Computational protocol: Long-term effects of stimulant exposure on cerebral blood flow response to methylphenidate and behavior in attention-deficit hyperactivity disorder

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Protocol publication

[…] Data were acquired using a 3.0 T Philips MR Scanner (Philips Medical Systems, Best, The Netherlands). First, an anatomical 3D–FFE T1-weighted scan was obtained with the following scan parameters: TR/TE = 9.8/4.6; FOV = 256x256x120; voxel size = 0.875 × 0.875 × 1.2 mm. CBF images were acquired using a pseudo continuous arterial spin labeling (pCASL) sequence with the following parameters: TR/TE = 4000/14 ms; post-labeling delay = 1650 ms; label duration = 1525 ms; FOV = 240x240x119; 75 dynamics; voxel size = 3x3x7mm, GE-EPI, SENSE = 2.5, no background suppression, scan time = 10 min. Heart rate (HR) was monitored using a peripheral pulse unit.Data were processed using the Iris pipeline for CBF quantification and multi-atlas region segmentation (Bron et al. ). All image registrations were performed using Elastix registration software (Klein et al. ). For the ASL data, motion estimation was performed using rigid registration with a group-wise method that uses a similarity metric based on principal component analysis. Then, outlier rejection was performed to correct for sudden head movements. Outlier rejection was based on the Mdiff images, the subtractions of all pairs of control (Mc) and label images (Ml). For each pair of Mdiff images, we computed the sum of squared differences (SSD) which is the sum of all squared voxel-wise differences between the two images. As such, for each of the 75 time points, we obtained 74 SSD values over which we computed the median and SD. To obtain a more robust estimate of the SD, we computed this based on only the SSD values that were lower than the median. If more than 50% of the SSD values were larger than the median + (3*SD) this timepoint was considered an outlier. Subsequently, motion correction was performed on the remaining timepoints, and the resulting motion-compensated Mdiff images were averaged to obtain a perfusion-weighted image (ΔM). Motion was quantified as the mean framewise displacement. Quantification of CBF was performed using the single-compartment model (Buxton et al. ), which is the recommended approach for pCASL (Alsop et al. ). The following parameters were used: labeling efficiency αGM = 0.85, T1GM = 1.6 ms, blood-brain partition coefficient λGM = 0.95 mL/g. The average of Mc images was used as a proton-density normalization image (M0) for the CBF quantification. Differences in post-labeling delays between slices (due to the 2D read-out) were accounted for. CBF was quantified in GM only, with a 3D method for partial volume correction based on local linear regression using the tissue probability maps (Asllani et al. ; Oliver et al. ). For each subject, probabilistic GM segmentations based on the T1-weighted scan (SPM8, Statistical Parametric Mapping, UCL, London, UK) were rigidly registered to the ΔM images by maximizing mutual information. For further analysis, CBF maps were transformed to the space of the T1-weighted scan. An example of a representative perfusion-weighted image can be observed in Fig. .Fig. 1 For each participant, we defined three regions of interest (ROIs) using a multi-atlas approach, registering 30 labeled T1-weighted images (Gousias et al. ; Hammers et al. ) with the participants’ T1-weighted images. The labels of the 30 atlas images were fused using a majority voting algorithm to obtain a final ROI labeling (Heckemann et al. ). For three pre-defined ROIs, comprising the striatum, thalamus and anterior cingulate cortex (ACC), CBF mean values were extracted (Fig. ). The striatum was selected because it is rich in DAT (the primary target of action of MPH) and the thalamus and prefrontal cortex were chosen because the animal literature demonstrated largest effects of early MPH treatment using phMRI in these two important neuronal projections from the striatum (Andersen et al. ).Fig. 2 […]

Pipeline specifications

Software tools elastix, SPM
Application Magnetic resonance imaging
Organisms Homo sapiens
Chemicals Dopamine, Methylphenidate