Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in which current methods fail, due to the lack of enough data points. We tested the performances of this new approach, in comparison with the classical approach based on ordinary least squares (OLS), by means of a simulation study implementing different ground-truth networks, under different network sizes and different levels of data points. Simulation results showed that the new approach provides better performances, in terms of accuracy of the parameters estimation and false positives/false negatives rates, in all conditions related to a low data points/model dimension ratio, and may thus be exploited to estimate and validate estimated patterns at single-trial level or when short time data segments are available.
Estimating brain connectivity when few data points are available: Perspectives and limitations / Antonacci, Yuri; Toppi, Jlenia; Caschera, Stefano; Anzolin, Alessandra; Mattia, Donatella; Astolfi, Laura. - ELETTRONICO. - (2017), pp. 4351-4354. (Intervento presentato al convegno 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 tenutosi a Island; South Korea nel 2017) [10.1109/EMBC.2017.8037819].
Estimating brain connectivity when few data points are available: Perspectives and limitations
Antonacci, Yuri
;Toppi, Jlenia;Caschera, Stefano;Anzolin, Alessandra;Mattia, Donatella;Astolfi, Laura
2017
Abstract
Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in which current methods fail, due to the lack of enough data points. We tested the performances of this new approach, in comparison with the classical approach based on ordinary least squares (OLS), by means of a simulation study implementing different ground-truth networks, under different network sizes and different levels of data points. Simulation results showed that the new approach provides better performances, in terms of accuracy of the parameters estimation and false positives/false negatives rates, in all conditions related to a low data points/model dimension ratio, and may thus be exploited to estimate and validate estimated patterns at single-trial level or when short time data segments are available.File | Dimensione | Formato | |
---|---|---|---|
Antonacci_Estimating-Brain-Connectivity_2017.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
905.04 kB
Formato
Adobe PDF
|
905.04 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.