4.7 Article

Perigenual anterior cingulate cortex and its structural covariance as predictors for future body fat gain in young adults

Journal

OBESITY
Volume 31, Issue 2, Pages 446-453

Publisher

WILEY
DOI: 10.1002/oby.23629

Keywords

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This study aimed to investigate whether baseline gray matter (GM) volume and structural covariance patterns could be used to predict body fat gain over a 1 to 2 year period in a relatively large sample. Voxel-based morphometry (VBM) analysis revealed a significant positive association between baseline GM volume in the perigenual anterior cingulate cortex (pgACC) and body fat gain. Furthermore, participants with higher future body fat gain showed increased structural covariances between pgACC and the middle frontal gyrus, middle temporal gyrus, inferior temporal gyrus, and cerebellum compared to those with lower future body fat gain.
ObjectiveThis study aimed to examine whether baseline gray matter (GM) volume and structural covariance patterns could predict body fat gain over 1 to 2 years in a relatively large sample. MethodsVoxel-based morphometry (VBM) analysis was applied to examine the association between baseline GM volume and body fat gain in 502 participants over 1 to 2 years. Furthermore, this study tested whether the structural covariances between the regions identified as seeds from VBM analysis and the rest of the brain were associated with future body fat gain. ResultsA significant positive association was observed between baseline GM volume in the perigenual anterior cingulate cortex (pgACC) and body fat gain over 1 to 2 years. Furthermore, relative to those with lower future body fat gain, pgACC covaried more extensively with the middle frontal gyrus, middle temporal gyrus, inferior temporal gyrus, and cerebellum in participants with higher future body fat gain. ConclusionsUsing VBM and structural covariance network analysis, the current study revealed that higher GM volume of pgACC and its increased structural covariances with specific brain regions were associated with future weight gain, which may guide the development of more effective prevention and treatment interventions for obesity.

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