FMI Studies in Obesity

FMI Studies in Obesity Obesity: insights from fMRI imaging studies Abstract One-third of the US adult population is obese. Obesity is associated with serious medical complications and costs a lot of money. In my paper, we will study this phenomena with fMRI (functional magnetic resonance imaging) when subjects were at resting state (subjects were instructed simply to keep their eyes closed and to not think of anything in particular). Before this, we postulate that there will be disruption in neural circuits, which result in obesity. There are four circuits that we mainly focus on: (a) reward, located in the nucleus accumbens (NAc) and the ventral pallidum; (b) motivation/drive, located in the orbitofrontal cortex (OFC) and the subcallosal cortex; (c) memory and learning, located in the amygdala and the hippocampus; and (d) control, located in the prefrontal cortex and the anterior cingulate gyrus (CG). We used SPM, which based on Matlab, to analyse our data, and processed the results by GCA (Granger causality analysis). Through it, we will get the connection between two ROIs(region of interesting). Key words: obesity; fMRI; resting state; GCA(Granger causality analysis) Introduction Obesity is a global problem with the improvement of our life. There are one-third of US adult population who is obese, whose body mass index(BMI)≠¥30 kg*m-2.[1] Undoubtedly, obesity costs a lot because it is associated with serious diseases(e.g. diabetes, heart disease, fatty liver and some cancers)[2,3]. What lead to obesity are complex and ambiguity, such as social and cultural factors, environments that promote unhealthy eating habits and physical inactivity, individual factors, etc [4]. Obesity with long time can result in function changes in human brain, but we do not know how this works. New imaging technologies such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have provided new ways to investigate the relationship of human brain. Much of previous studies were based on PET images which focus on the brain dopamine system, they found that there are different between normal and obese in DA system. Methods Based on previous studies, we study function changes of human brain by fMRI. On account of discover that others found before, we support some hypotheses that obesity can result in difference between normal and obese. We focused on four brain circuits in our research which were discussed in drug-addiction. As well, what we found maybe provide a method to treat obesity. There are two groups, one is normal and the other is obesity. The information of subjects are showed in Figure1. In our experiment, there were three groups, which were obesity before surgery and after one month, compared with the normal control. We collected data form TangDu Hospital. During scanning, subjects were asked for closing eyes Figure1:  Subjects Information. StandardDeviation(STD). Body Mass Index(BMI). Yale Food Addiction scale(YFAS). Sympton Check List 90(SCL90). without thinking anything but waking. Before scanning, they filled a questionnaire, which include depression, anxiety, and so on, to see their mental state. In this address, we just analysed the data that scanned before surgery and normal control. Obesity involved multiple brain circuits Obesity can result in multiple variation, however, there are four circuits which are very important in obesity: (a) reward, located in the nucleus accumbens (NAc) and the ventral pallidum; (b) motivation/drive, located in the orbitofrontal cortex (OFC) and the subcallosal cortex; (c) memory and learning, located in the amygdala and the hippocampus; and (d) control, located in the prefrontal cortex and the anterior cingulate gyrus (CG). These circuits work together and change with experience[5,6]. Moreover, there are other circuits involved in obesity which we will study further. Four circuits in obesity We processed data through SPM,[7] which is based on matlab. After that, we analysed the data by GCA(Granger Casual Analysis)[8]. At resting state, we found that there was abnormal in motivation/drive circuit which receded in obesity than normal (figure2). Because of the exception of motivation, it could lead to disorder of other circuits. In reward circuit, the saliency value to food stimulation was reset in obesity, which resulted in overeating to reach satisfy. Circuit of memory and learning maybe influence individual habit. When somebody who is overweight saw food or some place for more time. He or she would remember the stimulation and when he or she met it again, it would arouse memory to drive someone to get it. We also found that there was reduction in control circuit. Due to this reduction, obesity can not control their eating behavior very well even if they were full. Based   Figure2:Result of GCA [10] . on these disorder, we postulate that long-term obesity destroyed human brain function  through the top-down modulation[9]. Vulnerability to obesity A challenging problem in the neurobiology of obesity is to understand why some individuals become obese while others do not. Genetic factors are estimated to contribute between 45% and 85% of the variability in BMI [3,4]. Beyond that, we hypothesize that decreased sensitivity of reward circuit and the disorder of control circuit in obesity would lead a subject more or less vulnerable to food. At the same time, the environment that subjects could get high calorie food was significant. Because of these findings, obese could lose fat by intervening the brain circuits or controlling the environment around obesity. Discussion In our study, we just focused on four brain circuits. There were other circuits which involved in obesity. In further study, we would find them and research their function. In the future, our discover maybe be applied to clinic treatment with less wounds. There are some limitation in our experiment, for example, we didn’t get the result of fMRI what was gotten when subjects were at tasking state(somebody lies on bed with seeing images which are about low or high calorie food). In the further research, we will combine these result and obtain an excellent conclusion. Acknowledgments This work was supported by National Natural Science Foundation of China. My tutor gave me help on many fronts. I have to appreciate upperclassmen who gave me help too. The doctors who scanned subjects, the subjects who coordinated our experiment and the authors who provided idea should be given great appreciation. Finally, thanking Dr. Karen for teaching me how to address our experiment. References [1] N.D. Volkow, Gene-Jack Wang and R.D. Baler, Reward, dopamine and the control of food intake: implications for obesity, Trends in Cognitive Sciences January 2011, Vol. 15, No. 1. [2] Finkelstein, E.A.et al.(2009) Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff.28, w822–w831 [3] Baessler, A.et al.(2005) Genetic linkage and association of the growth hormone secretagogue receptor (ghrelin receptor) gene in human obesity. Diabetes 54, 259–267 [4] Silventoinen, K. and Kaprio, J. (2009) Genetics of tracking of body mass index from birth to late middle age: evidence from twin and family studies. Obes. Facts 2, 196–202 [5] N. D. Volkow, J. S. Fowler, and Gene-Jack Wang,(2003) The addicted human brain: insights from imaging studies, PERSPECTIVE SERIES, 111:1444–1451. [6] N.D. Volkow, B. Rosen, and L.Farde, 1997. Imaging the living human brain: magnetic resonance imaging and positron emission tomography. Proc. Natl. Acad. Sci. U. S. A. 94:2787–2788. [7] ReHofMRI1.0 (by Dr. HE Yong, free download from http://www.bic.mni.mcgill.ca/users/yonghe) [8] Mingzhou Ding, Yonghong Chen, Steven L. Bressler, Granger Causality: Basic Theory and Application‎ to ‎Neuroscience†, Preprint submitted to Elsevier Science,2008.02.07. [9] Wen X, Liu Y, Yao L, Ding M (2013) Top-down regulation of default mode activity in spatial visual attention. J Neurosci 33(15): 6444 –6453 [10] Mingzhou Ding, Yonghong Chen,(2006) Granger Causality: Basic Theory and Application to Neuroscience, Preprint submitted to Elsevier Science, arXiv:q-bio/0608035v1 Cai Weiwei