We then generated mean time series for each condition (three, four, and five-sniffs) by weighting Enzalutamide purchase these mixture-based time series according to the relative number of trials for each sniff number (cf. Figure 5A). Functional imaging was performed using a Siemens Trio 3T MRI scanner

to acquire gradient-echo T2∗-weighted echoplanar images (EPIs) with blood-oxygen-level-dependent (BOLD) contrast, using a 12-channel head coil and an integrated parallel acquisition technique known as GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisition) to improve signal recovery in medial temporal and basal frontal regions. Image acquisition was tilted 30° from the horizontal axis to reduce susceptibility artifact

in olfactory areas. Four runs of ∼450 volumes each were collected in an interleaved ascending sequence (24 slices per volume). Imaging parameters were as follows: repetition time (TR), 2 s; echo time, 20 ms; slice thickness, 2 mm; gap, 1 mm; in-plane resolution, 1.72 × 1.72 mm; field of view, 220 × 206 mm; matrix size, 128 × 120 voxels. Whole-brain high-resolution T1-weighted anatomical scans (1 mm3) were acquired after functional scanning, coregistered to the mean functional image, normalized, and averaged across subjects to aid in localization. Data preprocessing and analysis were achieved using SPM5 (http://www.fil.ion.ucl.ac.uk/spm/). After the first six INCB018424 cost “dummy” volumes were discarded to permit T1 relaxation, images were spatially realigned to the first volume of the first session and slice-time adjusted. This was followed by spatial normalization to a standard EPI template, resulting in a functional voxel size of 3 mm3, and smoothing

with a 6-mm Gaussian kernel, aiding multisubject comparisons. In Experiment 2, two others different fMRI models were implemented to investigate the neural basis of olfactory evidence accumulation in the human brain. Three-, four-, and five-sniff conditions were selected for analysis because these contained sufficient numbers of trials across each subject for meaningful comparisons to be made. This method also ensured that data were not simply averaged across subjects with different response times, which would have introduced smoothing artifacts in the time-course data. It is important to reiterate that the behavioral data (from which drift rates and integrator models were computed) were collected simultaneously during fMRI scanning. To investigate how region-specific fMRI time courses related to evidence integration, the preprocessed event-related fMRI data were analyzed using a finite impulse response (FIR) model, enabling us to model temporal integrative profiles. Selected conditions (three-, four-, and five-sniff trials) were specified using 14 time bins each of 2 s duration.