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      Cortical amyloid-beta burden is associated with changes in intracortical myelin in cognitively normal older adults

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          Abstract

          Amyloid-beta (Aβ) aggregates and myelin breakdown are among the earliest detrimental effects of Alzheimer’s disease (AD), likely inducing abnormal patterns of neuronal communication within cortical networks. However, human in vivo evidence linking Aβ burden, intracortical myelin, and cortical synchronization is lacking in cognitively normal older individuals. Here, we addressed this question combining 18F-Florbetaben-PET imaging, cortical T1-weigthed/T2-weighted (T1w/T2w) ratio maps, and resting-state functional connectivity (rs-FC) in cognitively unimpaired older adults. Results showed that global Aβ burden was both positively and negatively associated with the T1w/T2w ratio in different cortical territories. Affected cortical regions were further associated with abnormal patterns of rs-FC and with subclinical cognitive deficits. Finally, causal mediation analysis revealed that the negative impact of T1w/T2w ratio in left posterior cingulate cortex on processing speed was driven by Aβ burden. Collectively, these findings provide novel insights into the relationship between initial Aβ plaques and intracortical myelin before the onset of cognitive decline, which may contribute to monitor the efficacy of novel disease-modifying strategies in normal elderly individuals at risk for cognitive impairment.

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          Most cited references78

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

            The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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              Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man

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                Author and article information

                Contributors
                jlcanlor@upo.es
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                6 April 2023
                6 April 2023
                2023
                : 13
                : 115
                Affiliations
                [1 ]GRID grid.15449.3d, ISNI 0000 0001 2200 2355, Laboratory of Functional Neuroscience, , Pablo de Olavide University, ; Seville, Spain
                [2 ]GRID grid.418264.d, ISNI 0000 0004 1762 4012, CIBERNED, , Network Center for Biomedical Research in Neurodegenerative Diseases, ; Madrid, Spain
                Author information
                http://orcid.org/0000-0002-9258-3439
                http://orcid.org/0000-0002-2628-7076
                Article
                2420
                10.1038/s41398-023-02420-7
                10079650
                37024484
                41ef1983-c5e6-4aa7-acd7-720254a6fc40
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 December 2022
                : 24 March 2023
                : 27 March 2023
                Categories
                Article
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                © The Author(s) 2023

                Clinical Psychology & Psychiatry
                prognostic markers,molecular neuroscience
                Clinical Psychology & Psychiatry
                prognostic markers, molecular neuroscience

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