G. Handjaras

Publications (2)0 Total impact

• Conference Proceeding: Adaptive filtering for removing nonstationary physiological noise from resting state fMRI BOLD signals

  P. Piaggi, D. Menicucci, C. Gentili, G. Handjaras, M. Laurino, A. Piarulli, M. Guazzelli, A. Gemignani, A. Landi
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  ABSTRACT: fMRI is used to investigate brain functional connectivity after removing nonneural components by General Linear Model (GLM) approach with a reference ventriclederived signal as covariate. Ventricle signals are related to low frequency modulations of cardiac and respiratory rhythms, which are nonstationary activities. Herein, we employed an adaptive filtering approach to improve removing physiological noise from BOLD signals. Comparisons between filtering approaches were performed by evaluating the amount of removed signal variance and the connectivity between homologous contralateral regions of interest (ROIs). The global connectivity between ROIs was estimated with a generalized correlation named RV coefficient. The mean ROI decrease of variance was -52% and -11%, for adaptive filtering and GLM, respectively. Adaptive filtering led to higher connectivity between grey matter ROIs than that obtained with GLM. Thus, adaptive filtering is a feasible method for removing the physiological noise in the low frequency band and to highlight resting state functional networks.
  11th International Conference on Intelligent Systems Design and Applications (ISDA), Cordoba; 02/2013
• Conference Proceeding: Extraction and Synchronization of BOLD Spontaneous Oscillations Using Singular Spectrum Analysis

  D. Menicucci, A. Gemignani, A. Piarulli, R. Bedini, C. Gentili, G. Handjaras, S. Danti, M. Guazzelli, M. Laurino, P. Piaggi, A. Landi
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  ABSTRACT: Spontaneous cerebral blood oxygenation level-dependent (BOLD) fluctuations are gaining interest in the neurophysiology community. These oscillations are prominent in the low-frequency range with spatiotemporal correlations. From a healthy individual, a basal resting state BOLD fMRI acquisition has been performed by collecting 4 slices. Voxel signals from seven selected regions have been considered. We assumed a composite null-hypothesis of oscillations embedded in ¿red noise¿. To extract oscillations from BOLD signals we applied the Monte Carlo Singular Spectrum Analysis (SSA). Phase-synchronization of the oscillatory components, in the low-frequency range 0.085-0.13 Hz, have been also achieved. As results, region-dependent distributions were apparent both for the noise parameters and for the number of connections between voxels. Although further studies on population samples should confirm the result consistency, the SSA technique combined with a phase-synchronization analysis seems a feasible method to extract low frequency BOLD spontaneous oscillations and to find functional connections among cerebral areas.
  Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on; 12/2009