Imaging acquisition and analyses
pain
REGRESSION, DISORDERS
MRI data was acquired with an MRI 3T Philips ACHIEVA scanner using a dStream head coil (32 channels). The EPI (Echo Planar Imaging) was used for the fMRI. Structural MRI included an acquisition whole-head in 3 dimensions, in sagittal section and weighted in T1 with the following parameters: MPRAGE, repetition time (RT)/echo time (ET) = 7.4/3.5 ms, flip angle = 8°, field of view (FOV) = 240 × 240 × 180 mm, isotopic voxel size = 1 mmDuring MRI acquisition, participants were in supine position, the arms to the sides. They were instructed to keep their eyes opened, remain still, relax and not think of anything in particular while staying awake.Imaging analyses were done with the CONN toolbox from the Massachusetts Institute of Technology (version 21a) functioning under MatLab (version R2017b). Experimental matrix was defined with the four times of evaluation (t0, t1, t2 and t3). Usual pre-treatment was made with the following steps: (1) realignment, (2) slice timing, (3) structural segmentation in grey and white matters and normalization, (3’) functional normalization of the functional images on the MNI (Montreal Neurological Institute) atlas, (4) identification of aberrant activities, and (5) smoothing. Denoising was done through a linear regression of potential confounding effects in the BOLD signal and a default band-pass filter between 0.008 and 0.09 Hz.An a priori analysis method [Seed Based Analysis (SBA)] was chosen for different ROIs. First, ROIs forming different networks from the Willard atlas, available from the “Functional Imaging in Neuropsychiatric Disorders Lab”, were used. Studied networks were: the Default-Mode Network (DMN), the sensorimotor network (SMN), the Salience Network (SN) and the executive network (ECN). ROI-to-ROI analysis were done to evaluate change inside these networks following interventions. Moreover, 13 ROIs associated with pain were selected from the literature