Abstract
In the past decade, there has been an exponential growth in brain mapping studies using Functional MRI (fMRI). The need for more data to increase the statistical power of brain mapping studies made the researchers look at multi-center studies. But a major limitation in pooling data from multiple sites is the diversity in acquisition and analysis methods that effect the imaging results. This preliminary study aims at finding correcting functions to reduce the variability of activation maps produced using fMRI for a particular task at different sites. Having explored corrections based on ordinary least squares (OLS) and robust estimation methods, we found that even simple linear correction functions produced reasonable reduction in variability of activation maps across sites.
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Acknowledgments
Some of the data used in this study was acquired through and provided by the Biomedical Informatics Research Network under the following support: U24-RR021992, Function BIRN and U24 GM104203, Bio-Informatics Research Network Coordinating Center (BIRN-CC).
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Thomas, A.J., Bathula, D. (2016). Reducing Inter-Scanner Variability in Multi-site fMRI Activations Using Correction Functions: A Preliminary Study. In: Singh, R., Vatsa, M., Majumdar, A., Kumar, A. (eds) Machine Intelligence and Signal Processing. Advances in Intelligent Systems and Computing, vol 390. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2625-3_10
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DOI: https://doi.org/10.1007/978-81-322-2625-3_10
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