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8. Dual GIP–GLP1-Receptor Agonists In The Treatment Of Type 2 Diabetes: A Short Review On Emerging Data And Therapeutic Potential

Author

Bastin M and Andreelli F

Subjects
incretins, type 2 diabetes, co-agonists, GLP-1, GIP, body weight, Specialties of internal medicine, RC581-951
Abstract

Marie Bastin, Fabrizio Andreelli Diabetology-Metabolism Department, Sorbonne Université, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, F-75013, FranceCorrespondence: Fabrizio AndreelliDiabetology-Metabolism Department, Pitié-Salpêtrière Hospital, 47-83 Boulevard de l’Hôpital, Paris cedex 13, 75951, FranceEmail fabrizio.andreelli@aphp.frAbstract: The need for efficient and safe therapy to improve such metabolic diseases as obesity and type 2 diabetes mellitus is currently unmet. The development of dual GIPR–GLP1R coagonists that bind to either one or the other receptor (sequence-mixed dual agonists) has emerged as an innovative therapeutic strategy for obesity and type 2 diabetes. Combined activation of both receptors may act synergistically providing additive effects on glucose and body weight in comparison of GLP1 analogues alone. Preclinical studies have confirmed that GIPR–GLP1R coagonists improve several hallmarks of metabolic syndrome, such as obesity, hyperglycemia, and dyslipidemia. These metabolic benefits have been translated from mice to nonhuman primates and humans. Recent clinical trials have shown that coagonists induce significant benefits on body weight, fasting, and postprandial glucose levels, insulin sensitivity, and total cholesterol. Combined GIP- and GLP1R activators have the potential to become a treatment option for patients with type 2 diabetes.Keywords: incretins, type 2 diabetes, coagonists, GLP1, GIP, body weight

Published
2019

12. Optimal control of a NASCAR – specification race car.

Author

Limebeer, D. J. N., Bastin, M., Warren, E., and Fensham, H. G.

Subjects
*AUTOMOBILE racing drivers, *PERFORMANCE of tires, *RACING automobiles, *CURVATURE
Abstract

This paper adapts the minimum-lap-time simulation technologies developed in Formula One to NASCAR. The adaptation process requires recognition of the variable lateral curvature of some tracks, as well as the asymmetries of NASCAR-specification race cars that have been configured for high-speed oval tracks that are dominated by left-hand cornering. These asymmetries include different suspension characteristics on the left- and right-hand ends of each axle, different left- and right-hand tyres on each axle, as well as asymmetries in the car's aerodynamic performance. The results compare optimal control calculations, industrial simulation-based predictions and race-captured telemetry data. For the most part, these various data sources are in good agreement, with the importance of variable lateral camber highlighted. It is demonstrated how race car drivers have to find the best compromise between the conflicting objectives of exploiting the additional camber available on the outside of variable-camber tracks and increasing the travelled distance and racing line cornering curvature. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Cereb Cortex

Author

Shin, J., Ma, S., Hofer, E., Patel, Y., VOSBERG, D. E., TILLEY, S., ROSHCHUPKIN, G. V., SOUSA, A. M. M., JIAN, X., GOTTESMAN, R., MOSLEY, T. H., FORNAGE, M., SABA, Y., PIRPAMER, L., Schmidt, R., Schmidt, H., CARRION-CASTILLO, A., Crivello, F., Mazoyer, B., BIS, J. C., Li, S., Yang, Q., LUCIANO, M., Karama, S., Lewis, L., BASTIN, M. E., HARRIS, M. A., WARDLAW, J. M., DEARY, I. E., Scholz, M., Loeffler, M., WITTE, A. V., Beyer, F., VILLRINGER, A., ARMSTRONG, N. J., MATHER, K. A., Ames, D., Jiang, J., KWOK, J. B., SCHOFIELD, P. R., THALAMUTHU, A., TROLLOR, J. N., WRIGHT, M. J., BRODATY, H., Wen, W., SACHDEV, P. S., TERZIKHAN, N., EVANS, T. E., ADAMS, Hhhh, Ikram, M. A., FRENZEL, S., AUWERA-PALITSCHKA, S. V., WITTFELD, K., BULOW, R., Grabe, H. J., Tzourio, Christophe, Mishra, Aniket, MAINGAULT, S., Debette, Stéphanie, GILLESPIE, N. A., FRANZ, C. E., KREMEN, W. S., Ding, L., JAHANSHAD, N., Sestan, N., PAUSOVA, Z., SESHADRI, S., PAUS, T., Bordeaux population health (BPH), and Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
endocrine system, VINTAGE, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Abstract

We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade—albeit in a very limited manner—to behaviors as complex as the choice of one’s occupation.

Published
2020

27. Genetic architecture of subcortical brain structures in 38,854 individuals worldwide

Author

Satizabal, C., Adams, H., Hibar, D., White, C., Knol, M., Stein, J., Scholz, M., Sargurupremraj, M., Jahanshad, N., Roshchupkin, G., Smith, A., Bis, J., Jian, X., Luciano, M., Hofer, E., Teumer, A., Van der Lee, S., Yang, J., Yanek, L., Lee, T., Li, S., Hu, Y., Koh, J., Eicher, J., Desrivières, S., Arias-Vasquez, A., Chauhan, G., Athanasiu, L., Renteria, M., Kim, S., Höhn, D., Armstrong, N., Chen, Q., Holmes, A., Den Braber, A., Kloszewska, I., Andersson, M., Espeseth, T., Grimm, O., Abramovic, L., Alhusaini, S., Milaneschi, Y., Papmeyer, M., Axelsson, T., Ehrlich, S., Roiz-Santiañez, R., Kraemer, B., Håberg, A., Jones, H., Pike, G., Stein, D., Stevens, A., Bralten, J., Vernooij, M., Harris, T., Filippi, I., Witte, A., Guadalupe, T., Wittfeld, K., Mosley, T., Becker, J., Doan, N., Hagenaars, S., Saba, Y., Cuellar-Partida, G., Amin, N., Hilal, S., Nho, K., Karbalai, N., Arfanakis, K., Becker, D., Ames, D., Goldman, A., Lee, P., Boomsma, D., Lovestone, S., Giddaluru, S., Le Hellard, S., Mattheisen, M., Bohlken, M., Kasperaviciute, D., Schmaal, L., Lawrie, S., Agartz, I., Walton, E., Tordesillas-Gutierrez, D., Davies, G., Shin, J., Ipser, J., Vinke, L., Hoogman, M., Jia, T., Burkhardt, R., Klein, M., Crivello, F., Janowitz, D., Carmichael, O., Haukvik, U., Aribisala, B., Schmidt, H., Strike, L., Cheng, C., Risacher, S., Pütz, B., Fleischman, D., Assareh, A., Mattay, V., Buckner, R., Mecocci, P., Dale, A., Cichon, S., Boks, M., Matarin, M., Penninx, B., Calhoun, V., Chakravarty, M., Marquand, A., Macare, C., Masouleh, S., Oosterlaan, J., Amouyel, P., Hegenscheid, K., Rotter, J., Schork, A., Liewald, D., De Zubicaray, G., Wong, T., Shen, L., Sämann, P., Brodaty, H., Roffman, J., De Geus, E., Tsolaki, M., Erk, S., Van Eijk, K., Cavalleri, G., Van der Wee, N., McIntosh, A., Gollub, R., Bulayeva, K., Bernard, M., Richards, J., Himali, J., Loeffler, M., Rommelse, N., Hoffmann, W., Westlye, L., Valdés Hernández, M., Hansell, N., Van Erp, T., Wolf, C., Kwok, J., Vellas, B., Heinz, A., Olde Loohuis, L., Delanty, N., Ho, B., Ching, C., Shumskaya, E., Singh, B., Hofman, A., Van der Meer, D., Homuth, G., Psaty, B., Bastin, M., Montgomery, G., Foroud, T., Reppermund, S., Hottenga, J., Simmons, A., Meyer-Lindenberg, A., Cahn, W., Whelan, C., Van Donkelaar, M., Yang, Q., Hosten, N., Green, R., Thalamuthu, A., Mohnke, S., Hulshoff Pol, H., Lin, H., Jack Jr., C., Schofield, P., Mühleisen, T., Maillard, P., Potkin, S., Wen, W., Fletcher, E., Toga, A., Gruber, O., Huentelman, M., Smith, G., Launer, L., Nyberg, L., Jönsson, E., Crespo-Facorro, B., Koen, N., Greve, D., Uitterlinden, A., Weinberger, D., Steen, V., Fedko, I., Groenewold, N., Niessen, W., Toro, R., Tzourio, C., Longstreth Jr., W., Ikram, M., Smoller, J., Van Tol, M., Sussmann, J., Paus, T., Lemaître, H., Schroeter, M., Mazoyer, B., Andreassen, O., Holsboer, F., Depondt, C., Veltman, D., Turner, J., Pausova, Z., Schumann, G., Van Rooij, D., Djurovic, S., Deary, I., McMahon, K., Müller-Myhsok, B., Brouwer, R., Soininen, H., Pandolfo, M., Wassink, T., Cheung, J., Wolfers, T., Martinot, J., Zwiers, M., Nauck, M., Melle, I., Martin, N., Kanai, R., Westman, E., Kahn, R., Sisodiya, S., White, T., Saremi, A., Van Bokhoven, H., Brunner, H., Völzke, H., Wright, M., Van 't Ent, D., Nöthen, M., Ophoff, R., Buitelaar, J., Fernández, G., Sachdev, P., Rietschel, M., Van Haren, N., Fisher, S., Beiser, A., Francks, C., Saykin, A., Mather, K., Romanczuk-Seiferth, N., Hartman, C., DeStefano, A., Heslenfeld, D., Weiner, M., Walter, H., Hoekstra, P., Nyquist, P., Franke, B., Bennett, D., Grabe, H., Johnson, A., Chen, C., Van Duijn, C., Lopez, O., Fornage, M., Wardlaw, J., Schmidt, R., DeCarli, C., De Jager, P., Villringer, A., Debette, S., Gudnason, V., Medland, S., Shulman, J., Thompson, P., and Seshadri, S.

Subjects
nervous system
Abstract

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

Published
2019

29. Polygenic risk score for schizophrenia and structural brain connectivity in older age: A longitudinal connectome and tractography study

Author

Alloza, C., Cox, S. R., Blesa Cábez, M., Redmond, P, Whalley, H C, Ritchie, S J, Muñoz Maniega, S, del C Valdés Hernández, M, Tucker-Drob, E M, Lawrie, S M, Wardlaw, J M, Deary, I J, and Bastin, M E

Subjects
Multifactorial Inheritance, longitudinal, Structural connectivity, Brain, White Matter, Article, schizophrenia, Ageing, Cross-Sectional Studies, Diffusion Tensor Imaging, ageing, Risk Factors, Schizophrenia, Longitudinal, Genetics, Connectome, Humans, structural connectivity, genetics, Longitudinal Studies, Nerve Net, Aged
Abstract

Higher polygenic risk score for schizophrenia (szPGRS) has been associated with lower cognitive function and might be a predictor of decline in brain structure in apparently healthy populations. Age-related declines in structural brain connectivity—measured using white matter diffusion MRI —are evident from cross-sectional data. Yet, it remains unclear how graph theoretical metrics of the structural connectome change over time, and whether szPGRS is associated with differences in ageing-related changes in human brain connectivity. Here, we studied a large, relatively healthy, same-year-of-birth, older age cohort over a period of 3 years (age ∼ 73 years, N = 731; age ∼76 years, N = 488). From their brain scans we derived tract-averaged fractional anisotropy (FA) and mean diffusivity (MD), and network topology properties. We investigated the cross-sectional and longitudinal associations between these structural brain variables and szPGRS. Higher szPGRS showed significant associations with longitudinal increases in MD in the splenium (β = 0.132, pFDR = 0.040), arcuate (β = 0.291, pFDR = 0.040), anterior thalamic radiations (β = 0.215, pFDR = 0.040) and cingulum (β = 0.165, pFDR = 0.040). Significant declines over time were observed in graph theory metrics for FA-weighted networks, such as mean edge weight (β = −0.039, pFDR = 0.048) and strength (β = −0.027, pFDR = 0.048). No significant associations were found between szPGRS and graph theory metrics. These results are consistent with the hypothesis that szPGRS confers risk for ageing-related degradation of some aspects of structural connectivity., Highlights • Large longitudinal MRI study of brain structure in narrow age range older age cohort. • Longitudinal changes occur in white matter tracts from age 73 to 76. • Graph theory metrics also showed longitudinal reductions over that period. • Microstructural changes in white matter correlate with changes in brain’s network. • Higher genetic risk for schizophrenia is associated with changes in brain structure.

Published
2018

42. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Author

Chauhan, G., Adams, H., Satizabal, C., Bis, J., Teumer, A., Sargurupremraj, M., Hofer, E., Trompet, S., Hilal, S., Smith, A., Jian, X., Malik, R., Traylor, M., Pulit, S., Amouyel, P., Mazoyer, B., Zhu, Y., Kaffashian, S., Schilling, S., Beecham, G., Montine, T., Schellenberg, G., Kjartansson, O., Guethnason, V., Knopman, D., Griswold, M., Windham, B., Gottesman, R., Mosley, T., Schmidt, R., Saba, Y., Schmidt, H., Takeuchi, F., Yamaguchi, S., Nabika, T., Kato, N., Rajan, K., Aggarwal, N., De Jager, P., Evans, D., Psaty, B., Rotter, J., Rice, K., Lopez, O., Liao, J., Chen, C., Cheng, C., Wong, T., Ikram, M., van der Lee, S., Amin, N., Chouraki, V., DeStefano, A., Aparicio, H., Romero, J., Maillard, P., DeCarli, C., Wardlaw, J., Hernandez, M., Luciano, M., Liewald, D., Deary, I., Starr, J., Bastin, M., Munoz Maniega, S., Slagboom, P., Beekman, M., Deelen, J., Uh, H., Lemmens, R., Brodaty, H., Wright, M., Ames, D., Boncoraglio, G., Hopewell, J., Beecham, A., Blanton, S., Wright, C., Sacco, R., Wen, W., Thalamuthu, A., Armstrong, N., Chong, E., Schofield, P., Kwok, J., van der Grond, J., Stott, D., Ford, I., Jukema, J., Vernooij, M., Hofman, A., Uitterlinden, A., van der Lugt, A., Wittfeld, K., Grabe, H., Hosten, N., von Sarnowski, B., Volker, U., Levi, C., Jimenez-Conde, J., Sharma, P., Sudlow, C., Rosand, J., Woo, D., Cole, J., Meschia, J., Slowik, A., Thijs, V., Lindgren, A., Melander, O., Grewal, R., Rundek, T., Rexrode, K., Rothwell, P., Arnett, D., Jern, C., Johnson, J., Benavente, O., Wasssertheil-Smoller, S., Lee, J., Wong, Q., Mitchell, B., Rich, S., McArdle, P., Geerlings, M., van der Graaf, Y., de Bakker, P., Asselbergs, F., Srikanth, V., Thomson, R., McWhirter, R., Moran, C., Callisaya, M., Phan, T., Rutten-Jacobs, L., Bevan, S., Tzourio, C., Mather, K., Sachdev, P., van Duijn, C., Worrall, B., Dichgans, M., Kittner, S., Markus, H., Fornage, M., Launer, L., Seshadri, S., Longstreth, W., Debette, S., Stroke Genetics Network, t., the Neurology Working Group of the Cohorts for, H., and Aging Research in Genomic Epidemiology, C.

Abstract

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 x 10(-8); and LINC00539/ZDHHC20, p = 5.82 x 10(-9). Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 x 10(-25); p [SSBI] = 5.23 x 10(-14) for hypertension), smoking (p [BI] = 4.4 x 10(-10); p [SSBI] = 1.2 x 10(-4)), diabetes (p [BI] = 1.7 x 10(-8); p [SSBI] = 2.8 x 10(-3)), previous cardiovascular disease (p [BI] = 1.0 x 10(-18); p [SSBI] = 2.3 x 10(-7)), stroke (p [BI] = 3.9 x 10(-69); p [SSBI] = 3.2 x 10(-24)), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 x 10(-157); p [SSBI] = 3.16 x 10(-106)), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p

Published
2019

43. Genetic architecture of subcortical brain structures in 38,851 individuals

Author

SATIZABAL, C. L., ADAMS, H. H. H., HIBAR, D. P., WHITE, C. C., KNOL, M. J., STEIN, J. L., Scholz, M., Sargurupremraj, Muralidharan, JAHANSHAD, N., ROSHCHUPKIN, G. V., SMITH, A. V., BIS, J. C., JIAN, X., LUCIANO, M., Hofer, E., TEUMER, A., VAN DER LEE, S. J., Yang, J., YANEK, L. R., LEE, T. V., Li, S., Hu, Y., KOH, J. Y., EICHER, J. D., DESRIVIERES, S., ARIAS-VASQUEZ, A., Chauhan, G., ATHANASIU, L., RENTERIA, M. E., Kim, S., HOEHN, D., ARMSTRONG, N. J., Chen, Q., HOLMES, A. J., DEN BRABER, A., KLOSZEWSKA, I., Andersson, M., ESPESETH, T., Grimm, O., ABRAMOVIC, L., ALHUSAINI, S., MILANESCHI, Y., PAPMEYER, M., AXELSSON, T., Ehrlich, S., ROIZ-SANTIANEZ, R., KRAEMER, B., HABERG, A. K., JONES, H. J., Pike, G. B., STEIN, D. J., Stevens, A., BRALTEN, J., VERNOOIJ, M. W., HARRIS, T. B., FILIPPI, I., WITTE, A. V., Guadalupe, T., WITTFELD, K., MOSLEY, T. H., BECKER, J. T., DOAN, N. T., HAGENAARS, S. P., SABA, Y., CUELLAR-PARTIDA, G., Amin, N., HILAL, S., NHO, K., MIRZA-SCHREIBER, N., ARFANAKIS, K., BECKER, D. M., Ames, D., GOLDMAN, A. L., LEE, P. H., Boomsma, D. I., LOVESTONE, S., GIDDALURU, S., LE HELLARD, S., Mattheisen, M., BOHLKEN, M. M., KASPERAVICIUTE, D., SCHMAAL, L., LAWRIE, S. M., Agartz, I., Walton, E., TORDESILLAS-GUTIERREZ, D., DAVIES, G. E., Shin, J., IPSER, J. C., VINKE, L. N., HOOGMAN, M., Jia, T., BURKHARDT, R., Klein, M., Crivello, Fabrice, JANOWITZ, D., CARMICHAEL, O., HAUKVIK, U. K., ARIBISALA, B. S., Schmidt, H., STRIKE, L. T., CHENG, C. Y., RISACHER, S. L., PUTZ, B., FLEISCHMAN, D. A., ASSAREH, A. A., MATTAY, V. S., BUCKNER, R. L., MECOCCI, P., DALE, A. M., Cichon, S., BOKS, M. P., MATARIN, M., PENNINX, Bwjh, CALHOUN, V. D., CHAKRAVARTY, M. M., MARQUAND, A. F., MACARE, C., KHARABIAN MASOULEH, S., OOSTERLAAN, J., Amouyel, P., HEGENSCHEID, K., ROTTER, J. I., SCHORK, A. J., LIEWALD, D. C. M., DE ZUBICARAY, G. I., WONG, T. Y., Shen, L., SAMANN, P. G., BRODATY, H., ROFFMAN, J. L., DE GEUS, E. J. C., TSOLAKI, M., ERK, S., VAN EIJK, K. R., CAVALLERI, G. L., VAN DER WEE, N. J. A., MCINTOSH, A. M., GOLLUB, R. L., BULAYEVA, K. B., Bernard, M., RICHARDS, J. S., HIMALI, J. J., Loeffler, M., ROMMELSE, N., Hoffmann, W., WESTLYE, L. T., VALDES HERNANDEZ, M. C., HANSELL, N. K., VAN ERP, T. G. M., Wolf, C., KWOK, J. B. J., Vellas, B., Heinz, A., OLDE LOOHUIS, L. M., DELANTY, N., HO, B. C., CHING, C. R. K., SHUMSKAYA, E., Singh, B., Hofman, A., VAN DER MEER, D., Homuth, G., PSATY, B. M., BASTIN, M. E., MONTGOMERY, G. W., FOROUD, T. M., REPPERMUND, S., HOTTENGA, J. J., Simmons, A., Meyer-Lindenberg, A., Cahn, W., WHELAN, C. D., VAN DONKELAAR, M. M. J., Yang, Q., HOSTEN, N., GREEN, R. C., THALAMUTHU, A., MOHNKE, S., HULSHOFF POL, H. E., Lin, H., JACK, C. R., Jr., SCHOFIELD, P. R., MUHLEISEN, T. W., MAILLARD, P., POTKIN, S. G., Wen, W., FLETCHER, E., TOGA, A. W., Gruber, O., HUENTELMAN, M., DAVEY SMITH, G., LAUNER, L. J., Nyberg, L., JONSSON, E. G., CRESPO-FACORRO, B., KOEN, N., GREVE, D. N., UITTERLINDEN, A. G., WEINBERGER, D. R., STEEN, V. M., FEDKO, I. O., GROENEWOLD, N. A., Niessen, W. J., TORO, R., Tzourio, Christophe, LONGSTRETH, W. T., Jr., SMOLLER, J. W., VAN TOL, M. J., SUSSMANN, J. E., PAUS, T., Lemaitre, H., SCHROETER, M. L., Mazoyer, B., ANDREASSEN, O. A., Holsboer, F., DEPONDT, C., VELTMAN, D. J., Turner, J. A., PAUSOVA, Z., Schumann, G., Van Rooij, D., Djurovic, S., DEARY, I. J., MCMAHON, K. L., MULLER-MYHSOK, B., BROUWER, R. M., Soininen, H., Pandolfo, M., WASSINK, T. H., CHEUNG, J. W., WOLFERS, T., MARTINOT, J. L., ZWIERS, M. P., Nauck, M., Melle, I., Martin, N. G., Kanai, R., WESTMAN, E., KAHN, R. S., Sisodiya, S. M., White, T., SAREMI, A., van Bokhoven, H., Brunner, H. G., VOLZKE, H., WRIGHT, M. J., VAN 'T ENT, D., NOTHEN, M. M., OPHOFF, R. A., BUITELAAR, J. K., Fernandez, G., SACHDEV, P. S., Rietschel, M., VAN HAREN, N. E. M., Fisher, S. E., BEISER, A. S., Francks, C., SAYKIN, A. J., MATHER, K. A., ROMANCZUK-SEIFERTH, N., HARTMAN, C. A., DeStefano, A. L., HESLENFELD, D. J., WEINER, M. W., Walter, H., HOEKSTRA, P. J., NYQUIST, P. A., Franke, B., BENNETT, D. A., Grabe, H. J., JOHNSON, A. D., Chen, C., VAN DUIJN, C. M., LOPEZ, O. L., FORNAGE, M., WARDLAW, J. M., Schmidt, R., DeCarli, C., DE JAGER, P. L., VILLRINGER, A., Debette, Stephanie, GUDNASON, V., Medland, S. E., SHULMAN, J. M., THOMPSON, P. M., SESHADRI, S., IKRAM, M. K., Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS)

Subjects
nervous system, VINTAGE, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, HEALTHY
Abstract

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

Published
2019

47. Correlates of online changes in movement representations in 240ms

Author

UCL - SSS/IONS/COSY - Systems & cognitive Neuroscience, UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique, Mathew, James, Bastin, M, Lefèvre, Philippe, Crevecoeur, Frédéric, Neural Control of Movement, UCL - SSS/IONS/COSY - Systems & cognitive Neuroscience, UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique, Mathew, James, Bastin, M, Lefèvre, Philippe, Crevecoeur, Frédéric, and Neural Control of Movement

Abstract

Humans and animals use adaptive representations of movement dynamics, called internal models, which allow them to adapt motor commands to novel disturbances in the environment. To date this process has largely been described through learning curves characterizing changes in performance across movement repeats, yet recent evidence suggests that adaptive changes in reach representation may occur during a single movement. Here we sought to determine the latency of these rapid changes in movement representation based on electromyogram recordings. Healthy adults (n=18) performed standard reaching movement trials with a robotic handle randomly interleaved with force field trials (lateral force proportional to hand forward velocity) in unpredictable direction. First, we observed that the maximum hand displacement towards the direction of perturbation in a force field trial displayed very small improvement across trials, while the target overshoot was associated with a slower exponential decay. This result supports the presence of adaptive feedback control within each trial. Next, we identified in the main muscles involved in the motor corrections (shoulder flexors and extensors) a clear modulation explaining the reduction in target overshoot. Based on a sliding comparison of the difference between the first and last trials, we measured that the onset of the response tuned to the force field occurred on average at 237±15ms following reach onset (mean±SD). To further assess whether these corrections were tuned to the force field, we regressed the commanded force with the measured force and found highly significant improvements across trials. Moreover, the correlations became better than when measured force of a single trial was compared with commanded force of random trials. This is similar to what we have observed in a control experiment, when the force field is in a single predictable direction, indicating that the adjustments of feedback were tuned to the specific perturb

Published
2019

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