Browsing by Author "Koçak, O. M."

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Open Access
Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining
(Elsevier, 2018) Aljobouri, H. K.; Jaber, H. A.; Koçak, O. M.; Algin, O.; Çankaya, I.
Background: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algorithms remains a problem. New method: A novel application of the robust unsupervised learning approach is proposed in the current study. Robust growing neural gas (RGNG) algorithm was fed into fMRI data and compared with growing neural gas (GNG) algorithm, which has not been used for this purpose or any other medical application. Learning algorithms proposed in the current study are fed with real and free auditory fMRI datasets. Results: The fMRI result obtained by running RGNG was within the expected outcome and is similar to those found with the hypothesis method in detecting active areas within the expected auditory cortices. Comparison with existing method(s): The fMRI application of the presented RGNG approach is clearly superior to other approaches in terms of its insensitivity to different initializations and the presence of outliers, as well as its ability to determine the actual number of clusters successfully, as indicated by its performance measured by minimum description length (MDL) and receiver operating characteristic (ROC) analysis. Conclusions: The RGNG can detect the active zones in the brain, analyze brain function, and determine the optimal number of underlying clusters in fMRI datasets. This algorithm can define the positions of the center of an output cluster corresponding to the minimal MDL value.
Open Access
Optimal model-free approach based on MDL and CHL for active brain identification in fMRI data analysis
(Bentham Science Publishers Ltd., 2020-07-30) Jaber, H. A.; Çankaya, I.; Aljobouri, H. K.; Koçak, O. M.; Algın, Oktay
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.
Open Access
Preparing fMRI data for postprocessing: conversion modalities, preprocessing pipeline, and parametric and nonparametric approaches
(Institute of Electrical and Electronics Engineers Inc., 2019) Jaber, Hussain A.; Aljobouri, H. K.; Çankaya, İ.; Koçak, O. M.; Algın, Oktay
The complexity of raw functional magnetic resonance imaging (fMRI) data with artifacts leads to significant challenges in multioperations with these data. FMRI data analysis is extensively used in neuroimaging fields, but the tools for processing fMRI data are lacking. A novel APP DESIGNER conversion, preprocessing, and postprocessing of fMRI (CPREPP fMRI) tool is proposed and developed in this work. This toolbox is intended for pipeline fMRI data analysis, including full analysis of fMRI data, starting from DICOM conversion, then checking the quality of data at each step, and ending in postprocessing analysis. The CPREPP fMRI tool includes 12 conversions of scientific processes that reflect all conversion possibilities among them. In addition, specific preprocessing order steps are proposed on the basis of data acquisition mode (interleaved and sequential modes). A severe and crucial comparison between statistical parametric and nonparametric mapping approaches of second-level analysis is presented in the same tool. The CPREPP fMRI tool can provide reports to exclude subjects with the extreme movement of the head during the scan, and a range of fMRI images are generated to verify the normalization effect easily. Real fMRI data are used in this work to prepare fMRI data tests. The experiment stimuli are chewing and biting, and the data are acquired from the National Magnetic Resonance Research (UMRAM) Center in Ankara, Turkey. A free dataset is used to compare the methods for postprocessing fMRI tests.