Abstract #1658

Accuracy and inter-subject reproducibility of default mode networks identified from ASL data

Felipe Barreto^1,2, Xiufeng Li¹, Amir Moheet³, Anjali Kumar³, Lynn Eberly⁴, Elizabeth Seaquist³, Fabrizio Esposito⁵, and Silvia Mangia¹

¹CMRR, Department of Radiology, University of Minnesota, Minneapolis, MN, United States, ²Department of Physics, University of Sao Paulo, Ribeirao Preto, Brazil, ³Department of Medicine, University of Minnesota, Minneapolis, MN, United States, ⁴Division of Biostatistics, University of Minnesota, Minneapolis, MN, United States, ⁵Department of Medicine and Surgery, University of Salerno, Baronissi, Italy

The present study aimed at characterizing the robustness of the default mode network (DMN) extracted at single subject level from ASL datasets with independent component analysis. Three different analyses modes were considered, including the series of perfusion weighted images, the full time series, and the pair-wise average of control/tag images (pseudo-BOLD). Results show that the three analysis modes produce DMNs with similar accuracy at a group level, but the pseudo-BOLD mode resulted in smaller inter-subject variability of the spatial distribution of the single-subject DMNs.

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