OBJECTIVE: A realistic simulation exploiting real cortical sources identified from non-invasive extra-cranial recordings in healthy subjects has been considered in order to select the most robust procedure for choosing the correct order of multivariate autoregressive (MVAR) models. Different signal-to-noise ratios filter settings and sampling rates were also tested on the estimate of functional connectivity among the network nodes, in simulated and real cases. METHODS: Starting from magnetoencephalographic recordings, cortical sources in primary sensorimotor areas of the hand were obtained by functional source separation (FSS). Different criteria for the choice of the model order were compared in the simulated network constructed through one of the FSS-extracted sources and its noise-added delayed copies. In two real cases, a validation of the model order (not known a priori) choice was obtained by comparing the time-frequency properties as depicted by classical non-parametric and MVAR methods at rest, during isometric contraction (stationary states) and while dynamically responding to a sensory stimulation (transient state). For completeness, the whole set of MVAR functional connectivity measures was taken into account, to assess the most suitable for our network description. RESULTS: That the use of an incorrect model order distorts network functional connectivity estimate was documented both in the realistic simulation and in the two real cases. The Minimal Description Length and Schwartz Bayesian Criterion were selected as the most robust for MVAR model order choice. Partial directed coherence (PDC) was the most suitable method for time-frequency connectivity estimate in the simulated as well as in the real cases, both in stationary and transient states. Moreover, the results of MVAR-based connectivity estimate depend on filter setting in the real case. CONCLUSIONS: The most robust procedure for choosing the correct MVAR model order was provided. The adjunctive comparison of MVAR to classical methods is recommended to validate the choice in the real case. SIGNIFICANCE: Correct MVAR model order choice and band filtering play an important role for the correct network connectivity estimate.
OBJECTIVE: A realistic simulation exploiting real cortical sources identified from non-invasive extra-cranial recordings in healthy subjects has been considered in order to select the most robust procedure for choosing the correct order of multivariate autoregressive (MVAR) models. Different signal-to-noise ratios filter settings and sampling rates were also tested on the estimate of functional connectivity among the network nodes, in simulated and real cases. METHODS: Starting from magnetoencephalographic recordings, cortical sources in primary sensorimotor areas of the hand were obtained by functional source separation (FSS). Different criteria for the choice of the model order were compared in the simulated network constructed through one of the FSS-extracted sources and its noise-added delayed copies. In two real cases, a validation of the model order (not known a priori) choice was obtained by comparing the time-frequency properties as depicted by classical non-parametric and MVAR methods at rest, during isometric contraction (stationary states) and while dynamically responding to a sensory stimulation (transient state). For completeness, the whole set of MVAR functional connectivity measures was taken into account, to assess the most suitable for our network description. RESULTS: That the use of an incorrect model order distorts network functional connectivity estimate was documented both in the realistic simulation and in the two real cases. The Minimal Description Length and Schwartz Bayesian Criterion were selected as the most robust for MVAR model order choice. Partial directed coherence (PDC) was the most suitable method for time-frequency connectivity estimate in the simulated as well as in the real cases, both in stationary and transient states. Moreover, the results of MVAR-based connectivity estimate depend on filter setting in the real case. CONCLUSIONS: The most robust procedure for choosing the correct MVAR model order was provided. The adjunctive comparison of MVAR to classical methods is recommended to validate the choice in the real case. SIGNIFICANCE: Correct MVAR model order choice and band filtering play an important role for the correct network connectivity estimate.
Authors: Gregor M Hoerzer; Stefanie Liebe; Alois Schloegl; Nikos K Logothetis; Gregor Rainer Journal: Front Comput Neurosci Date: 2010-06-02 Impact factor: 2.380
Authors: R Becker; D Vidaurre; A J Quinn; R G Abeysuriya; O Parker Jones; S Jbabdi; M W Woolrich Journal: Neuroimage Date: 2020-04-07 Impact factor: 6.556
Authors: Leighton B N Hinkley; Julia P Owen; Melissa Fisher; Anne M Findlay; Sophia Vinogradov; Srikantan S Nagarajan Journal: Front Hum Neurosci Date: 2010-11-11 Impact factor: 3.169
Authors: Thomas P White; Iona Symington; Nazareth P Castellanos; Philip J Brittain; Seán Froudist Walsh; Kie-Woo Nam; João R Sato; Matthew P G Allin; Sukhi S Shergill; Robin M Murray; Steve C R Williams; Chiara Nosarti Journal: Neuroimage Clin Date: 2014-01-18 Impact factor: 4.881