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Participants included 69 adolescents/young adults (36 UHR and 33 controls), aged 16–21 (mean age = 18.86, SD = 1.5), who were recruited to the University of Colorado Boulder’s Adolescent Development and Preventive Treatment (ADAPT) research program. Email, newspaper advertisements, Craigslist, and community referrals were used to recruit UHR participants. Control participants were recruited through flyers and newspaper announcements. Exclusion criteria for both groups included history of head injury, neurological disorder, any contraindications to the magnetic resonance imaging (MRI) environment (e.g. current pregnancy or metal in the body), and having a DSM-IV-TR Axis I psychotic disorder or substance dependence. The presence of a psychotic disorder in a first-degree relative or meeting for an Axis I disorder was exclusionary criteria for controls. In regards to diagnoses, there was a large discrepancy between groups as to whether they had a current Axis I diagnosis due to sampling methodology. For the UHR group, a variety of diagnoses were given including mood disorders, anxiety disorders, and substance use disorders (although no one met criteria for substance dependence). This variety of Axis I diagnoses is typical of an UHR population [34]. Due to this diagnostic presentation of the UHR group, current medication usage was also discrepant. Data was generated as to whether the participant was currently taking a psychotropic medication (yes or no) to be used as a covariate in imaging analyses. The University of Colorado Boulder Institutional Review Board approved the protocol and written informed consent procedures for this investigation.
The Structured Interview for Prodromal Syndromes (SIPS) [35] was administered to detect the presence of a prodromal syndrome. Study participants met criteria for a prodromal syndrome in three possible ways: 1) Attenuated Positive Symptom Prodromal Syndrome (a score between 3 and 5 on the SIPS based on a scale from 0 to 6) and/or 2) decline in global functioning accompanying the presence of schizotypal personality disorder and age <19 and/or 3) Genetic Risk and Deterioration Prodromal Syndrome. The SIPS gauges distinct categories of prodromal symptoms including positive and negative symptom dimensions. A total score is generated for each domain, with higher scores corresponding to greater symptomatology. The Structured Clinical Interview for the Diagnostic and Statistical Manual was administered to determine the presence of psychosis and substance dependence exclusionary criteria (SCID-I) [36]. The Global Functioning Scale-Social (GFS:S) [37] was administered by trained graduate students to clinically assess for SF over the past month. This interview was developed for use with UHR adolescents/young adults and rates engagement with peers/relationships, conflict with peers, and intimate/family relationships [38]. The scale is rated by the clinician and scored from 1–10 (higher scores indicate better functioning). The training of clinical interviewers (who were advanced doctoral students) was conducted over a 2-month period, and inter-rater reliabilities exceeded the minimum study criterion of Kappa ≥ 0.80. The Penn Emotion Recognition Task (ER-40) is a computerized test of facial emotional recognition [39]. Participants view 40 faces displaying one of five emotions (happy, sad, anger, fear, or neutral) and determine which emotion is being expressed. Each emotional expression is presented eight times, with balanced representations of gender and ethnicity. This behavioral task is used in a variety of different studies, shows good psychometric properties, and is completed in around five minutes [40]. The order of stimuli is randomized, but fixed across subjects. The maximum score is 40, with higher scores indicating better performance. Structural images were acquired with a T1-weighted 3D magnetization prepared rapid gradient multi-echo sequence (MPRAGE; sagittal plane; repetition time [TR] = 2,530 ms; echo times [TE] = 1.64 ms, 3.5 ms, 5.36 ms, 7.22 ms, 9.08 ms; GRAPPA parallel imaging factor of 2; 1 mm3 isomorphic voxels, 192 interleaved slices; FOV = 256 mm; flip angle = 7°). Additionally, a five minute 34 second resting state blood-oxygen-level dependent (BOLD) scan was acquired with a T2-weighted echo-planar functional protocol (number of volumes = 165; TR = 2,000ms; TE = 29 ms; matrix size = 64 x 64 x 33; FA = 75°; 3.8 x 3.8 x 3.5 mm3 voxels; 33 slices; FOV = 240 mm). Participants were instructed to relax with their eyes closed during the resting state scan time. A turbo spin echo proton density (PD)/T2-weighted acquisition (TSE; axial oblique aligned with anterior commissure-posterior commissure line; TR = 3,720 ms; TE = 89 ms; GRAPPA parallel imaging factor of 2; FOV = 240 mm; flip ange: 120°; 0.9 x 0.9 mm2 voxels; 77 interleaved 1.5 mm slices) was generated to investigate incidental pathology. Studies indicate that the functional connectivity MRI (fcMRI) duration utilized in the present study provides equal power to longer scan times [41]. Data were preprocessed in FSL (v. 5; http://fsl.fmrib.ox.ac.uk/fsl), which involved motion correction, brain extraction, high-pass filtering (100 s), and spatial smoothing (6mm FWHM). Next, functional images were aligned to the MNI 2-mm brain template with a two-step procedure. First, the resting state scan was aligned to the high-resolution MPRAGE using a linear boundary-based registration method, which relies on white matter boundaries [42–44]. Second, the MPRAGE was nonlinearly aligned to the template and the two registrations were then combined to align the functional resting state scan to the template. Recent papers have demonstrated the importance of properly correcting for motion by not only regressing out motion parameters, but also regressing out or eliminating specific frames with motion outliers [45]. To accomplish this, we used the Artifact Rejection Toolbox (ART; http://www.nitrc.org/projects/artifact_detect/) to create confound regressors for motion parameters (3 translation and 3 rotation parameters), and additional confound regressors for specific image frames with outliers based on brain activation and head movement. In order to identify outliers in brain activation, the mean global brain activity (i.e., the mean signal across all voxels) was calculated as a function of time, and was then Z normalized. Outliers were defined as any frames where the global mean signal exceeded 3 SD. Similarly, frame-wise measures of motion (composite measure of total motion across translation and rotation) were used to identify any motion outliers (i.e., motion spikes). Motion outliers were defined as any frame where the motion exceeded 1 mm. All functional connectivity analyses were performed in the CONN toolbox 14.p [46], with SPM8 (Wellcome Department of Imaging Neuroscience, London, UK; www.fil.ion.ucl.ac.uk/spm). Anatomical images were segmented into gray matter, white matter, and CSF with SPM8 in order to create masks for signal extraction. The CONN toolbox [46] uses principal component analysis (PCA) to extract 5 temporal components from the segmented CSF and white matter, which were entered as confound regressors in the subject-level GLM. This approach corrects for confounds of motion and physiological noise without regressing out global signal, which has been shown to introduce spurious anticorrelations [47, 48]. Motion from the ART toolbox was included as a confound regressor. From the motion translation parameters, the ART toolbox calculates mean displacement, and we used this measure as well as the number of motion and mean signal outliers in order to compare the degree of head movement between the groups. Further preprocessing included a band-pass filter (0.008 to 0.09 Hz), detrending, and despiking, in accordance with procedures used to target resting state data [46]. The mean time-series, averaged across all voxels within each seed was used as a regression parameter, and correlated with all other voxel in the brain in seed-to-voxel connectivity analyses.
Functional connectivity was performed in the conn toolbox v. 14p [46]. Both the SN and DMN were evaluated using resting state functional connectivity using seed-to-voxel connectivity across the whole brain. Resting-state functional connectivity allows for the identification of specific intrinsic functional networks without the potential confounds of introducing functional tasks [23]. A priori seeds for these networks were masked based on established literature and single seeds (as opposed to multiple) were used to represent nodes of the SN and DMN in order to limit analyses and maximize power [16, 49] (Fig 1). The right anterior insula (rAI) is a crucial node of the SN [18] and was used to define the SN network, while the posterior cingulate cortex (PCC) represented the node for the DMN, consistent with previous UHR research [28, 29]. Seeds were generated using the Wake Forest University PickAtlas [50, 51] using a 4mm radius.
Figure data removed from full text. Figure identifier and caption: 10.1371/journal.pone.0134936.g001 Seed regions of interest.Note: The salience network, defined using a seed in the rAI (right anterior insula), MNI coordinates (38, -22, 10). The default mode network, defined using a seed in the PCC (posterior cingulate cortex), MNI coordinates (1, -55, 17).
First, we confirmed that the pattern of connectivity of the SN and DMN in our control and UHR sample was in line with established research [18, 23] (see S1–S4 Tables). Due to the impact of medication on the brain [52, 53], all between group analyses and UHR within group analyses controlled for whether a participant was currently taking a psychotropic medication or not. Following our inspection of the SN and DMN in both groups, we conducted between group analyses examining differences in both networks using seed to voxel connectivity across the whole brain. Next, we investigated relationships between SN and DMN connectivity in regards to both FER and SF, again using seed-to-voxel connectivity in the whole brain. In order to evaluate the relationship between connectivity and behavior, we examined connectivity involving the SN and DMN with both FER and SF within each group separately along with testing whether an interaction was present whereby the relationship between connectivity and behavior differed based on group status. Within network connections were defined as associations among a priori seeds and voxels of regions clearly established by literature to comprise either the SN (e.g. anterior cingulate cortex) or DMN (e.g. medial prefrontal cortex). In contrast, hypotheses evaluating connectivity of the SN/DMN and other brain regions examined associations among a priori seeds and voxels in the brain that do not belong to regions that typically comprise either the SN or DMN. Results of all analyses were thresholded at the voxel-level at puncorrected <0.001 and then corrected at the cluster-level using a false-discovery rate (FDR) of p<0.05 [54].
ANOVA models were used to evaluate group differences in demographic variables, FER, and SF. Because preliminary studies examining social processes impairment have found effect sizes at or exceeding 0.31 [55], there is good evidence to suggest that the current sample has adequate power to detect the hypothesized relationship. Further, the present evaluation exceeds the existing UHR literature sample sizes where evaluation of the SN and DMN has taken place [28, 29] along with the one study to examine familial risk for psychosis, the DMN, and social functioning [30]. Based on existing research, the current sample is adequately powered to detect group differences in social processes, connectivity, and links between connectivity and behavior.
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