Abstract
We aim to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity. Following [1], we formulate this problem as estimating a multivariate autoregressive (MAR) model with sparse linear regression. We introduce a model selection framework based on cross-validation. We select the appropriate sparsity of the connectivity matrices and demonstrate that choosing an ordering for the MAR that lends to sparser models is more appropriate than a random. Finally, we suggest randomized Least Absolute Shrinkage and Selective Operator (LASSO) in order to identify relevant anatomo-functional links with better recovery of ground truth.
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Deligianni, F. et al. (2012). Relating Brain Functional Connectivity to Anatomical Connections: Model Selection. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_23
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DOI: https://doi.org/10.1007/978-3-642-34713-9_23
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