Abstract #3480

REPRODUCIBILITY OF GRAPH METRICS ESTIMATED FROM ALTERNATIVE STRATEGIES OF NETWORK WEIGHTING:EVIDENCES FROM REPEAT MRI SCANS

STAVROS I DIMITRIADIS^1,2, MARK DRAKESMITH², SONYA BELLS³, GREG PARKER³, DAVID LINDEN³, and DEREK K JONES⁴

¹SCHOOL OF MEDICINE, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicin, CARDIFF, United Kingdom, ²SCHOOL OF MEDICINE, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, CARDIFF, United Kingdom, ³SCHOOL OF PSYCHOLOGY, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, CARDIFF, United Kingdom, ⁴SCHOOL OF PSYCHOLOGY, CARDIFF, United Kingdom

Sinopsis:To evaluate the reliability of well-known network metrics over alternative weighting strategies using diffusion MRI. Methods: Using ten different network weighting strategies to construct structural networks from repeat dMRI scans, we estimated the reliability of network metrics estimated over the networks. Additionally, the recognition accuracy was estimated for each of the ten strategies. Results: We demonstrated excellent ICC for six network metrics for the seven out of network weighting strategies. Recogniton accuracy was 100% accurate for the number and percentage of streamlines and tract volume. Conclusions: Our results highlight the importance of reliable network metrics from structural brain networks.

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