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Updated: Jan 9, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
Inferring causal relations from multivariate data using Large-Scale Augmented Granger Causality (lsAGC)
Axel Wismüller1, Ali Vosoughi2, Akhil Kasturi2
1Department of Imaging Sciences, Rochester, 14620, NY, USA; Department of Electrical and Computer Engineering, Rochester, 14620, NY, USA; Department of Biomedical Engineering, Rochester, 14620, NY, USA; Faculty of ICR, Ludwig Maximilian University, Munich, Germany.
Abstract:
Causal inference from high-dimensional and short time-series data is crucial to scientific discovery across diverse fields. Yet, standard approaches frequently fail under these constraints. We propose Large-scale Augmented Granger Causality (lsAGC), integrating dimension reduction, a Granger-based predictive framework, and data augmentation, to handle large-scale networks even when T
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