Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention.

MRI biomarker deep learning obesity

Journal

Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065

Informations de publication

Date de publication:
15 Feb 2024
Historique:
revised: 16 11 2023
received: 01 05 2023
accepted: 03 01 2024
medline: 20 2 2024
pubmed: 20 2 2024
entrez: 20 2 2024
Statut: ppublish

Résumé

Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.

Identifiants

pubmed: 38375968
doi: 10.1002/hbm.26595
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e26595

Subventions

Organisme : NIH HHS
ID : U01 AG024904
Pays : United States
Organisme : NIMH NIH HHS
ID : 5R21MH107045
Pays : United States
Organisme : NIMH NIH HHS
ID : R03MH096321
Pays : United States
Organisme : NIMH NIH HHS
ID : K23MH087770
Pays : United States

Informations de copyright

© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Références

Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., & Kim, B. (2018). Sanity checks for saliency maps. https://doi.org/10.48550/ARXIV.1810.03292
Alber, M., Lapuschkin, S., Seegerer, P., Hägele, M., Schütt, K. T., Montavon, G., Samek, W., Müller, K.-R., Dähne, S., & Kindermans, P.-J. (2018). iNNvestigate neural networks! https://doi.org/10.48550/ARXIV.1808.04260
Alford, S., Patel, D., Perakakis, N., & Mantzoros, C. S. (2018). Obesity as a risk factor for Alzheimer's disease: Weighing the evidence. Obesity Reviews, 19(2), 269-280. https://doi.org/10.1111/obr.12629
Arjmand, G., Abbas-Zadeh, M., & Eftekhari, M. H. (2022). Effect of MIND diet intervention on cognitive performance and brain structure in healthy obese women: A randomized controlled trial. Scientific Reports, 12(1), 2871. https://doi.org/10.1038/s41598-021-04258-9
Ashraf, A., Khan, S., Bhagwat, N., Chakravarty, M., & Taati, B. (2018). Learning to unlearn: Building immunity to dataset bias in medical imaging studies. https://doi.org/10.48550/ARXIV.1812.01716
Beheshti, I., Nugent, S., Potvin, O., & Duchesne, S. (2019). Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme. NeuroImage: Clinical, 24, 102063. https://doi.org/10.1016/j.nicl.2019.102063
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Bocancea, D. I., van Loenhoud, A. C., Groot, C., Barkhof, F., van der Flier, W. M., & Ossenkoppele, R. (2021). Measuring resilience and resistance in aging and Alzheimer disease using residual methods: A systematic review and meta-analysis. Neurology, 97(10), 474-488. https://doi.org/10.1212/WNL.0000000000012499
Boutari, C., & Mantzoros, C. S. (2022). A 2022 update on the epidemiology of obesity and a call to action: As its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism, 133, 155217. https://doi.org/10.1016/j.metabol.2022.155217
Chen, E. Y., Eickhoff, S. B., Giovannetti, T., & Smith, D. V. (2020). Obesity is associated with reduced orbitofrontal cortex volume: A coordinate-based meta-analysis. NeuroImage: Clinical, 28, 102420. https://doi.org/10.1016/j.nicl.2020.102420
Chollet, F. (2018). Keras: The python deep learning library. Astrophysics source code library, ascl-180.022.
Chooi, Y. C., Ding, C., & Magkos, F. (2019). The epidemiology of obesity. Metabolism, 92, 6-10. https://doi.org/10.1016/j.metabol.2018.09.005
Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017). Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage, 163, 115-124. https://doi.org/10.1016/j.neuroimage.2017.07.059
Cole, J. H., Ritchie, S. J., Bastin, M. E., Valdés Hernández, M. C., Muñoz Maniega, S., Royle, N., Corley, J., Pattie, A., Harris, S. E., Zhang, Q., Wray, N. R., Redmond, P., Marioni, R. E., Starr, J. M., Cox, S. R., Wardlaw, J. M., Sharp, D. J., & Deary, I. J. (2018). Brain age predicts mortality. Molecular Psychiatry, 23(5), 1385-1392. https://doi.org/10.1038/mp.2017.62
Debette, S., Beiser, A., Hoffmann, U., DeCarli, C., O'Donnell, C. J., Massaro, J. M., Au, R., Himali, J. J., Wolf, P. A., Fox, C. S., & Seshadri, S. (2010). Visceral fat is associated with lower brain volume in healthy middle-aged adults. Annals of Neurology, 68, 136-144. https://doi.org/10.1002/ana.22062
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968-980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297-302. https://doi.org/10.2307/1932409
Elia, M. (2001). Obesity in the elderly. Obesity Research, 9(S11), 244S-248S. https://doi.org/10.1038/oby.2001.126
Espeland, M. A., Erickson, K., Neiberg, R. H., Jakicic, J. M., Wadden, T. A., Wing, R. R., Desiderio, L., Erus, G., Hsieh, M.-K., Davatzikos, C., Maschak-Carey, B. J., Laurienti, P. J., Demos-McDermott, K., Bryan, R. N., & for the Action for Health in Diabetes Brain Magnetic Resonance Imaging (Look AHEAD Brain) Ancillary Study Research Group. (2016). Brain and white matter hyperintensity volumes after 10 years of random assignment to lifestyle intervention. Diabetes Care, 39(5), 764-771. https://doi.org/10.2337/dc15-2230
Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774-781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Franke, K., Luders, E., May, A., Wilke, M., & Gaser, C. (2012). Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI. NeuroImage, 63(3), 1305-1312. https://doi.org/10.1016/j.neuroimage.2012.08.001
García-García, I., Michaud, A., Dadar, M., Zeighami, Y., Neseliler, S., Collins, D. L., Evans, A. C., & Dagher, A. (2019). Neuroanatomical differences in obesity: Meta-analytic findings and their validation in an independent dataset. International Journal of Obesity, 43(5), 943-951. https://doi.org/10.1038/s41366-018-0164-4
Gierach, M., Gierach, J., Ewertowska, M., Arndt, A., & Junik, R. (2014). Correlation between body mass index and waist circumference in patients with metabolic syndrome. ISRN Endocrinology, 2014, 514589. https://doi.org/10.1155/2014/514589
Gorgolewski, K., Burns, C., Madison, C., Clark, D., Halchenko, Y., Waskom, M., & Ghosh, S. (2011). Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics, 5. https://doi.org/10.3389/fninf.2011.00013
Han, Y.-P., Tang, X., Han, M., Yang, J., Cardoso, M. A., Zhou, J., & Simó, R. (2021). Relationship between obesity and structural brain abnormality: Accumulated evidence from observational studies. Ageing Research Reviews, 71, 101445. https://doi.org/10.1016/j.arr.2021.101445
Herrmann, M. J., Tesar, A., Beier, J., Berg, M., & Warrings, B. (2019). Grey matter alterations in obesity: A meta-analysis of whole-brain studies. Obesity Reviews, 20(3), 464-471. https://doi.org/10.1111/obr.12799
Iglesias, J. E., Liu, C.-Y., Thompson, P. M., & Tu, Z. (2011). Robust brain extraction across datasets and comparison with publicly available methods. IEEE Transactions on Medical Imaging, 30(9), 1617-1634. https://doi.org/10.1109/TMI.2011.2138152
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782-790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Kaplan, A., Zelicha, H., Yaskolka Meir, A., Rinott, E., Tsaban, G., Levakov, G., Prager, O., Salti, M., Yovell, Y., Ofer, J., Huhn, S., Beyer, F., Witte, V., Villringer, A., Meiran, N., Emesh, B. T., Kovacs, P., Von Bergen, M., Ceglarek, U., … Shai, I. (2022). The effect of a high-polyphenol Mediterranean diet (green-MED) combined with physical activity on age-related brain atrophy: The dietary intervention randomized controlled trial polyphenols Unprocessed study (DIRECT PLUS). The American Journal of Clinical Nutrition, 115(5), 1270-1281. https://doi.org/10.1093/ajcn/nqac001
Kivipelto, M., Ngandu, T., Fratiglioni, L., Viitanen, M., Kåreholt, I., Winblad, B., Helkala, E.-L., Tuomilehto, J., Soininen, H., & Nissinen, A. (2005). Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Archives of Neurology, 62(10), 1556-1560. https://doi.org/10.1001/archneur.62.10.1556
Kurth, F., Levitt, J. G., Phillips, O. R., Luders, E., Woods, R. P., Mazziotta, J. C., Toga, A. W., & Narr, K. L. (2013). Relationships between gray matter, body mass index, and waist circumference in healthy adults. Human Brain Mapping, 34(7), 1737-1746. https://doi.org/10.1002/hbm.22021
Leong, K. S., & Wilding, J. P. (1999). Obesity and diabetes. Best Practice & Research Clinical Endocrinology & Metabolism, 13(2), 221-237. https://doi.org/10.1053/beem.1999.0017
Levakov, G., Kaplan, A., Yaskolka Meir, A., Rinott, E., Tsaban, G., Zelicha, H., Bluher, M., Ceglarek, U., Stumvoll, M., Shelef, I., Avidan, G., & Shai, I. (2023). The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity. ELife, 12, e83604. https://doi.org/10.7554/elife.83604
Levakov, G., Rosenthal, G., Shelef, I., Raviv, T. R., & Avidan, G. (2020). From a deep learning model back to the brain-identifying regional predictors and their relation to aging. Human Brain Mapping, 41(12), 3235-3252. https://doi.org/10.1002/hbm.25011
Lund, M. J., Alnaes, D., de Lange, A.-M. G., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2022). Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. NeuroImage: Clinical, 33, 102921. https://doi.org/10.1016/j.nicl.2021.102921
Medic, N., Ziauddeen, H., Ersche, K. D., Farooqi, I. S., Bullmore, E. T., Nathan, P. J., Ronan, L., & Fletcher, P. C. (2016). Increased body mass index is associated with specific regional alterations in brain structure. International Journal of Obesity, 40(7), 1177-1182. https://doi.org/10.1038/ijo.2016.42
Moreno-Lopez, L., Contreras-Rodriguez, O., Soriano-Mas, C., Stamatakis, E. A., & Verdejo-Garcia, A. (2016). Disrupted functional connectivity in adolescent obesity. NeuroImage: Clinical, 12, 262-268. https://doi.org/10.1016/j.nicl.2016.07.005
Mori, S., Wakana, S., van Zijl, P. C. M., & Nagae-Poetscher, L. M. (2006). MRI atlas of human white matter. AJNR: American Journal of Neuroradiology, 27(6), 1384-1385.
Nguyen, J. C. D., Killcross, A. S., & Jenkins, T. A. (2014). Obesity and cognitive decline: Role of inflammation and vascular changes. Frontiers in Neuroscience, 8, 375. https://doi.org/10.3389/fnins.2014.00375
Pannacciulli, N., Del Parigi, A., Chen, K., Le, D. S. N. T., Reiman, E. M., & Tataranni, P. A. (2006). Brain abnormalities in human obesity: A voxel-based morphometric study. NeuroImage, 31(4), 1419-1425. https://doi.org/10.1016/j.neuroimage.2006.01.047
Razay, G., Vreugdenhil, A., & Wilcock, G. (2006). Obesity, abdominal obesity and Alzheimer disease. Dementia and Geriatric Cognitive Disorders, 22(2), 173-176. https://doi.org/10.1159/000094586
Reas, D. L., Nygård, J. F., Svensson, E., Sørensen, T., & Sandanger, I. (2007). Changes in body mass index by age, gender, and socio-economic status among a cohort of Norwegian men and women (1990-2001). BMC Public Health, 7(1), 269. https://doi.org/10.1186/1471-2458-7-269
Reinhold, J. C., Dewey, B. E., Carass, A., & Prince, J. L. (2018). Evaluating the impact of intensity normalization on MR image synthesis. https://doi.org/10.48550/ARXIV.1812.04652
Scheen, A. J., & Luyckx, F. H. (2002). Obesity and liver disease. Best Practice & Research Clinical Endocrinology & Metabolism, 16(4), 703-716. https://doi.org/10.1053/beem.2002.0225
Seabrook, L. T., & Borgland, S. L. (2020). The orbitofrontal cortex, food intake and obesity. Journal of Psychiatry and Neuroscience, 45(5), 304-312. https://doi.org/10.1503/jpn.190163
Shefer, G., Marcus, Y., & Stern, N. (2013). Is obesity a brain disease? Neuroscience & Biobehavioral Reviews, 37(10), 2489-2503. https://doi.org/10.1016/j.neubiorev.2013.07.015
Shott, M. E., Cornier, M.-A., Mittal, V. A., Pryor, T. L., Orr, J. M., Brown, M. S., & Frank, G. K. W. (2015). Orbitofrontal cortex volume and brain reward response in obesity. International Journal of Obesity (2005), 39(2), 214-221. https://doi.org/10.1038/ijo.2014.121
Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017). SmoothGrad: Removing noise by adding noise. https://doi.org/10.48550/ARXIV.1706.03825
Spyridaki, E. C., Avgoustinaki, P. D., & Margioris, A. N. (2016). Obesity, inflammation and cognition. Current Opinion in Behavioral Sciences, 9, 169-175. https://doi.org/10.1016/j.cobeha.2016.05.004
Student. (1908). The probable error of a mean. Biometrika, 6(1), 1. https://doi.org/10.2307/2331554
Tommasi, T., Patricia, N., Caputo, B., & Tuytelaars, T. (2017). A deeper look at dataset bias. In G. Csurka (Ed.), Domain adaptation in computer vision applications (pp. 37-55). Springer International Publishing. https://doi.org/10.1007/978-3-319-58347-1_2
Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. https://doi.org/10.1109/TMI.2010.2046908
Uranga, R. M., & Keller, J. N. (2019). The complex interactions between obesity, metabolism and the brain. Frontiers in Neuroscience, 13, 513. https://doi.org/10.3389/fnins.2019.00513
Vakli, P., Deák-Meszlényi, R. J., Auer, T., & Vidnyánszky, Z. (2020). Predicting body mass index from structural MRI brain images using a deep convolutional neural network. Frontiers in Neuroinformatics, 14, 10. https://doi.org/10.3389/fninf.2020.00010
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., Yu, T., & Scikit-Image Contributors. (2014). scikit-image: Image processing in python. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453
Veit, R., Kullmann, S., Heni, M., Machann, J., Häring, H.-U., Fritsche, A., & Preissl, H. (2014). Reduced cortical thickness associated with visceral fat and BMI. NeuroImage: Clinical, 6, 307-311. https://doi.org/10.1016/j.nicl.2014.09.013
Wachinger, C., Rieckmann, A., & Pölsterl, S. (2021). Detect and correct bias in multi-site neuroimaging datasets. Medical Image Analysis, 67, 101879. https://doi.org/10.1016/j.media.2020.101879
Wang, M., Li, Y., Cong, L., Hou, T., Luo, Y., Shi, L., Chang, L., Zhang, C., Wang, Y., Wang, X., Du, Y., & Qiu, C. (2021). High-density lipoprotein cholesterol and brain aging amongst rural-dwelling older adults: A population-based magnetic resonance imaging study. European Journal of Neurology, 28(9), 2882-2892. https://doi.org/10.1111/ene.14939
Ward, M. A., Bendlin, B. B., Mclaren, D. G., Hess, T. M., Gallagher, C. L., Kastman, E. K., Rowley, H. A., Asthana, S., Carlsson, C. M., Sager, M. A., & Johnson, S. C. (2010). Low HDL cholesterol is associated with lower gray matter volume in cognitively healthy adults. Frontiers in Aging Neuroscience., 2, 29. https://doi.org/10.3389/fnagi.2010.00029
Weiss, R., Dziura, J., Burgert, T. S., Tamborlane, W. V., Taksali, S. E., Yeckel, C. W., Allen, K., Lopes, M., Savoye, M., Morrison, J., Sherwin, R. S., & Caprio, S. (2004). Obesity and the metabolic syndrome in children and adolescents. New England Journal of Medicine, 350(23), 2362-2374. https://doi.org/10.1056/NEJMoa031049
Whitmer, R. A., Gunderson, E. P., Barrett-Connor, E., Quesenberry, C. P., & Yaffe, K. (2005). Obesity in middle age and future risk of dementia: A 27 year longitudinal population based study. BMJ, 330(7504), 1360. https://doi.org/10.1136/bmj.38446.466238.E0
Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., Beckmann, C., Jenkinson, M., & Smith, S. M. (2009). Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45(1), S173-S186. https://doi.org/10.1016/j.neuroimage.2008.10.055
Yadav, C., & Razavian, N. (2019). Using brain MRI images to predict memory, BMI & age. 2019 IEEE International Conference on Humanized Computing and Communication, 126-128. https://doi.org/10.1109/HCC46620.2019.00026
Yaskolka Meir, A., Rinott, E., Tsaban, G., Zelicha, H., Kaplan, A., Rosen, P., Shelef, I., Youngster, I., Shalev, A., Blüher, M., Ceglarek, U., Stumvoll, M., Tuohy, K., Diotallevi, C., Vrhovsek, U., Hu, F., Stampfer, M., & Shai, I. (2021). Effect of green-Mediterranean diet on intrahepatic fat: The DIRECT PLUS randomised controlled trial. Gut, 70(11), 2085-2095. https://doi.org/10.1136/gutjnl-2020-323106

Auteurs

Ofek Finkelstein (O)

Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Gidon Levakov (G)

Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Alon Kaplan (A)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
The Chaim Sheba Medical Center, Tel Hashomer, Ramat-Gan, Israel.

Hila Zelicha (H)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.

Anat Yaskolka Meir (AY)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.

Ehud Rinott (E)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.

Gal Tsaban (G)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Soroka University Medical Center, Beer Sheva, Israel.

Anja Veronica Witte (AV)

Department of Neurology, Max Planck-Institute for Human Cognitive and Brain Sciences, and Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany.

Matthias Blüher (M)

Department of Medicine, University of Leipzig, Leipzig, Germany.

Michael Stumvoll (M)

Department of Medicine, University of Leipzig, Leipzig, Germany.

Ilan Shelef (I)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Soroka University Medical Center, Beer Sheva, Israel.

Iris Shai (I)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Tammy Riklin Raviv (T)

The School of Electrical and Computer Engineering, Ben Gurion University of the Negev, Beer Sheva, Israel.

Galia Avidan (G)

Department of Psychology, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Classifications MeSH