scholarly journals Intersubject MVPD: Empirical Comparison of fMRI Denoising Methods for Connectivity Analysis

2018 ◽  
Author(s):  
Yichen Li ◽  
Rebecca Saxe ◽  
Stefano Anzellotti
Keyword(s):  
Brain Region ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Brain Regions ◽  
Fmri Data ◽  
Statistical Dependence ◽  
The Real ◽  
The Difference ◽  
Multivariate Pattern

AbstractNoise is a major challenge for the analysis of fMRI data in general and for connectivity analyses in particular. As researchers develop increasingly sophisticated tools to model statistical dependence between the fMRI signal in different brain regions, there is a risk that these models may increasingly capture artifactual relationships between regions, that are the result of noise. Thus, choosing optimal denoising methods is a crucial step to maximize the accuracy and reproducibility of connectivity models. Most comparisons between denoising methods require knowledge of the ground truth: of what is the ‘real signal’. For this reason, they are usually based on simulated fMRI data. However, simulated data may not match the statistical properties of real data, limiting the generalizability of the conclusions. In this article, we propose an approach to evaluate denoising methods using real (non-simulated) fMRI data. First, we introduce an intersubject version of multivariate pattern dependence (iMVPD) that computes the statistical dependence between a brain region in one participant, and another brain region in a different participant. iMVPD has the following advantages: 1) it is multivariate, 2) it trains and tests models on independent folds of the real fMRI data, and 3) it generates predictions that are both between subjects and between regions. Since whole-brain sources of noise are more strongly correlated within subject than between subjects, we can use the difference between standard MVPD and iMVPD as a ‘discrepancy metric’ to evaluate denoising techniques (where more effective techniques should yield smaller differences). As predicted, the difference is the greatest in the absence of denoising methods. Furthermore, a combination of removal of the global signal and CompCorr optimizes denoising (among the set of denoising options tested).

scholarly journals Evaluating the reproducibility of single-cell gene regulatory network inference algorithms

2020 ◽  
Author(s):  
Yoonjee Kang ◽  
Denis Thieffry ◽  
Laura Cantini
Keyword(s):  
Single Cell ◽  
Network Inference ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Gene Regulatory Network Inference ◽  
Sequencing Platform ◽  
Cell Network ◽  
Inference Algorithms ◽  
Inference Methods

AbstractNetworks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth.Here, we benchmark four single-cell network inference methods based on their reproducibility, i.e. their ability to infer similar networks when applied to two independent datasets for the same biological condition. We tested each of these methods on real data from three biological conditions: human retina, T-cells in colorectal cancer, and human hematopoiesis.GENIE3 results to be the most reproducible algorithm, independently from the single-cell sequencing platform, the cell type annotation system, the number of cells constituting the dataset, or the thresholding applied to the links of the inferred networks. In order to ensure the reproducibility and ease extensions of this benchmark study, we implemented all the analyses in scNET, a Jupyter notebook available at https://github.com/ComputationalSystemsBiology/scNET.

scholarly journals Cycle-CNN for Colorization towards Real Monochrome-Color Camera Systems

2020 ◽  
Vol 34 (07) ◽  
pp. 10721-10728
Author(s):  
Xuan Dong ◽  
Weixin Li ◽  
Xiaojie Wang ◽  
Yunhong Wang
Keyword(s):  
Color Image ◽  
Ground Truth ◽  
Real Data ◽  
Color Information ◽  
The Real ◽  
Gray Image ◽  
Camera Systems ◽  
Structure Similarity ◽  
First Time ◽  
Color Map

Colorization in monochrome-color camera systems aims to colorize the gray image IG from the monochrome camera using the color image RC from the color camera as reference. Since monochrome cameras have better imaging quality than color cameras, the colorization can help obtain higher quality color images. Related learning based methods usually simulate the monochrome-color camera systems to generate the synthesized data for training, due to the lack of ground-truth color information of the gray image in the real data. However, the methods that are trained relying on the synthesized data may get poor results when colorizing real data, because the synthesized data may deviate from the real data. We present a new CNN model, named cycle CNN, which can directly use the real data from monochrome-color camera systems for training. In detail, we use the colorization CNN model to do the colorization twice. First, we colorize IG using RC as reference to obtain the first-time colorization result IC. Second, we colorize the de-colored map of RC, i.e. RG, using the first-time colorization result IC as reference to obtain the second-time colorization result R′C. In this way, for the second-time colorization result R′C, we use the original color map RC as ground-truth and introduce the cycle consistency loss to push R′C ≈ RC. Also, for the first-time colorization result IC, we propose a structure similarity loss to encourage the luminance maps between IG and IC to have similar structures. In addition, we introduce a spatial smoothness loss within the colorization CNN model to encourage spatial smoothness of the colorization result. Combining all these losses, we could train the colorization CNN model using the real data in the absence of the ground-truth color information of IG. Experimental results show that we can outperform related methods largely for colorizing real data.

scholarly journals A Framework for the Objective Assessment of Registration Accuracy

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Francesca Pizzorni Ferrarese ◽  
Flavio Simonetti ◽  
Roberto Israel Foroni ◽  
Gloria Menegaz
Keyword(s):  
Accuracy Assessment ◽  
Objective Assessment ◽  
Synthetic Data ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Magnetic Resonance Images ◽  
Good Prediction ◽  
Registration Accuracy ◽  
Affine Registration

Validation and accuracy assessment are the main bottlenecks preventing the adoption of image processing algorithms in the clinical practice. In the classical approach, a posteriori analysis is performed through objective metrics. In this work, a different approach based on Petri nets is proposed. The basic idea consists in predicting the accuracy of a given pipeline based on the identification and characterization of the sources of inaccuracy. The concept is demonstrated on a case study: intrasubject rigid and affine registration of magnetic resonance images. Both synthetic and real data are considered. While synthetic data allow the benchmarking of the performance with respect to the ground truth, real data enable to assess the robustness of the methodology in real contexts as well as to determine the suitability of the use of synthetic data in the training phase. Results revealed a higher correlation and a lower dispersion among the metrics for simulated data, while the opposite trend was observed for pathologic ones. Results show that the proposed model not only provides a good prediction performance but also leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to different and more complex scenarios.

scholarly journals Computational Fingerprints: Modeling Interactions Between Brain Regions as Points in a Function Space

2021 ◽  
Author(s):  
Craig Poskanzer ◽  
Stefano Anzellotti
Keyword(s):  
Function Space ◽  
Hermite Polynomials ◽  
Ground Truth ◽  
Brain Regions ◽  
Basis Functions ◽  
Statistical Dependence ◽  
Seed Region ◽  
Nonlinear Interactions ◽  
Brain Areas ◽  
Face Area

In this paper we propose a novel technique to investigate the nonlinear interactions between brain regions that captures both the strength and the type of the functional relationship. Inspired by the field of functional analysis, we propose that the relationship between activity in two different brain areas can be viewed as a point in function space, identified by coordinates along an infinite set of basis functions. Using Hermite Polynomials as basis functions, we estimate from fMRI data a truncated set of coordinates that serve as a "computational fingerprint," characterizing the interaction between two brain areas. We provide a proof of the convergence of the estimates in the limit, and we validate the method with simulations in which the ground truth is known, additionally showing that computational fingerprints detect statistical dependence also when correlations ("functional connectivity") is near zero. We then use computational fingerprints to examine the neural interactions with a seed region of choice: the Fusiform Face Area (FFA). Using k-means clustering across each voxel's computational fingerprint, we illustrate that the addition of the nonlinear basis functions allows for the discrimination of inter-regional interactions that are otherwise grouped together when only linear dependence is used. Finally, we show that regions in V5 and medial occipital and temporal lobes exhibit significant nonlinear interactions with the FFA.

scholarly journals On co-activation pattern analysis and non-stationarity of resting brain activity

2021 ◽  
Author(s):  
Teppei Matsui ◽  
Trung Quang Pham ◽  
Koji Jimura ◽  
Junichi Chikazoe
Keyword(s):  
Pattern Analysis ◽  
Brain Activity ◽  
Simulated Data ◽  
Real Data ◽  
Functional Magnetic Resonance ◽  
Care Needs ◽  
Sliding Windows ◽  
The Real ◽  
Co Activation ◽  
Cap Analysis

AbstractThe non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the non-stationary characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, whether these techniques adequately capture non-stationarity needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity.

scholarly journals Computational approaches to the neuroscience of social perception

2020 ◽  
Author(s):  
Jeffrey A Brooks ◽  
Ryan M Stolier ◽  
Jonathan B Freeman
Keyword(s):  
Social Perception ◽  
Impression Formation ◽  
Social Information ◽  
Brain Regions ◽  
Neural Representation ◽  
Fmri Data ◽  
Psychological Models ◽  
Fmri Analysis ◽  
Multivariate Pattern ◽  
Multiple Domains

Abstract Across multiple domains of social perception - including social categorization, emotion perception, impression formation, and mentalizing - multivariate pattern analysis (MVPA) of fMRI data has permitted a more detailed understanding of how social information is processed and represented in the brain. As in other neuroimaging fields, the neuroscientific study of social perception initially relied on broad structure-function associations derived from univariate fMRI analysis to map neural regions involved in these processes. In this review, we trace the ways that social neuroscience studies using MVPA have built on these neuroanatomical associations to better characterize the computational relevance of different brain regions, and how MVPA allows explicit tests of the correspondence between psychological models and the neural representation of social information. We also describe current and future advances in methodological approaches to multivariate fMRI data and their theoretical value for the neuroscience of social perception.

scholarly journals Simulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling

2020 ◽  
Vol 12 (5) ◽  
pp. 771 ◽  
Author(s):  
Miguel Angel Ortíz-Barrios ◽  
Ian Cleland ◽  
Chris Nugent ◽  
Pablo Pancardo ◽  
Eric Järpe ◽  
...  
Keyword(s):  
Poisson Regression ◽  
Smart Home ◽  
Real Life ◽  
Synthetic Data ◽  
Simulated Data ◽  
Real Data ◽  
The Real ◽  
Intrinsic Nature ◽  
Simulation Tools ◽  
Effective Care

Automatic detection and recognition of Activities of Daily Living (ADL) are crucial for providing effective care to frail older adults living alone. A step forward in addressing this challenge is the deployment of smart home sensors capturing the intrinsic nature of ADLs performed by these people. As the real-life scenario is characterized by a comprehensive range of ADLs and smart home layouts, deviations are expected in the number of sensor events per activity (SEPA), a variable often used for training activity recognition models. Such models, however, rely on the availability of suitable and representative data collection and is habitually expensive and resource-intensive. Simulation tools are an alternative for tackling these barriers; nonetheless, an ongoing challenge is their ability to generate synthetic data representing the real SEPA. Hence, this paper proposes the use of Poisson regression modelling for transforming simulated data in a better approximation of real SEPA. First, synthetic and real data were compared to verify the equivalence hypothesis. Then, several Poisson regression models were formulated for estimating real SEPA using simulated data. The outcomes revealed that real SEPA can be better approximated ( R pred 2 = 92.72 % ) if synthetic data is post-processed through Poisson regression incorporating dummy variables.

scholarly journals Possible seismo-ionosphere perturbations revealed by VLF signals collected on ground and on a satellite

2007 ◽  
Vol 7 (5) ◽  
pp. 617-624 ◽  
Author(s):  
A. Rozhnoi ◽  
O. Molchanov ◽  
M. Solovieva ◽  
V. Gladyshev ◽  
O. Akentieva ◽  
...  
Keyword(s):  
Satellite Data ◽  
Seismic Activity ◽  
Real Data ◽  
Magnetic Activity ◽  
Signal Distribution ◽  
The Real ◽  
The Third ◽  
Polynomial Expression ◽  
The Difference ◽  
Fresnel Zones

Abstract. The results of the monitoring of three VLF/LF signals collected in Petropavlovsk station (Kamchatka, Russia) and one VLF signal collected on board of the DEMETER French satellite are presented. Two periods of the seismic activity occurred in the Japan-Kamchatka area during November–December 2004 and July–September 2005 were investigated and the earthquakes with M≥6.0 in the Japan-Kamchatka area, located inside one or more of the third Fresnel zones of the three radio paths were considered. The ground data were analysed using residual signal of phase dP or of amplitude dA, defined as the difference between the signal and the average of few quiet days (±5 days) immediately preceding or following the current day. Also the satellite data were processed by a method based on the difference between the real signal and the reference one, but in order to obtain this last signal it was necessary to construct previously a model of the signal distribution over the selected area. The method consists: (a) in averaging all the data available in the considered region over a period characterized by low level seismicity, regardless of the global disturbances, in particular, of the magnetic activity; (b) in computing a polynomial expression for the surface as a function of the longitude and the latitude. The model well describes the real data in condition of their completeness and in absence of magnetic storms or seismic forcing. In the quoted periods of seismic activity clear anomalies both in the ground and in satellite data were revealed. The influence of the geomagnetic activity cannot to be excluded, but the seismic forcing seems more probable.

scholarly journals Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 716
Author(s):  
Yunhe Liu ◽  
Aoshen Wu ◽  
Xueqing Peng ◽  
Xiaona Liu ◽  
Gang Liu ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Transcriptome Profiling ◽  
Actual Data ◽  
Generation Process ◽  
Data Generation ◽  
The Matrix ◽  
The Impact

Despite the scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown “true” clusters. Referencing the transcriptomic heterogeneity of cell clusters, a “true” mRNA number matrix of cell individuals was defined as ground truth. Based on the matrix and the actual data generation procedure, a simulation program (SSCRNA) for raw data was developed. Subsequently, the consistency between simulated data and real data was evaluated. Furthermore, the impact of sequencing depth and algorithms for analyses on cluster accuracy was quantified. As a result, the simulation result was highly consistent with that of the actual data. Among the clustering algorithms, the Gaussian normalization method was the more recommended. As for the clustering algorithms, the K-means clustering method was more stable than K-means plus Louvain clustering. In conclusion, the scRNA simulation algorithm developed restores the actual data generation process, discovers the impact of parameters on classification, compares the normalization/clustering algorithms, and provides novel insight into scRNA analyses.

scholarly journals A case study of 2019-nCOV cases in Argentina with the real data based on daily cases from March 03, 2020 to March 29, 2021 using classical and fractional derivatives

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Pushpendra Kumar ◽  
Vedat Suat Erturk ◽  
Marina Murillo-Arcila ◽  
Ramashis Banerjee ◽  
A. Manickam
Keyword(s):  
Fractional Order ◽  
Trust Region ◽  
Simulated Data ◽  
Real Data ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Order Model ◽  
The Real ◽  
The Stability ◽  
Parameter Values

AbstractIn this study, our aim is to explore the dynamics of COVID-19 or 2019-nCOV in Argentina considering the parameter values based on the real data of this virus from March 03, 2020 to March 29, 2021 which is a data range of more than one complete year. We propose a Atangana–Baleanu type fractional-order model and simulate it by using predictor–corrector (P-C) method. First we introduce the biological nature of this virus in theoretical way and then formulate a mathematical model to define its dynamics. We use a well-known effective optimization scheme based on the renowned trust-region-reflective (TRR) method to perform the model calibration. We have plotted the real cases of COVID-19 and compared our integer-order model with the simulated data along with the calculation of basic reproductive number. Concerning fractional-order simulations, first we prove the existence and uniqueness of solution and then write the solution along with the stability of the given P-C method. A number of graphs at various fractional-order values are simulated to predict the future dynamics of the virus in Argentina which is the main contribution of this paper.

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