FYI - This from a recent paper that the UPenn group published on PNC.
Cereb Cortex. 2014 Apr 25. [Epub ahead of print]
Linked Sex Differences in Cognition and Functional Connectivity in Youth.
Satterthwaite TD1, Wolf DH, Roalf DR, Ruparel K, Erus G, Vandekar S, Gennatas ED, Elliott MA, Smith A, Hakonarson H, Verma R, Davatzikos C, Gur RE, GurRC.
Subject-Level Time Series Processing
A voxel-averaged time series was extracted from each of the 264 nodes in subject-space for every participant. Time series data were processed using a validated confound regression procedure that has been optimized to reduce the influence of subject motion (Satterthwaite et al. 2013a, 2013b). The first 4 volumes of the functional time series were removed to allow signal stabilization, leaving 120 volumes for subsequent analysis. Functional time series were band-pass filtered to retain frequencies between 0.01 and 0.08 Hz. Functional images were re-aligned using MCFLIRT (Jenkinson et al. 2002). Structural images were skull-stripped using BET (Smith 2002) and segmented using FAST (Zhang et al. 2001); mean WM and cerebrospinal fluid (CSF) signals were extracted from the tissue segments generated for each subject (Jakobs et al. 2012; Reetz et al. 2012). Improved confound regression (Satterthwaite et al. 2013a) included 9 standard confounding signals (6 motion parameters + global/WM/CSF) as well as the temporal derivative, quadratic term, and temporal derivative of the quadratic of each. Prior to confound regression, all motion parameters and confound time courses were band-pass filtered in an identical fashion as the time series data itself in order to prevent mismatch in the frequency domain and allow the confound parameters to best fit the retained signal frequencies (Hallquist et al. 2013). Furthermore, motion-related spike regressors were included in the model whenever a volume-to-volume displacement was >0.25 mm; for each such movement, a single regressor was included for each volume bounding the observed displacement (i.e., TR −1 and TR +1); these spike regressors effectively censor the influence of these volumes in subsequent analysis of residual time series (Lemieux et al. 2007). As 2 volumes are lost from analysis for each spike, participants with >20 spikes were excluded (see “Participants” above), ensuring that each subject had at least 4 min (80 volumes) of time series data for analysis (van Dijk et al. 2010).
FYI - This from a recent paper that the UPenn group published on PNC. Cereb Cortex. 2014 Apr 25. [Epub ahead of print] Linked Sex Differences in Cognition and Functional Connectivity in Youth. Satterthwaite TD1, Wolf DH, Roalf DR, Ruparel K, Erus G, Vandekar S, Gennatas ED, Elliott MA, Smith A, Hakonarson H, Verma R, Davatzikos C, Gur RE, GurRC. Subject-Level Time Series Processing
A voxel-averaged time series was extracted from each of the 264 nodes in subject-space for every participant. Time series data were processed using a validated confound regression procedure that has been optimized to reduce the influence of subject motion (Satterthwaite et al. 2013a, 2013b). The first 4 volumes of the functional time series were removed to allow signal stabilization, leaving 120 volumes for subsequent analysis. Functional time series were band-pass filtered to retain frequencies between 0.01 and 0.08 Hz. Functional images were re-aligned using MCFLIRT (Jenkinson et al. 2002). Structural images were skull-stripped using BET (Smith 2002) and segmented using FAST (Zhang et al. 2001); mean WM and cerebrospinal fluid (CSF) signals were extracted from the tissue segments generated for each subject (Jakobs et al. 2012; Reetz et al. 2012). Improved confound regression (Satterthwaite et al. 2013a) included 9 standard confounding signals (6 motion parameters + global/WM/CSF) as well as the temporal derivative, quadratic term, and temporal derivative of the quadratic of each. Prior to confound regression, all motion parameters and confound time courses were band-pass filtered in an identical fashion as the time series data itself in order to prevent mismatch in the frequency domain and allow the confound parameters to best fit the retained signal frequencies (Hallquist et al. 2013). Furthermore, motion-related spike regressors were included in the model whenever a volume-to-volume displacement was >0.25 mm; for each such movement, a single regressor was included for each volume bounding the observed displacement (i.e., TR −1 and TR +1); these spike regressors effectively censor the influence of these volumes in subsequent analysis of residual time series (Lemieux et al. 2007). As 2 volumes are lost from analysis for each spike, participants with >20 spikes were excluded (see “Participants” above), ensuring that each subject had at least 4 min (80 volumes) of time series data for analysis (van Dijk et al. 2010).