Impact of spatial scale in cortical thickness networks is not significant in discriminating Alzheimer’s and Autism from controls *Pradeep Reddy Raamana1, Stephen C. Strother1,3 and for the Alzheimer’s Disease Neuroimaging Initiative 1 Rotman Research Institute, Baycrest Health Sciences; 3Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. *Corresponding author: praamana@research.baycrest.org 1 Introduction •Development of automatic imaging biomarkers predictive of future cognitive decline is necessary for the early detection of neurodegenerative disorders such as Alzheimer’s disease (AD). •There has been great progress in the past few decades in this direction due to the advent of large multi-site datasets like ADNI that are publicly available. •However, owing to the nature of large variability in disease presentation and overlap among different diseases in the early stages, the specificity and sensitivity of the imaging biomarkers is still unsatisfactory. •In order to improve the accuracy of the base imaging biomarkers, network-level feature extraction is gaining popularity as the method of choice [1]. •Here, we present •one such approach whose potential has been demonstrated in ADNI dataset in discriminating AD from Healthy Controls (CN) [2], and •a detailed study of its methodology to evaluate its robustness and understand the impact of the key choices [3] under 2 levels of separability. •Results from replication analysis on a subset of Autism Brain Imaging Data Exchange (ABIDE) dataset. 2 Method and Results •Given high potential for the development of accurate early detection methods and its wide-accessibility structural, T1 MRIs, thickness-based networks deserve a systematic study to understand •the impact of the choice of edge metric that defines connectivity (such as correlation [4], similarity [2], dissimilarity [5]) •the impact of the choice of cortical parcellation (patch-wise delineation vs region-growing vs functional ROI segregation) and its scale (size and number of ROIs) affect the network properties and their predictive performance? See Fig. 1. •We perform this study using the data from ADNI and ABIDE datasets (demographics in Tables 1 and 2) under 3 levels of separability. •Due to the variable sizes of ROIs, as shown in Fig. 2, approaches [2,4,5] rely on summary statistics to compute edge weights and do not utilize the rich description of thickness features. •By discretizing the thickness distribution in each patch, we enable a histogram- based framework to compute edge weights from the entire distribution from a given patch, instead of basing them on summary statistics such as mean or meadians. •We present and study 6 different methods of edge computation based on histogram distances as listed in Table 2, including two baseline methods. •The predictive power of these 6 methods are studies at 6 different spatial scales (nodal sizes) of the cortical parcellation as noted in Table 2, using the cross-validation procedure published in [3,2]. •The distributions of AUC for different combinations of edge definitions and scales are shown in Fig. 3. References: 1. Evans, A. C. (2013). Networks of anatomical covariance. NeuroImage, 80, 489–504. 2. Raamana, P. R., Weiner, M. W., Wang, L., Beg, M. F. (2015). Neurobiology of Aging, 36 Suppl 1, S91–S102. 3. Raamana, P. R., Strother, S. C., (2016). International Workshop on Pattern Recognition in Neuroimaging (PRNI), Trento, pp. 1-4. 4. He, Y., Chen, Z. J., & Evans, A. C. (2007), Cerebral Cortex, 17(10), 2407–2419. 5. Tijms, B. M., Seriès, P., Willshaw, D. J., & Lawrie, S. M. (2012). Cerebral Cortex, 22(7), 1530–1541. Funding: This research has been sponsored by Canadian Institute of Health Research (CIHR), Ontario Brain Institute (OBI) and Ontario Neurodegenerative Disease Research Initiative (ONDRI). Type of base distribution Type of Edge Metric Acronym Definition Summarized Similarity (diff. in medians) MD exp(similarity) EMD raw distribution Wil