Our main goal was to identify associations (linear and quadratic) of BMI and characteristics of eating behavior (CR, DIS) with BOLD activation during volitional regulation of food craving. We tested separate regression models to individually assess the relationship of BMI, CR, DIS or regulation success and the respective regulation contrasts (REGULATE_TASTY>ADMIT_TASTY, REGULATE_TASTY>REGULATE_NOT_TASTY) including age (analyses of BMI, CR, DIS, regulation success) or age and BMI (analysis of BMI 2 ) as covariates. To assess the relationship of craving intensity and appetitive brain activity, separate regression models were tested on the respective craving contrasts (ADMIT_TASTY>REGULATE_TASTY, ADMIT_TASTY>ADMIT_NOT_TASTY). Please see Supplementary Table III for a summary of performed regression analyses. Second-level maps were thresholded voxelwise at P<0.001 and corrected for multiple comparisons at a cluster threshold of P<0.05 (family-wise error) for the whole brain.
Functional connections analysis
Functional connectivity was assessed by means of psychophysiological interaction (PPI) analysis. 28 Source regions were based on the above-mentioned regression analysis of BOLD activation and BMI, our primary research focus. Individual BOLD signal time series within 4-mm spheres surrounding detected peak coordinates were extracted (based on the inverted U-shaped relationship of BMI and REGULATE_TASTY>ADMIT_TASTY, please see ‘Show’ section and Table 2 for details). General linear models were estimated separately for every source region including the following regressors: Time course of the respective source region (physiological vector), a vector coding for the main effect (psychological vector; REGULATE_TASTY>ADMIT_TASTY; with the former term weighted as +1 and the latter one weighted as ?1), and the PPI term (element-by-element product between the time course of the source region and the vector coding the main effect). The models also included realignment parameters as nuisance regressors. Single-subject contrasts for the PPI regressors were calculated. In the second-level analysis, we aimed to identify regions whose functional connectivity was related to BMI (linear and quadratic) or characteristics of eating behavior (CR, DIS). Therefore, the PPI terms were regressed on these measures in separate multiple regression analyses. Second-level models also included the regressors of no interest mentioned under subsection ‘Analysis of BOLD response’. Second-level maps were thresholded voxelwise at P<0.001 and corrected for multiple comparisons at a cluster threshold of P<0.05 (family-wise error) for the whole brain. Clusters were considered to be significant at P<0.017 (Bonferroni adjustment to account for the number of investigated seeds). Please see Supplementary Table III for a summary of performed regression analyses.
I observed a powerful confident relationship off Bmi and you can DIS (Roentgen 2 =0.285, P>0.001, Pearson correlation, Second Profile Ia). Multiple regression data revealed an awful relationship regarding Body mass index 2 that have CR (R 2 =0.151, P=0.038, covariate Bmi; Secondary Shape Ib), showing an upside-down U-molded relationships. Urge strength failed to associate with Bmi (R=?0.206, P=0.185, Pearson correlation). We discovered a development out-of an awful correlation between control achievements and Body mass index (R=?0.295, P=0.055, Pearson correlation). Select Desk 1 getting descriptive statistics.
To regulate their craving, every professionals (particularly over weight volunteers) envisioned the fresh new negative long-title outcomes away from food brand new illustrated palatable dining. Really professionals switched ranging from other controls strategies in the course of brand new experiment (discover Secondary Desk IV to own all about approach fool around with). Whenever trained in order to recognize, all professionals thought taste or structure of displayed foods.
Dating between Ambitious craft and you may Body mass index, restaurants behavior, craving strength or subjective regulation profits
Activity in a cluster comprising left putamen, amygdala and insula was nonlinearly (inverted U-shaped) related to BMI during volitional regulation devoid of craving influences (REGULATE_TASTY>ADMIT_TASTY; Table 2, Figure 2). Activation during regulation specific to hedonic food (REGULATE_TASTY>REGULATE_NOT_TASTY) was unrelated to BMI. We found no linear relationships with BMI. Craving intensity correlated positively with activity in the right hippocampus/amygdala during craving devoid of volitional regulatory influences (ADMIT_TASTY>REGULATE_TASTY; Table 2, Supplementary Figure X), but did not correlate with activation during craving specific to hedonic food (ADMIT_TASTY>ADMIT_NOT_TASTY). Neither subjective regulation success nor measures of eating behavior were significantly related to task-related BOLD activity. The above-mentioned results indicate some lateralization of the findings. However, when a less strict threshold was applied, bilateral BOLD activation of all mentioned regions associated with BMI and craving intensity was observed (relationship of BOLD and BMI: t-values thresholded at P<0.05, uncorrected; relationship of BOLD and craving intensity: t-values thresholded at P<0.001, uncorrected).