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Comparative Study
. 2012 May;33(5):1052-61.
doi: 10.1002/hbm.21268. Epub 2011 Apr 21.

The obese brain: association of body mass index and insulin sensitivity with resting state network functional connectivity

Stephanie Kullmann  1 , Martin Heni, Ralf Veit, Caroline Ketterer, Fritz Schick, Hans-Ulrich Häring, Andreas Fritsche, Hubert Preissl
Affiliations
Comparative Study

The obese brain: association of body mass index and insulin sensitivity with resting state network functional connectivity

Stephanie Kullmann et al. Hum Brain Mapp. 2012 May.

Abstract

Obesity is a key risk factor for the development of insulin resistance, Type 2 diabetes and associated diseases; thus, it has become a major public health concern. In this context, a detailed understanding of brain networks regulating food intake, including hormonal modulation, is crucial. At present, little is known about potential alterations of cerebral networks regulating ingestive behavior. We used "resting state" functional magnetic resonance imaging to investigate the functional connectivity integrity of resting state networks (RSNs) related to food intake in lean and obese subjects using independent component analysis. Our results showed altered functional connectivity strength in obese compared to lean subjects in the default mode network (DMN) and temporal lobe network. In the DMN, obese subjects showed in the precuneus bilaterally increased and in the right anterior cingulate decreased functional connectivity strength. Furthermore, in the temporal lobe network, obese subjects showed decreased functional connectivity strength in the left insular cortex. The functional connectivity magnitude significantly correlated with body mass index (BMI). Two further RSNs, including brain regions associated with food and reward processing, did not show BMI, but insulin associated functional connectivity strength. Here, the left orbitofrontal cortex and right putamen functional connectivity strength was positively correlated with fasting insulin levels and negatively correlated with insulin sensitivity index. Taken together, these results complement and expand previous functional neuroimaging findings by demonstrating that obesity and insulin levels influence brain function during rest in networks supporting reward and food regulation.

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Figures

Figure 1
Figure 1
Data analysis overview. Summary sketch of the data analysis steps. Displayed on the right are the applied software packages. Abbreviations: ICs‐Independent Components, BMI‐body mass index, ISI‐insulin sensitivity index.
Figure 2
Figure 2
Contrast of DMN between lean and obese subjects. Color map represents significant (P <0.05, FWE) voxels of altered functional connectivity in obese compared to lean subjects. Color bar represents F‐values. The top scatter plot shows significant negative correlation between the right anterior cingulate cortex (BA24) (x = 3, y = −12, z = 36) and BMI, adjusted for fasting insulin levels. The bottom scatter plot shows significant positive correlation between the left precuneus (x = −3, y = −78, z = 33) and BMI, adjusted for fasting insulin levels.
Figure 3
Figure 3
Contrast of temporal lobe network between lean and obese subjects. Color map represents significant (P < 0.05, FWE) voxels of decreased functional connectivity in obese compared to lean subjects. Color bar represents T‐values. Scatter plot shows significant negative correlation between the left insular cortex (x = −42, y = −6, z = 0) and BMI, adjusted for fasting insulin levels.
Figure 4
Figure 4
Relationship between prefrontal lobe network functional connectivity and fasting insulin levels (loge‐scaled) and insulin sensitivity index (loge‐scaled) in lean and obese subjects. Color map represents significant (P <0.05, FWE) voxels of insulin associated connectivity. Color bar represents T‐values. The left scatter plot shows a significant positive correlation between the left orbitofrontal cortex (x = −30, y = 45, z = −12) and fasting insulin, adjusted for BMI. The right scatter plot shows a significant negative correlation between left orbitofrontal cortex and insulin sensitivity index, adjusted for BMI.
Figure 5
Figure 5
Relationship between basal ganglia network functional connectivity and fasting Insulin levels (loge‐scaled) and insulin sensitivity index (loge‐scaled) in lean and obese subjects. Color map represents significant (P <0.05, FWE) voxels of insulin associated activity. Color bar represents T‐ values. The left scatter plot shows a significant positive correlation between the right putamen (x = 33, y = 0, z = −9) and fasting insulin, adjusted for BMI. The right scatter plot shows a significant negative correlation between the right putamen and insulin sensitivity index, adjusted for BMI.
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References

    1. Aharon I, Etcoff N, Ariely D, Chabris CF, O'Connor E, Breiter HC ( 2001): Beautiful faces have variable reward value: fMRI and behavioral evidence. Neuron 32: 537–551. - PubMed
    1. Ashburner J, Friston KJ ( 2005): Unified segmentation. Neuroimage 26: 839–851. - PubMed
    1. Beckmann CF, DeLuca M, Devlin JT, Smith SM ( 2005): Investigations into resting‐state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 360: 1001–1013. - PMC - PubMed
    1. Bell AJ, Sejnowski TJ ( 1995): An information‐maximization approach to blind separation and blind deconvolution. Neural Comput 7: 1129–1159. - PubMed
    1. Calhoun VD, Adali T, Pearlson GD, Pekar JJ ( 2001): A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14: 140–151. - PMC - PubMed

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