Twenty-seven neurotypical individuals (18 female) with no history of psychiatric, neurological, or learning disorders were scanned while making evaluations of self
in the social
domains, once at age 10 (M
=0.35) and again at age 13 (M
=0.33). The other target was a fictional character, Harry Potter, about whom participants all had substantial knowledge (as indicated via questionnaire). An equal number of items were positive and negative valence. Sample phrases included: “I am popular,” “I wish I had more friends,” “I like to read just for fun,” and “Writing is so boring” (see Pfeifer et al., 2007
, for a complete description of paradigm development and full list of stimuli). Three participants were included both in this sample and that of Pfeifer et al. (2007)
As described in Pfeifer et al. (2007)
, one phrase was presented every three seconds, leaving approximately two seconds to respond as phrases averaged one second in duration. The 40 phrases were organized into four blocks of 20 stimuli each, and the four task blocks alternated between reporting whether the phrase described oneself or a fictional, highly familiar other: Harry Potter. Prior to the scan, it was ensured that all participants had sufficient familiarity with Harry Potter based on self-reports of knowledge about him on a 5-point scale (M
s = 3.76 and 3.59 for T1 and T2, respectively). Variability in knowledge across timepoints was not significantly correlated with brain activity extracted from the region of ventral mPFC that demonstrated longitudinal increases during self > other evaluations. Objectively, each participant reported reading at least one book, or watching at least three movies, from the series. Block order was counterbalanced between participants. The initial block was always followed by a block containing the same phrases but applied to the remaining target (Self or Other), and then the last two blocks contained the remaining phrases (applied to the Self and Other targets in the same order as the first two blocks). Each of the four blocks was 75 seconds long, and included the 20 stimulus trials (ordered pseudorandomly) and five null events during which no phrase was presented. Blocks were separated by 21 seconds of rest. Participants were verbally instructed on this task outside the scanner and prior to the initiation of the run, and then reminded of these instructions at the start of each block during the run.
At both timepoints, participants also self-reported about observed changes in visible secondary sex characteristics (e.g., pubic hair, skin problems, genital development) using the Pubertal Development Scale (PDS; Petersen, Crockett, Richards, and Boxer, 1988
). The PDS was always administered with a researcher nearby to answer any questions participants had about the meaning of items. Parents were notified several days in advance of the session that a form assessing pubertal development would be part of the assessment, which gave parents the opportunity to talk with their children about this topic in advance if they so desired. We utilized the scoring guidelines provided by Petersen and colleagues, and we omitted the social comparison question (about development relative to the pace of peers). There was a highly significant increase from T1 to T2 on the PDS (M
s = 1.64 and 2.58, SD
s = 0.46 and 0.68 at T1 and T2, respectively; t
(1, 26) = 7.95, p
0). Because there were no significant gender differences in PDS scores at either timepoint (female M
s = 1.66 and 2.66, male M
s = 1.60 and 2.40, ns
), analyses collapsed across gender to increase statistical power (consistent with the methods of Pfeifer et al., 2011
; and further appropriate because there were no significant differences in task performance by gender). At T1, there was no relationship between chronological age and pubertal development (r
(25) = 0.04, ns
). At T2, chronological age and pubertal development were moderately correlated in the expected direction, although this was only a trend (r
(25) = 0.29, one-tailed p
= .07). It should be noted that two participants (one girl and one boy) each “regressed” by less than one point on average in their self-reports of pubertal development, but the presence and proportion of “regressions” in this sample is highly consistent with that reported in other longitudinal research using the PDS (e.g., Petersen et al., 1988
). Furthermore, excluding these two participants from correlational analyses relating to pubertal development did not change the results. In order to assess the influence of puberty independently from that of chronological age, linear regression was conducted to obtain unstandardized residual values of pubertal development, indexing variability in PDS scores that could not be explained by age (in other words, age was used to predict PDS at each timepoint and the unstandardized residuals were saved, then utilized in further analyses).
fMRI images were acquired on a Siemens Allegra 3T scanner. The functional scan lasted 4 minutes and 54 seconds (gradient-echo, TR = 3000ms, TE = 25ms, flip angle = 90°, matrix size 64 by 64, FOV = 20cm, 36 slices, 3.125-mm in-plane resolution, 3-mm thick). For each participant, a high-resolution structural T2-weighted echo-planar imaging volume (spin-echo, TR = 5000ms, TE = 33ms, matrix size 128 by 128, FOV = 20cm, 36 slices, 1.56-mm in-plane resolution, 3-mm thick) was also acquired coplanar with the functional scan. Stimuli were presented to participants through high-resolution magnet-compatible goggles.
fMRI data were converted from dicoms using MRIconvert (http://lcni.uoregon.edu/~jolinda/MRIConvert/
), and then skull-stripped using FSL’s Brain Extraction Tool (http://www.fmrib.ox.ac.uk/analysis/research/bet/bet.pdf
). ARTrepair (http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html
) was used to detect and fix bad slices in the unpreprocessed functional data. Structural images were then manually reoriented to the AC-PC, after which preprocessing was carried out in Neuroelf (http://neuroelf.net/
) using SPM8 functions as follows: (a) structural images for each participant were first coregistered to the SPM T1 template; (b) functional images were then realigned to correct for head motion using a two-pass least squares approach and a 6 parameter (rigid body) spatial transformation (to the first image, then to the mean image); (c) realigned images were coregistered to their respective high-resolution structural image using a rigid-body transformation in 3-dimensions; (d) the structural image was segmented based on the SPM template tissue probability maps; (e) the determined parameters from the segmentation were used to spatially normalize functional images into a Talairach-compatible atlas using 12-parameter affine transformation; and (f) finally all functional images were smoothed using an 6-mm full width, half-maximum isotropic Gaussian Kernel. In each run, participants demonstrated < 5% of images with bad slices detected and fixed by ARTrepair, and < 0.5 mm mean image-to-image motion or < 5 mm max (single) image-to-image motion.
Following preprocessing, statistical analyses were implemented in SPM8 (Wellcome Department of Cognitive Neurology, London, UK; http://www.fil.ion.ucl.ac.uk/spm/
). For each subject, condition effects were estimated according to the general linear model, following the methods in Pfeifer et al. (2007)
. Blocks (Self Social, Other Social, Self Academic and Other Academic) were convolved with a canonical hemodynamic response function. Low-frequency drifts were removed by global scaling and motion parameters were included as regressors of no interest. T1 and T2 were modeled as two separate runs. The resulting contrast images were entered into group-level analyses using random effects models. Paired t-tests and a 2×2×2 Analysis of Variance (ANOVA) was conducted with three factors: Target (Self and Other), Domain (Social and Academic) and Age (T1 and T2). Unless otherwise noted, results were thresholded at p
< .001, k
= 10 voxels (3×3×3mm), to balance between Types I and II errors; in particular, results surviving FWE correction are indicated as such in the text and table.