Processing of pain by the developing brain: evidence of differences between adolescent and adult females.

Authors:
Tong H; Maloney TC; Payne MF; King CD; Ting TV and 3 more

Journal:
Pain

Publication Year: 2022

DOI:
10.1097/j.pain.0000000000002571

PMCID:
PMC9391252

PMID:
35297790

Journal Information

Full Title: Pain

Abbreviation: Pain

Country: Unknown

Publisher: Unknown

Language: N/A

Publication Details

Subject Category: Psychophysiology

Available in Europe PMC: Yes

Available in PMC: Yes

PDF Available: No

Transparency Score
4/6
66.7% Transparent
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Evidence found in paper:

"Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article."

Evidence found in paper:

"This work was funded by Cincinnati Children's Hospital Medical Center's Trustee Grant Award and NIH/NIAMS Grants R01 AR074795 and P30 AR076316. Marina López-Solà, PhD is hired as part of the Serra Hunter Programme of the Generalitat de Catalunya."

Evidence found in paper:

"The neuroimaging data were preprocessed using FSL (FMRIB Software Library version 6.0.3; the Analysis Group, FMRIB, Oxford, United Kingdom), and AFNI (Analysis of Functional Neuroimages version 20.3.02; Medical College of Wisconsin, WI). For the T1-weighted structural image of each participant, brain extraction was performed using FSL's BET (Brain Extraction Tool), then bias correction and segmentation were done using FSL's FAST (FMRIB's Automated Segmentation Tool). The brain extracted image was then normalized and resampled to the 2-mm isotropic Montreal Neurological Institute (MNI) ICBM 152 nonlinear sixth-generation template using FSL's FLIRT (FMRIB's Linear Image Registration Tool)., Each participant's functional (BOLD) scans were preprocessed in the following steps: First, brain extraction was performed using FSL's BET. Next, outlying functional volumes (ie, spikes) were detected using the DVARS metric within FSL's “fsl_motion_outliers.” Motion correction of the BOLD time series was done using MCFLIRT. The motion-corrected data were high-pass filtered at 0.01 Hz (100 seconds) and smoothed with a 6-mm full-width-at-half-maximum (FWHM) filter using AFNI's 3dBandpass. Intensity normalization (ie, scaling each functional volume by its mean global intensity) was applied to minimize confounds arising from pain-induced global cerebral blood flow fluctuations.,,, The intensity-normalized data were then aligned to the MNI template by first coregistering it with the participant's T1 structural MPRAGE image using FSL's FLIRT (6-parameter rigid body model).,"

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Paper is freely available to read
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Last Updated: Aug 05, 2025