12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Cerebral blood flow, blood supply, and cognition in Type 2 Diabetes Mellitus

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We investigated whether type 2 diabetes (T2DM) and the presence of cognitive impairment are associated with altered cerebral blood flow (CBF). Forty-one participants with and thirty-nine without T2DM underwent 3-Tesla MRI, including a quantitative technique measuring (macrovascular) blood flow in the internal carotid artery and an arterial spin labeling technique measuring (microvascular) perfusion in the grey matter (GM). Three analysis methods were used to quantify the CBF: a region of interest analysis, a voxel-based statistical parametric mapping technique, and a ‘distributed deviating voxels’ method. Participants with T2DM exhibited significantly more tissue with low CBF values in the cerebral cortex and the subcortical GM (3.8-fold increase). The latter was the only region where the hypoperfusion remained after correcting for atrophy, indicating that the effect of T2DM on CBF, independent of atrophy, is small. Subcortical CBF was associated with depression. No associations were observed for CBF in other regions with diabetes status, for carotid blood flow with diabetes status, or for CBF or flow in relation with cognitive function. To conclude, a novel method that tallies total ‘distributed deviating voxels’ demonstrates T2DM-associated hypoperfusion in the subcortical GM, not associated with cognitive performance. Whether a vascular mechanism underlies cognitive decrements remains inconclusive.

          Related collections

          Most cited references46

          • Record: found
          • Abstract: found
          • Article: not found

          FSL.

          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

            We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm.

              The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation--no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.
                Bookmark

                Author and article information

                Contributors
                jacobus.jansen@mumc.nl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 December 2016
                2016
                : 6
                : 160003
                Affiliations
                [1 ]GRID grid.412966.e, ISNI 0000 0004 0480 1382, Departments of Radiology & Nuclear Medicine, , Maastricht University Medical Center, ; Maastricht, the Netherlands
                [2 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, School for Mental Health and Neuroscience (MHeNS), ; Maastricht, the Netherlands
                [3 ]GRID grid.412966.e, ISNI 0000 0004 0480 1382, Departments of Psychiatry and Neuropsychology, , Maastricht University Medical Center, ; Maastricht, the Netherlands
                [4 ]ISNI 0000000089452978, GRID grid.10419.3d, Department of Radiology, , Leiden University Medical Center, ; Leiden, the Netherlands
                [5 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Cardiovascular Research Institute Maastricht (CARIM), ; Maastricht, the Netherlands
                [6 ]GRID grid.412966.e, ISNI 0000 0004 0480 1382, Department of Neurology, , Maastricht University Medical Center, ; Maastricht, the Netherlands
                [7 ]GRID grid.412966.e, ISNI 0000 0004 0480 1382, Department of Internal Medicine, , Maastricht University Medical Center, ; Maastricht, the Netherlands
                Author information
                http://orcid.org/0000-0002-5271-8060
                Article
                3
                10.1038/s41598-016-0003-6
                8276879
                27920431
                77f17905-c038-4065-bfaf-edbb8e3966f6
                © The Author(s) 2016

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 16 February 2016
                : 24 August 2016
                Categories
                Article
                Custom metadata
                © The Author(s) 2016

                Uncategorized
                diagnostic markers,diabetes complications,dementia
                Uncategorized
                diagnostic markers, diabetes complications, dementia

                Comments

                Comment on this article