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001-es BibID:BIBFORM103565
035-os BibID:(Scopus)85137531133 (WoS)000849139400001
Első szerző:Aranyi Sándor Csaba (programtervező informatikus)
Cím:Topological dissimilarities of hierarchical resting networks in type 2 diabetes mellitus and obesity / Aranyi Sándor Csaba, Képes Zita, Nagy Marianna, Opposits Gábor, Garai Ildikó, Káplár Miklós, Emri Miklós
Dátum:2023
ISSN:0929-5313
Megjegyzések:Type 2 diabetes mellitus (T2DM) is reported to cause widespread changes in brain function, leading to cognitive impairments. Research using resting-state functional magnetic resonance imaging data already aims to understand functional changes in complex brain connectivity systems. However, no previous studies with dynamic causal modelling (DCM) tried to investigate large-scale effective connectivity in diabetes. We aimed to examine the differences in large-scale resting state networks in diabetic and obese patients using combined DCM and graph theory methodologies. With the participation of 70 subjects (43 diabetics, 27 obese), we used cross-spectra DCM to estimate connectivity between 36 regions, subdivided into seven resting networks (RSN) commonly recognized in the literature. We assessed group-wise connectivity of T2DM and obesity, as well as group differences, with parametric empirical Bayes and Bayesian model reduction techniques. We analyzed network connectivity globally, between RSNs, and regionally. We found that average connection strength was higher in T2DM globally and between RSNs, as well. On the network level, the salience network shows stronger total within-network connectivity in diabetes (8.07) than in the obese group (4.02). Regionally, we measured the most significant average decrease in the right middle temporal gyrus (-0.013 Hz) and the right inferior parietal lobule (-0.01 Hz) relative to the obese group. In comparison, connectivity increased most notably in the left anterior prefrontal cortex (0.01 Hz) and the medial dorsal thalamus (0.009 Hz). In conclusion, we find the usage of complex analysis of large-scale networks suitable for diabetes instead of focusing on specific changes in brain function.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Type 2 diabetes mellitus
Obesity
Effective connectivity
Dynamic causal modelling
Graph theory
Megjelenés:Journal Of Computational Neuroscience. - 51 : 1 (2023), p. 71-86. -
További szerzők:Képes Zita (1991-) (orvos) Nagy Marianna (1987-) (orvosdiagnosztikai képalkotó) Opposits Gábor (1974-) (fizikus, szoftver fejlesztő) Garai Ildikó (1966-) (radiológus) Káplár Miklós (1965-) (belgyógyász, diabetológus) Emri Miklós (1962-) (fizikus)
Pályázati támogatás:2017-1.2.1-NKP-2017-00002
Egyéb
TKP2021-NKTA-34
Egyéb
GINOP-2.1.1-15-2015-00609
GINOP
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