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Your search keyword '"Sinha, Rajita"' showing total 9 results
Start Over Author "Sinha, Rajita" Publisher american diabetes association
9 results on '"Sinha, Rajita"'

1. Neural correlates of stress- and food cue-induced food craving in obesity: association with insulin levels

Author

Jastreboff, Ania M., Sinha, Rajita, Lacadie, Cheryl, Small, Dana M., Sherwin, Robert S., and Potenza, Marc N.

Subjects
Obesity -- Complications and side effects, Insulin resistance -- Risk factors -- Research, Insulin -- Physiological aspects -- Research, Food habits -- Physiological aspects -- Research, Health
Abstract

OBJECTIVE--Obesity is associated with alterations in corticolimbic-striatal brain regions involved in food motivation and reward. Stress and the presence of food cues may each motivate eating and engage corticolimibic-striatal neurocircuitry. [...]

Published
2013
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2. 145-OR: GLP-1 Analogue Effects on Neural Responses to High-Fructose Corn Syrup and Eating Behavior in Obesity

Author

JOSEPH, KELLY, primary, LEVENTHAL, JESSICA, additional, HWANG, SEUNGJU, additional, KNIGHT, MARI-LYNET, additional, SEO, DONGJU, additional, LACADIE, CHERYL, additional, VAN NAME, MICHELLE A., additional, CAPRIO, SONIA, additional, SHERR, JENNIFER, additional, TAMBORLANE, WILLIAM V., additional, SINHA, RAJITA, additional, and JASTREBOFF, ANIA M., additional

Published
2020
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4. Short-Term Weight Loss Undermines Brain Regulation of Food Consumption

Author

LAM, WAI, primary, SEO, DONGJU, additional, LACADIE, CHERYL, additional, SCHMIDT, CHRISTIAN P., additional, ROSENBERG, SAMUEL, additional, HOANG, HAI, additional, HWANG, JANICE, additional, CONSTABLE, TODD, additional, SAVOYE, MARY, additional, SINHA, RAJITA, additional, SHERWIN, ROBERT, additional, and BELFORT-DEAGUIAR, RENATA, additional

Published
2018
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6. Hyperinsulinemia Invokes Greater Brain Perfusion in Reward and Motivation Regions in Obesity

Author

GROSKREUTZ, DEREK, primary, SINHA, RAJITA, additional, LACADIE, CHERYL, additional, LAM, WAI, additional, HAMZA, MUHAMMAD, additional, SCHMIDT, CHRISTIAN P., additional, SEO, DONGJU, additional, HWANG, JANICE, additional, BELFORT-DEAGUIAR, RENATA, additional, LEVENTHAL, JESSICA, additional, ROSENBERG, SAMUEL, additional, PARIKH, LISA, additional, CONSTABLE, TODD, additional, SHERWIN, ROBERT, additional, and JASTREBOFF, ANIA M., additional

Published
2018
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7. Altered Brain Response to Drinking Glucose and Fructose in Obese Adolescents

Author

Jastreboff, Ania M., primary, Sinha, Rajita, additional, Arora, Jagriti, additional, Giannini, Cosimo, additional, Kubat, Jessica, additional, Malik, Saima, additional, Van Name, Michelle A., additional, Santoro, Nicola, additional, Savoye, Mary, additional, Duran, Elvira J., additional, Pierpont, Bridget, additional, Cline, Gary, additional, Constable, R. Todd, additional, Sherwin, Robert S., additional, and Caprio, Sonia, additional

Published
2016
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9. 275-LB: Connectome Predictive Modeling May Identify Brain Connectivity Signatures to Help Predict Who Will Benefit from Low-Calorie Diet.

Author

GROSKREUTZ, DEREK, LAM, WAI, LACADIE, CHERYL, ELSHAFIE, AHMED, HWANG, JANICE J., SEO, DONGJU, SAVOYE, MARY, SINHA, RAJITA, CONSTABLE, TODD, SHERWIN, ROBERT, and BELFORT-DEAGUIAR, RENATA

Abstract

While studies have investigated changes in brain connectivity following diet and weight loss, it has not yet been explored whether baseline brain connectivity can be used to predict weight loss during diet or to identify individual subjects for which a particular diet may be effective. The following study uses Connectome-based predictive modeling (CPM), a data-driven analysis approach for producing predictive models of individual brain-behavior relationships from brain connectivity data using cross-validation, to help identify individual participants who will lose weight after an 8-week low calorie diet. 16 Healthy OB subjects (10F/6M, age 44.4±8 years, BMI 32.7±2) and 9 T2DM subjects (5F/4M, age 48±9, BMI 33.9±2) underwent functional MRI (fMRI) during a hyperglycemic-euglycemic clamp. Blood-oxygen-level-dependent (BOLD) brain activity was assessed while subjects viewed food (high-calorie, low-calorie) pictures and non-food images. The study was repeated after 8 weeks of a reduced calorie diet. CPM was applied to BOLD activity during hyperglycemia and euglycemia. Analyses show that models built on individual subject brain connectivity matrices averaged between hyperglycemia and euglycemia before an 8-week low calorie diet are able to predict 50.2% and 53.4% of the variance in BMI and weight loss (kg), respectively (p<0.05). While models built on brain connectivity matrices during euglycemia (similar to the post-prandial state), but not hyperglycemia predicted 42.2% of the variance in BMI change (p=0.017). These results suggest the possibility that individual brain connectivity signatures may be used to identify subjects for whom a low calorie diet might be an effective weight loss strategy, while also revealing the relevant functional connections associated with weight loss success. Disclosure: D. Groskreutz: None. W. Lam: None. C. Lacadie: None. A. Elshafie: None. J.J. Hwang: None. D. Seo: None. M. Savoye: None. R. Sinha: None. T. Constable: None. R. Sherwin: Other Relationship; Self; ICON plc., IQVIA, MannKind Corporation. R. Belfort-DeAguiar: Research Support; Self; Silver Palate Kitchens, Inc. Funding: National Institutes of Health [ABSTRACT FROM AUTHOR]

Published
2019
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