Abstract
The statistical analysis of functional magnetic resonance imaging (fMRI) is used to extract functional data of cerebral activation during a given experimental task. It allows for assessing changes in cerebral function related to cerebral activities. This methodology has been widely used and a few initiatives aim to develop shared data resources. Searching these data resources for a specific research goal remains a challenging problem. In particular, work is needed to create a global content–based (CB) fMRI retrieval capability.
This work presents a CB fMRI retrieval approach based on the brain activation maps extracted using Probabilistic Independent Component Analysis (PICA). We obtained promising results on data from a variety of experiments which highlight the potential of the system as a tool that provides support for finding hidden similarities between brain activation maps.
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The Montreal Neurological Institute (MNI) defined a standard brain, which is representative of the population, by using a large series of MRI scans on normal controls.
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Acknowledgments
This research was supported by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM), and Lister Hill National Center for Biomedical Communications (LHNCBC). Thanks to Dr. Bing Bai and Prof. Paul Kantor for providing us the fMRI experimental data.
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G. Seco de Herrera, A., Long, L.R., Antani, S. (2016). Content-Based fMRI Brain Maps Retrieval. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_17
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