Materials and Methods 73

3.2.1 Subjects

The subject of interest (ND) was a 31-year-old male memory athlete. He is an expert in using mental imagery (particularly the method of loci) to rapidly encode information into his long-term memory. He has won the USA Memory Championship

(www.usamemorychampionship.com) four times and has placed highly in international memory competitions. He is right-handed, neurologically healthy, has completed 18 years of education, and possesses normal vision. Informed consent was obtained in accordance with standard Washington University human research practices, and he was monetarily compensated for his participation.

Data from ND were compared to task data from the 10 subjects in the MSC sample, details for whom are present in Chapter 2 (Table 2.1).

3.2.2 Materials

Materials used were identical to those described in Chapter 2.

3.2.3 Implicit memory task

The same implicit memory task (IMT) used for the MSC subjects (described in Chapter 2) was also used for ND, with the following modification: Task data were collected over 8 sessions instead of 10 sessions. To adjust for this change, ND was presented with four task runs instead of three for the first 6 scan sessions. The final task was a repetition of the first task type (i.e., if ND was initially tasked with judging faces for a given session, then his fourth IMT run

would also use faces, albeit with novel stimuli). In addition, two scanning sessions were

collected in a single day, once in the early afternoon and again in the evening. This means that all of his data were collected over four days total, and therefore both the time of day and the rate of scanning overall differed between ND and the MSC group. Other task parameters were

consistent with those described in Chapter 2, including the specific order in which stimuli appeared in each run.

3.2.4 Analysis of behavioral data

Responses to stimuli were scored for accuracy and response time (RT). These were analyzed with ANOVAs and t-tests using SPSS software version 23 (http://www-

01.ibm.com/software/analytics/spss/). A 95% confidence interval was computed for accuracy and reaction time around the MSC group mean, and ND was said to be different if his mean fell outside of this range.

3.2.5 MRI data acquisition

Data were acquired as described in Chapter 2.

3.2.6 Data preprocessing

Data were processed as described in Chapter 2.

3.2.7 GLM-based fMRI data analysis

GLMs were created as described in Chapter 2.

3.2.8 ANOVA and t-test parameters

Statistical tests were conducted as described in Chapter 2.

3.2.9 ROI definition

3.2.10 PMN ROI comparison

Several different steps were taken when comparing ND’s data to that of the MSC control group. First, the magnitude of the BOLD response was compared between ND and the MSC group in the left pIPL/dAG ROI. This ROI was selected because it was identified in ND as well as being the most commonly observed region showing repetition-related effects in MSC subjects. Based on observations made in Chapter 2, the magnitudes compared combined activity from the Scene and Word judgments. A 95% confidence interval was computed for the MSC group for the Presentation 1, Presentation 2, and Presentation 3 responses (averaging activity at Time Points 3- 5 as a means of estimating the response magnitude). ND’s responses were considered significant if his point-estimated magnitude fell outside of the 95% confidence interval around the MSC group mean.

3.2.11 Comparison of number of significant voxels

In addition to examining potential differences in response magnitudes, we examined whether or not the total number of significant voxels identified in ND and the MSC group might differ. We examine this in three different ways. First, we counted all voxels that emerged as significant following the Presentation x Time Point ANOVA and subsequent t-test, as outlined in Chapter 2 (see Section 2.2.10; Figure 2.2). Then we calculated the mean and 95% confidence interval of this value for the MSC group and determined if ND fell within or outside of this range. We also conducted two similar analyses, using a more lenient means of identifying significant voxels. This second approach required only a t-test comparing Time Points 3-5 for Presentation 3 and Presentation 1 to differ with a |z| > 1.25. This was the same threshold used to generate Figures A.7 to A.10. We then compared the number of voxels identified as showing Presentation 3 > Presentation 1 effects (i.e., repetition enhancement effects), or voxels showing

either Presentation 3 > Presentation 1, or Presentation 1 > Presentation 3 effects (i.e., any difference between presentations). A mean and 95% confidence interval was computed for the MSC group, and ND’s values were compared to this range.

3.2.12 Correlating raw Presentation x Time Point ANOVA maps

A final approach to examining ND to the MSC control group was to compare the overall statistical image generated by the initial Presentation x Time Point ANOVA across individuals. The unthresholded images for each person were converted into a single vector of values

representing all values for all voxels in each subject’s volume, and these vectors were then correlated across subjects. After calculating all pairwise correlations, the resulting values were submitted to a hierarchical clustering analysis using the linkage function in Matlab (The MathWorks, Natick, MA). This progressively linked all subjects in increasing order of

dissimilarity. The correlations were then converted using Fisher’s r to z’ transformation (Fisher, 1915), and average values were computed across all MSC pairings for each MSC subject. A separate mean was computed for all pairwise values between ND and each MSC subject. A group mean and 95% confidence interval was computed for the MSC subjects and was compared to ND’s z’ value to determine if he significantly differed from the group.