Purpose The need of an automatic reorganization of the massive Resting State (RS) output is impelling due to the spreading of the RS fMRI [1] , as a huge number of RS network is carried out for each subject and only a few of them are interesting. The aim of the study is to use a Machine Learning algorithm to identify four relevant networks (Default Mode, Auditory, Visual, Frontal), developing an automated protocol of network classification. Methods RS analysis has been carried out with CONN software [2] in 21 elderly subjects (11 M–10 F, 73 ± 4 years), acquired in GE 3T scanner with a EPI sequence (TR/TE 2000/30 ms, 3.2 × 3.2 × 3.4 mm3, Volumes 250): the raw fMRI images were spatially and temporally preprocessed with standard analysis steps and subsequently a Independent Component Analysis (ICA) was performed to carry out the RS networks for each subject. The four classes of relevant networks were tested against a class of random chosen networks in PRoNTo software [3] with a multiclass Gaussian Process. After the cross-validation test phase, the weight of single voxels into the classification process was obtained for each network class. Results PRoNTo successfully classified the networks with a permutation test obtaining a total accuracy of 92.38% (p Table 1 . Fig. 1 represents the confusion matrix. Conclusion The Machine Learning algorithm correctly identified four relevant RS networks against random ones, showing the possibility to automatize a protocol of RS network classification. Funding DRINN PROJECT – Diabetes Research Innovation 2015.
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