Abstract
A proper analysis and interpretation of resting-state function Magnetic Resonance Imaging (rs-fMRI) signals is highly dependent upon the capability of discriminating signal from noise. From among the many non-system-related types of noise, the physiological noise and noise related to motion are the most relevant ones. This paper introduces two scores that allow to cluster images so as to identify those with high level motion scores. To do so, we collect post-mortem and in-vivo rs-fMRI signals from 123 individuals, classify them according to motion indicators, and cluster them into groups that somehow establish the level of noise on which they rely. Data was obtained using a 7Tesla rs-MRI system at the Faculty of Medicine of the São Paulo University were captured. From these signals, time series were generated and used to create a nearest-neighbor model capable of scoring the rs-fMRI signals in terms of their motion noise level. The results showed that even in the ideal situation of patient’s zero motion, rs-fMRI signal still displays motion induced noise. A clustering over the patients’ score allowed the identification of cluster of increasing motion-level signals.
Supported by FAPESP and CNPq.
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Pasti, R., Chaim, K.T., Otaduy, M.C.G., de Faria, P.M., Biczyk, M., de Castro, L.N. (2023). Motion Induced Scores for 7Tesla rs-fMRI with Post-Mortem Data as Reference. In: Omatu, S., Mehmood, R., Sitek, P., Cicerone, S., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-031-20859-1_23
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