Statistical methods for comparing brain connectomes at different scales

Djalel-E. Meskaldji; Stephan Morgenthaler; Dimitri Van De Ville

doi:10.1117/12.2189847

24 August 2015 Statistical methods for comparing brain connectomes at different scales

Djalel-E. Meskaldji, Stephan Morgenthaler, Dimitri Van De Ville

Proceedings Volume 9597, Wavelets and Sparsity XVI; 95971L (2015) https://doi.org/10.1117/12.2189847
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

Physiological Brain connectivity and spontaneous interaction between regions of interest of the brain can be represented by a matrix (full or sparse) or equivalently by a complex network called connectome. This representation of brain connectivity is adopted when comparing different patterns of structural and functional connectivity to null models or between groups of individuals. Two levels of comparison could be considered when analyzing brain connectivity: the global level and the local level. In the global level, the whole brain information is summarized by one summary statistic, whereas in the local analysis, each region of interest of the brain is summarized by a specific statistic. We show that these levels are mutually informatively integrative in some extent. We present different methods of analysis at both levels, the most relevant global and local network measures. We discuss as well the assumptions to be satisfied for each method; the error rates controlled by each method, and the challenges to overcome, especially, in the local case. We also highlight the possible factors that could influence the statistical results and the questions that have to be addressed in such analyses.

Citation Download Citation

Djalel-E. Meskaldji, Stephan Morgenthaler, and Dimitri Van De Ville "Statistical methods for comparing brain connectomes at different scales", Proc. SPIE 9597, Wavelets and Sparsity XVI, 95971L (24 August 2015); https://doi.org/10.1117/12.2189847

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available