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Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality. Neuroinformatics 2016 Jan;14(1):99-120

Date

10/17/2015

Pubmed ID

26470866

DOI

10.1007/s12021-015-9281-6

Scopus ID

2-s2.0-84953837503 (requires institutional sign-in at Scopus site)   17 Citations

Abstract

Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling.

Author List

Youssofzadeh V, Prasad G, Naeem M, Wong-Lin K

Author

Vahab Youssofzadeh PhD Assistant Professor in the Neurology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Acoustic Stimulation
Auditory Cortex
Auditory Perception
Brain
Data Interpretation, Statistical
Electroencephalography
Evoked Potentials
Evoked Potentials, Auditory
Humans
Models, Neurological
Nonlinear Dynamics
Signal Processing, Computer-Assisted