Abstract
The analysis of resting-state brain connectivity allows unraveling the fundamentals of functional brain organization. Especially changes of network connectivity related to age or diseases promise to serve as early biomarkers. After control of subject movement, we found that, when reaching a critical number of subjects, age prediction is reproducible for all seed selection strategies tested here (functional, anatomical and random based seeds). On the Enhanced Rockland Community Sample, we use support vector regression (SVR) and intense permutation testing for statistical validation.
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Scheel, N., Essenwanger, A., Münte, T., Heldmann, M., Krämer, U., Mamlouk, A. (2015). Selection of Seeds for Resting-State fMRI-Based Prediction of Individual Brain Maturity. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46224-9_64
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DOI: https://doi.org/10.1007/978-3-662-46224-9_64
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