scholarly journals BayesFactorFMRI: Implementing Bayesian second-level fMRI analysis with multiple comparison correction and Bayesian meta-analysis of fMRI images with multiprocessing

2020 ◽  
Author(s):  
Hyemin Han
Keyword(s):  
Bayesian Analysis ◽  
Meta Analysis ◽  
Image Data ◽  
Fmri Data ◽  
Classical Analysis ◽  
Multiple Processors ◽  
Computationally Intensive ◽  
Fmri Analysis ◽  
Multiple Comparison Correction ◽  
Level Analysis

AbstractBayesFactorFMRI is a tool developed with R and Python to allow neuroimaging researchers to conduct Bayesian second-level analysis and Bayesian meta-analysis of fMRI image data with multiprocessing. This tool expedites computationally intensive Bayesian fMRI analysis through multiprocessing. Its GUI allows researchers who are not experts in computer programming to feasibly perform Bayesian fMRI analysis. BayesFactorFMRI is available via Zenodo and GitHub for download. It would be widely reused by neuroimaging researchers who intend to analyse their fMRI data with Bayesian analysis with better sensitivity compared with classical analysis while improving performance by distributing analysis tasks into multiple processors.

scholarly journals Implementation of Bayesian Multiple Comparison Correction in the Second-Level Analysis of fMRI Data: With Pilot Analyses of Simulation and Real fMRI Datasets Based on Voxelwise Inference

2019 ◽  
Author(s):  
Hyemin Han
Keyword(s):  
False Alarm ◽  
Correction Method ◽  
Multiple Comparison ◽  
Real Image ◽  
Classical Inference ◽  
Fmri Analysis ◽  
Simulated Images ◽  
Multiple Comparison Correction ◽  
Level Analysis ◽  
Image Datasets

AbstractWe developed and tested Bayesian multiple comparison correction method for Bayesian voxelwise second-level fMRI analysis with R. The performance of the developed method was tested with simulation and real image datasets. First, we compared false alarm and hit rates, which were used as proxies for selectivity and sensitivity, respectively, between Bayesian and classical inference were conducted. For the comparison, we created simulated images, added noise to the created images, and analyzed the noise-added images while applying Bayesian and classical multiple comparison correction methods. Second, we analyzed five real image datasets to examine how our Bayesian method worked in realistic settings. When the performance assessment was conducted, Bayesian correction method demonstrated good sensitivity (hit rate ≥ 75%) and acceptable selectivity (false alarm rate < 10%) when N ≤ 8. Furthermore, Bayesian correction method showed better sensitivity compared with classical correction method while maintaining the aforementioned acceptable selectivity.

scholarly journals Non-selective response inhibition in Go/NoGo task: Bayesian analysis of fMRI data

2019 ◽  
Author(s):  
Ruslan Masharipov ◽  
Maxim Kireev ◽  
Alexander Korotkov ◽  
Svyatoslav Medvedev
Keyword(s):  
Bayesian Analysis ◽  
Neuronal Activity ◽  
Response Inhibition ◽  
Null Hypothesis ◽  
Meta Analysis ◽  
Equal Probability ◽  
Fmri Data ◽  
List Type ◽  
Significant Difference ◽  
Selective Mechanism

AbstractResponse inhibition is typically considered a selective mechanism triggered by particular “inhibitory” stimuli or events. Based on recent research, an alternative non-selective mechanism was proposed by several authors. Presumably, the mechanism may involve non-selective inhibition of all potential responses to allow execution of an appropriate response, and such inhibition may be triggered not only by the presentation of “inhibitory” stimuli but also by any imperative stimuli, including Go stimuli. Earlier support for this notion was mainly based on the absence of a significant difference between neural activity evoked by equiprobable Go and NoGo stimuli. Previous neuroimaging studies utilized classical null hypothesis significance testing, which makes it impossible to accept the null hypothesis. Therefore, the current research aimed to provide evidence for practical equivalence of neuronal activity in Go and NoGo trials using Bayesian analysis of functional magnetic resonance imaging (fMRI) data. Twenty healthy participants performed a cued Go/NoGo task with an equiprobable presentation of Go and NoGo stimuli. To reveal brain areas previously associated with response inhibition in similar experimental conditions, we performed a meta-analysis of fMRI studies using equal probability Go/NoGo tasks. As a result, we observed overlap between response inhibition areas and areas demonstrating practical equivalence of neuronal activity located in the right dorsolateral prefrontal cortex, inferior parietal lobule, premotor cortex, and left inferior frontal gyrus. Thus, obtained results favour the existence of non-selective response inhibition, which can act in settings of context uncertainty, modelled by the equal probability of Go and NoGo stimuli.HighlightsNon-selective response inhibition was assessed by equiprobable Go/NoGo taskBayesian analysis of fMRI data was combined with a meta-analysis of fMRI studiesNodes of response-inhibition system were equally involved in Go and NoGo trialsEvidence for non-selective response inhibition in uncertainty context was found

scholarly journals Validation of open-source science tools and background model construction in γ-ray astronomy

2019 ◽  
Vol 632 ◽  
pp. A72
Author(s):  
L. Mohrmann ◽  
A. Specovius ◽  
D. Tiziani ◽  
S. Funk ◽  
D. Malyshev ◽  
...  
Keyword(s):  
Cosmic Ray ◽  
Field Of View ◽  
Background Model ◽  
Analysis Approach ◽  
Source Analysis ◽  
Classical Analysis ◽  
Background Estimation ◽  
Likelihood Analysis ◽  
High Level ◽  
Level Analysis

In classical analyses of γ-ray data from imaging atmospheric Cherenkov telescopes (IACTs), such as the High Energy Stereoscopic System (H.E.S.S.), aperture photometry, or photon counting, is applied in a (typically circular) region of interest (RoI) encompassing the source. A key element in the analysis is to estimate the amount of background in the RoI due to residual cosmic ray-induced air showers in the data. Various standard background estimation techniques have been developed in the last decades, most of them rely on a measurement of the background from source-free regions within the observed field of view. However, in particular in the Galactic plane, source analysis and background estimation are hampered by the large number of, sometimes overlapping, γ-ray sources and large-scale diffuse γ-ray emission. For complicated fields of view, a three-dimensional (3D) likelihood analysis shows the potential to be superior to classical analysis. In this analysis technique, a spectromorphological model, consisting of one or multiple source components and a background component, is fitted to the data, resulting in a complete spectral and spatial description of the field of view. For the application to IACT data, the major challenge of such an approach is the construction of a robust background model. In this work, we apply the 3D likelihood analysis to various test data recently made public by the H.E.S.S. collaboration, using the open analysis frameworks ctools and Gammapy. First, we show that, when using these tools in a classical analysis approach and comparing to the proprietary H.E.S.S. analysis framework, virtually identical high-level analysis results, such as field-of-view maps and spectra, are obtained. We then describe the construction of a generic background model from data of H.E.S.S. observations, and demonstrate that a 3D likelihood analysis using this background model yields high-level analysis results that are highly compatible with those obtained from the classical analyses. This validation of the 3D likelihood analysis approach on experimental data is an important step towards using this method for IACT data analysis, and in particular for the analysis of data from the upcoming Cherenkov Telescope Array (CTA).

scholarly journals How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging

2016 ◽  
Author(s):  
Joram Soch ◽  
Achim Pascal Meyer ◽  
John-Dylan Haynes ◽  
Carsten Allefeld
Keyword(s):  
Data Analysis ◽  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bayesian Model Selection ◽  
Fmri Data ◽  
Parameter Estimates ◽  
Fmri Data Analysis ◽  
Level Analysis

AbstractIn functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: “How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection”, NeuroImage, vol. 141, pp. 469-489; DOI: 10.1016/j. neuroimage.2016.07.047), we have introduced cross-validated Bayesian model selection (cvBMS) to infer the best model for a group of subjects and use it to guide second-level analysis. While this is the optimal approach given that the same GLM has to be used for all subjects, there is a much more efficient procedure when model selection only addresses nuisance variables and regressors of interest are included in all candidate models. In this work, we propose cross-validated Bayesian model averaging (cvBMA) to improve parameter estimates for these regressors of interest by combining information from all models using their posterior probabilities. This is particularly useful as different models can lead to different conclusions regarding experimental effects and the most complex model is not necessarily the best choice. We find that cvBMS can prevent not detecting established effects and that cvBMA can be more sensitive to experimental effects than just using even the best model in each subject or the model which is best in a group of subjects.

Redeployed functions versus spreading activation: A potential confound

2010 ◽  
Vol 33 (4) ◽  
pp. 280-281 ◽  
Author(s):  
Colin Klein
Keyword(s):  
Information Processing ◽  
Meta Analysis ◽  
Fmri Data ◽  
Functional Networks ◽  
Spreading Activation ◽  
Adaptation Studies ◽  
Per Se ◽  
Fmri Adaptation ◽  
Potential Confound

AbstractAnderson's meta-analysis of fMRI data is subject to a potential confound. Areas identified as active may make no functional contribution to the task being studied, or may indicate regions involved in the coordination of functional networks rather than information processing per se. I suggest a way in which fMRI adaptation studies might provide a useful test between these alternatives.

scholarly journals Multivariate group-level analysis for task fMRI data with canonical correlation analysis

NeuroImage ◽  
2019 ◽  
Vol 194 ◽  
pp. 25-41 ◽  
Author(s):  
Xiaowei Zhuang ◽  
Zhengshi Yang ◽  
Karthik R. Sreenivasan ◽  
Virendra R. Mishra ◽  
Tim Curran ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Fmri Data ◽  
Group Level ◽  
Level Analysis
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