Tim trio 3 tesla

All neuroimaging was performed on the same Siemens Tim Trio 3-tesla MRI scanner located at Brown University MRI Research Facility. High-resolution structural MRI of the whole brain was acquired in the sagittal plane using a T1-weighted MPRAGE pulse sequence with TE/TR = 3.06/2250ms, flip angle = 9°, FOV = 220mm, matrix = 256×256, slice thickness = 0.86mm. Head restraint was used during image acquisition and quality control of T1 images was performed immediately following acquisition to identify excessive movement and other artifacts. Repeated acquisitions were prescribed as necessary, and images with inadequate quality were excluded from analysis. Parcellation of brain regions was performed on each T1-weighted MRI image using automated algorithms implemented in Freesurfer, where anatomical labels are assigned to each voxel based on probabilistic estimates after a nonlinear registration to an atlas, and volumetric measures were derived for each brain^{24 (link)}. Combined bilateral volumes of cortical grey matter (GM), cerebral white matter (WM), caudate, putamen, globus pallidus, thalamus, hippocampus, amygdala, and overall ventricles were thus derived (Figure 1). Intracranial volumes were also measured and included in all statistical models to control for variability in head size.

PNC neuroimaging was performed on a single Siemens Tim Trio 3 Tesla,
Erlangen, Germany, whole body scanner with a 32 channel head coil^{18 (link)}; participants were scanned for
a total of six minutes, eighteen seconds^{18 (link)}. The exact sequence parameters for rs-fMRI imaging were
reported in by Satterthwaite and colleagues^{18 (link)}.
Image processing was performed with SPM12 called through MATLAB2015b.
These steps included slice time correction, motion correction, co-registration,
segmentation, and normalization (Fig. 1).
Images were normalized onto the Montreal Neurological Institute (MNI) atlas
space. A high-pass temporal filter of 0.005 Hz was applied to all voxel time
courses and motion time courses. The aCompcor method of nuisance
regression^{19 (link)} was
utilized to mitigate against the detrimental effects of scan motion ^{20 (link),21 (link)}; motion time courses and non-neuronal aCompcor nuisance
time courses were regressed out of all voxels. Images were then spatially
smoothed (6-mm FWHM) and temporally filtered (bandpass 0.01–0.1 Hz). To
further account for the effects of scan motion, time points with FD > 0.2
mm were removed (i.e. scrubbed)^{21 (link)}. Full-brain connectivity maps were generated for each
subject with a 4-millimeter seed placed in the ventro-lateral pulvinar nucleus
(MNI coordinates: x = −24, y = −32, z = −2).

Two fMRI paradigms were utilized, a fractal n-back working memory task probing executive system function, and an emotion identification task probing amygdala responses to threatening faces (Figure 1A&B). The n-back task ^{31 (link),32 (link)} employed fractals and a robust block design to measure activation of the executive system across three levels of working memory load. To examine amygdala responses to threat, we applied a validated emotion identification paradigm, ^{33 (link),34 (link)} grouping expressions into threatening (anger, fear) and non-threatening (happy, sad) emotions for event-related analysis, as in prior work ^{12 (link),34 (link)–36 (link)}. This grouping is based on prior theoretical and empirical work (see Satterthwaite et al.^{34 (link)} pp. 354–355 for detailed rationale); it is worth noting, however, that interpretations of this grouping not directly related to Threat are possible (e.g., negative and positive emotions), and subjective responses to particular emotional categories may vary according to individual subject characteristics including psychiatric symptomatology.
All imaging data were acquired on the same scanner (Siemens Tim Trio 3 Tesla; 32 channel head coil) using the same imaging sequences. Imaging acquisition sequences, procedures, and pre-processing methods have been previously reported ^{30 (link)} and are described in eMethods.