Basic and Clinical Neuroscience Journal
مجله علوم اعصاب پایه و بالینی
BCN
Medical Sciences
http://bcn.iums.ac.ir
137
journal137
2008-126X
2228-7442
10.32598/bcn
en
jalali
1397
12
1
gregorian
2019
3
1
10
2
online
1
fulltext
en
Prediction of Brain Connectivity Map in Resting-State fMRI Data Using Shrinkage Estimator
Computational Neuroscience
Computational Neuroscience
Original
Original
<div style="text-align: justify;"><strong>Introduction:</strong> In recent years, brain functional connectivity studies are extended using the advanced statistical methods. Functional connectivity is identified by synchronous activation in a spatially distinct region of the brain in resting-state functional Magnetic Resonance Imaging (MRI) data. For this purpose there are several methods such as seed-based correlation analysis based on temporal correlation between different Regions of Interests (ROIs) or between brain’s voxels of prior seed.<br>
<strong>Methods: </strong>In the current study, test-retest Resting State functional MRI (rs-fMRI) data of 21 healthy subjects were analyzed to predict second replication connectivity map using first replication data. A potential estimator is “raw estimator” that uses the first replication data from each subject to predict the second replication connectivity map of the same subject. The second estimator, “mean estimator” uses the average of all sample subjects' connectivity to estimate the correlation map. Shrinkage estimator is made by shrinking raw estimator towards the average connectivity map of all subjects' first replicate. Prediction performance of the second replication correlation map is evaluated by Mean Squared Error (MSE) criteria.<br>
<strong>Results:</strong> By the employment of seed-based correlation analysis and choosing precentral gyrus as the ROI over 21 subjects in the study, on average MSE for raw, mean and shrinkage estimator were 0.2169, 0.1118, and 0.1103, respectively. Also, percent reduction of MSE for shrinkage and mean estimator in comparison with raw estimator is 49.14 and 48.45, respectively.<br>
<strong>Conclusion: </strong>Shrinkage approach has the positive effect on the prediction of functional connectivity. When data has a large between session variability, prediction of connectivity map can be improved by shrinking towards population mean. </div>
Resting-State fMRI, Functional connectivity, Shrinkage estimator, Mean Squared Error, Seed-based correlation analysis
147
156
http://bcn.iums.ac.ir/browse.php?a_code=A-10-1232-1&slc_lang=en&sid=1
Atiye
Nazari
atiyenazari@yahoo.com
13700319475328460019553
13700319475328460019553
No
Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hamid
Alavimajd
alavimajd@sbmu.ac.ir
13700319475328460019554
13700319475328460019554
Yes
Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Nezhat
Shakeri
13700319475328460019555
13700319475328460019555
No
Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohsen
Bakhshandeh
13700319475328460019556
13700319475328460019556
No
Department of Radiology Technology, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Elham
Faghihzadeh
13700319475328460019557
13700319475328460019557
No
Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hengameh
Marzbani
13700319475328460019558
13700319475328460019558
No
Department of Biomedical Engineering and Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.