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Subjects' beta maps for each condition were then transformed to Talairach space, and resampled to a 2-mm isotropic resolution.
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We first extracted each GA and IA participant's mean decision screen beta maps for the two conditions from the GLM results, and standardized (z-scored) them per participant, per condition, and per parcel.
We first obtained each participant's mean "Decision screen ×4" activity map by averaging over the corresponding GLM beta maps for the two runs.
We created beta maps per subject for 2 contrasts: (hysteresis vs. no hysteresis) and (0° vs. 90°).
We then created beta maps per subject for the contrasts (AR = 1 vs. baseline), (AR = 1.1 vs. baseline), and (AR = 1.2 vs. baseline) for trials with 2 stimuli and ran an F-test to compare between the 3 conditions.
Mean parameter estimates were extracted from each of the four subsections for each subject (from the group-registered beta maps).
Resulting beta maps were averaged across subjects and treated as cross-subject connectivity maps for a given seed region.
For example, FSL's parameter estimate maps (e.g., pe1.nii.gz) are the equivalent of SPM's beta maps (e.g., beta_0001.nii).
It developed strategy maps and assigned a senior manager for each theme (the maps for each are shown in the exhibit "Mapping Corporate Strategy at DuPont").
This produced final maps for each mapping population.
We mention that these inequalities were applied for Euler's beta mapping and special means such as the arithmetic mean, the geometric mean, the harmonic mean and so on (for more details, see Ref. [19] and the references therein).
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CEO of Professional Science Editing for Scientists @ prosciediting.com