Stephanie

Background

The data from this project comes from Rosanna Olsen's dissertation.

Highpass filtering

Temporally filtering fMRI data can be useful in cleaning up the signal. Given knowledge about how quickly the signal should be oscillating back and forth, it can be useful to remove noise that is oscillating slower than the expected frequency. For instance, slow (i.e., low frequency) scanner drift, basal metabolism, or many other sources can cause raw fMRI signal to drift over the course of a session. In order to remove these low-frequency trends, one can highpass filter the data, filtering out the low frequencies while "passing through" the high frequencies (i.e., the frequencies related to the task of interest; see Fig. 3).

Figure 3. Example of a highpass filter (http://fsl.fmrib.ox.ac.uk/fslcourse/lectures/feat1_part1.pdf)

In order to remove the low frequency noise, one must first determine a highpass frequency cutoff (HPFC) parameter. This parameter can either be specified in Hz or in seconds. In FSL, the HPFC parameter is specified in seconds; this corresponds to the period of interest from the experiment. That is, frequencies that are lower (i.e., occurring over a period longer than the period of interest) will be removed, whereas higher frequencies are retained. After the design matrix is constructed, the highpass filter is applied to each individual voxel's timecourse before the model estimation takes place. Essentially, this highpass temporal filtering uses a local fit of a straight line (Gaussian-weighted within the line to give a smooth response) to remove low frequency artifacts.

Materials and Methods

Subjects

Subjects were 19 healthy, right-handed volunteers.

Memory Task

Subjects completed 12 scanned runs consisting of an encoding phase and a recognition test phase (see Fig. 1). Each encoding phase lasted 4.5 minutes, and the test phase lasted 1.5 minutes; separating the encoding and test phases was a delay period during which subjects performed an odd/even judgment task as a baseline condition.

Figure 1. Task Design

During a given encoding phase, subjects were presented with 15 unique shapes. Each of the 15 shapes was presented three times for 2.5 s each time (separated by a 1.5s ISI, during which a fixation was presented). Subjects were tasked with making a size judgment (i.e., aspect ratio; taller than wide, or wider than tall) for every shape.

Figure 2. Morph Stimuli

For each test phase, subjects were presented with 15 shapes (2.5 s duration, 1.5 s fixation); critically, five of the shapes were identical to the studied shapes, five were perceptually near shapes ("near"), and five were perceptually far ("far"). These shape stimuli were designed [[[XXXXXXXXXXX]]Link title]. For each of the 15 shapes, subjects were asked to rate if the shape was new or old, using a 1-5 confidence rating scale (1:sure new, 2:moderately sure new, 3:guess, 4:moderately sure old, 5:sure old).

fMRI Analysis

MR acquisition

Imaging data were acquired on a 3.0 T Signa whole-body MRI system with a custom-built head coil (GE Medical Systems, Milwaukee, WI, USA).

In total, 2,400 functional volumes were acquired for each participant using a T2*-sensitive gradient echo spiral in/our pulse sequence (Glover and Law, 2001). Functional imaging parameters were optimized to provide whole brain coverage (TR=2000ms; TE= 30ms; flip angle = 75°; FOV = 22 cm; 3.44 x 3.44 x 4 mm resolution, 30 slices).

fMRI Analysis

The fMRI data was analyzed using Lyman, Nipype, Freesurfer, AFNI, and FSL software tools.

Pre-processing

All data went through a standard preprocessing pipeline using Freesurfer and FSL, including motion (RapidART) and slice time correction, realignment (middle volume of each run), skull stripping, temporal filtering (high-pass cutoff 128 Hz), and surface-based coregistration (bbregister). Data were spatially smoothed (6 fwhm, SUSAN -- only averages a given voxel with local voxels that have a similar intensity), scaled grand median of timeseries to 10000, & normalized for group analyses (nonlinear warp to FSL’s MNI152 space). Data were modeled using a double gamma function, and the first 5 frames of each run were discarded.

Results

Behavioral Results

Perceptual similarity

First, 2 separate groups of subjects rated the perceptual similarity between the different morphs used during the recognition test phase (1-7 scale, 1=least similar to 7=most similar). In general, subjects rated the identical morphs as very perceptually similar to the same morph. Further, subjects rated the near morphs as being more perceptually similar to the parent morph than they rated the far morphs; however, the near morphs were accurately judged to be less perceptually similar to the parent morphs than the identical morphs (see Fig. 4).

Figure 4. Average perceptual similarity ratings for the 3 morph types

Recognition Memory

As predicted, recognition memory confidence was modulated by the perceptual similarity of the test morphs to the parent morph presented during encoding. That is, as the similarity between the test morph and the studied morph increased, recognition confidence ratings increased. Here, a repeated measures ANOVA comparing the mean recognition confidence scores across test morph type (identical: M=4.14, near: M=3.58 and far: M=2.72) found a significant effect of test morph similarity on recognition confidence ratings (F(92.14, p < 0.001). Paired comparisons between identical vs. near and near vs. far also revealed significant differences between recognition for these morphs (ps < 0.001).
For the fMRI analyses, due to the relatively small number of trials in each condition/response bin, we coded trials as "OLD" if the subject had responded with a recognition confidence rating of 4 or 5 (moderately sure or sure old). Likewise, we coded trials as "NEW" if the subject had responded with a recognition confidence rating of 1 or 2 (moderately sure or sure new). Here, analyses confirmed that subjects rated a higher proportion of identical morphs as "OLD." Moreover, on average subjects rated more than half of the near morphs as "OLD." In general, the proportion of morphs rated as "OLD" decreased as the perceptual similarity between the test morph and the encoded parent shape decreased (identical > near > far; see Fig. 5).

Figure 5. Proportion trials rated "OLD" by Morph Type

Further, based on recent findings from Duncan & Davachi (2012), we were interested in whether a subject's recognition confidence on a given trial might be modulated by their response confidence on the preceding trial. In other words, if a subject rates a morph as "confident old," might they then be more likely to rate the next morph as old? In addition, we were curious whether this effect might be modulated by the current trial morph, and it's similarity with the encoded shape (i.e., identical, near, far). To investigate this, we binned each trial based on the current morph type (i.e., identical, near, far), and the response confidence on the previous trial (i.e., OLD: 4,5 or NEW: 1,2). Here, we used a linear mixed effects model (LMM) predicting recognition confidence on the current trial, with the current morph type and previous trial confidence as fixed effects, random intercepts for subjects, and random slopes by morph type and previous trial confidence. As hypothesized, we found a significant effect of previous trial confidence (beta = 0.10, t=3.10), and no interaction (p>0.05), such that response confidence on the previous trial influences response confidence on the current trial regardless of the current trial morph type (see Fig. 6).

Figure 7. Recognition Confidence by Morph Type & Previous Trial Confidence

Conclusions

In summary, k jfklasjfdskaljd lksajdlksa jlksd

References - Resources and related work

References

Software