Assessing and Compensating for Zero-Lag Correlation Effects in Time-Lagged Granger Causality Analysis of fMRI
Effective connectivity in brain networks can be studied using Granger causality analysis, which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this paper showed that the zero-lag correlation does “leak” into estimates of time-lagged causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.
An instruction-level simulator for IBM 3090 with VF (vector facility) has been developed for studying the performance of vector processors and their memory hierarchies. Results of a study of the locality of several large scientific applications are presented. The cache miss ratios of vectorized applications are found to be almost equal to those of their original scalar executions. Moreover, both the spatial and temporal locality of these applications (in scalar and vector executions) are strong enough to show a sufficiently high hit ratio on conventional cache structures.<<ETX>>