenetic networks extend trees to enable simultaneous modeling of both vertical and horizontal evolutionary processes. PhyloNet is a software package that has been under constant development for over 10 years and includes a wide array of functionalities for inferring and analyzing phylogenetic networks. These functionalities differ in terms of the input data they require, the criteria and models they employ, and the types of information they allow to infer about the networks beyond their topologies. Furthermore, PhyloNet includes functionalities for simulating synthetic data on phylogenetic networks, quantifying the topological differences between phylogenetic networks, and evaluating evolutionary hypotheses given in the form of phylogenetic networks.In this paper, we use a simulated data set to illustrate the use of several of PhyloNet’s functionalities and make recommendations on how to analyze data sets and interpret the results when using these functionalities. All inference methods that we illustrate are incomplete lineage sorting (ILS) aware; that is, they account for the potential of ILS in the data while inferring the phylogenetic network. While the models do not include gene duplication and loss, we discuss how the methods can be used to analyze data in the presence of polyploidy.The concept of species is irrelevant for the computational analyses enabled by PhyloNet in that species-individuals mappings are user-defined. Consequently, none of the functionalities in PhyloNet deals with the task of species delimitation. In this sense, the data being analyzed could come from different individuals within a single species, in which case population structure along with potential gene flow is inferred (assuming the data has sufficient signal), or from different individuals sampled from different species, in which case the species phylogeny is being inferred.

scholarly journals A Longitudinal Model for Functional Connectivity Networks Using Resting-State fMRI

2017 ◽  
Author(s):  
Brian Hart ◽  
Ivor Cribben ◽  
Mark Fiecas ◽  
Keyword(s):  
Functional Connectivity ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Resting State Fmri ◽  
Testing Procedure ◽  
Fmri Data ◽  
Series Data ◽  
Group Differences ◽  
Permutation Testing ◽  
Novel Method

AbstractMany neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current network modeling literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC network model using a variance components approach. First, for all subjects’ visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the FC network, and 3) the FC network’s longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in baseline FC and change in FC over time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer’s Disease Neuroimaging Initiative database.

Sign in / Sign up

Export Citation Format

Share Document