Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

A FREQUENCY SIGNAL METHOD FOR fMRI DATA ANALYSIS

2010 ◽  
Vol 22 (05) ◽  
pp. 377-383
Author(s):  
Jiang Zhang ◽  
Huafu Chen ◽  
Fang Fang ◽  
Hualin Liu ◽  
Wei Liao
Keyword(s):  
Data Analysis ◽  
Statistical Parametric Mapping ◽  
Main Idea ◽  
Roc Curves ◽  
Fmri Data ◽  
Support Vector ◽  
Frequency Information ◽  
Processing Strategies ◽  
Fmri Data Analysis

Currently, all the data processing strategies for functional magnetic resonance imaging (fMRI) utilize temporal informationpaying little attention to or totally ignoring frequency information. In this paper, a new method is proposed to detect the functional activation regions in the brain by using the frequency information of fMRI time series. The main idea is that the frequency entropy information (FEI) difference of fMRI data between task and control states is specified as brain activation index. The validity of the proposed FEI approach is confirmed by analyzing the result of the simulated synthesized data. Additionally, the comparison of receiver operating characteristic (ROC) curves acquired respectively from the proposed scheme, the statistical parametric mapping (SPM), and the Support Vector Machine (SVM) methods of fMRI data analysis indicate an obvious superiority of the former. In vivo fMRI studies of subjects with event-related experiment reveal that FEI method can enable the effective detection of brain functional activation.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

A Survey of FMRI Data Analysis Methods

2018 ◽  
Author(s):  
Anahid Ehtemami ◽  
Rollin Scott ◽  
Shonda Bernadin
Keyword(s):  
Data Analysis ◽  
Fmri Data ◽  
Analysis Methods ◽  
Fmri Data Analysis ◽  
Data Analysis Methods

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.

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