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      Multimodal imaging of the aging brain: Baseline findings of the LoCARPoN study

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          Abstract

          We quantified and investigated multimodal brain MRI measures in the LoCARPoN Study due to lack of normative data among Indians. A total of 401 participants (aged 50–88 years) without stroke or dementia completed MRI investigation. We assessed 31 brain measures in total using four brain MRI modalities, including macrostructural (global & lobar volumes, white matter hyperintensities [WMHs]), microstructural (global and tract-specific white matter fractional anisotropy [WM-FA] and mean diffusivity [MD]) and perfusion measures (global and lobar cerebral blood flow [CBF]). The absolute brain volumes of males were significantly larger than those of females, but such differences were relatively small (<1.2% of intracranial volume). With increasing age, lower macrostructural brain volumes, lower WM-FA, greater WMHs, higher WM-MD were found ( P = 0.00018, Bonferroni threshold). Perfusion measures did not show significant differences with increasing age. Hippocampal volume showed the greatest association with age, with a reduction of approximately 0.48%/year. This preliminary study augments and provides insight into multimodal brain measures during the nascent stages of aging among the Indian population (South Asian ethnicity). Our findings establish the groundwork for future hypothetical testing studies.

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          Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

          There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.
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            MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging.

            The type, frequency, and extent of MR signal abnormalities in Alzheimer's disease and normal aging are a subject of controversy. With a 1.5-MR unit we studied 12 Alzheimer patients, four subjects suffering from multiinfarct dementia and nine age-matched controls. Punctate or early confluent high-signal abnormalities in the deep white matter, noted in 60% of both Alzheimer patients and controls, were unrelated to the presence of hypertension or other vascular risk factors. A significant number of Alzheimer patients exhibited a more extensive smooth "halo" of periventricular hyperintensity when compared with controls (p = .024). Widespread deep white-matter hyperintensity (two patients) and extensive, irregular periventricular hyperintensity (three patients) were seen in multiinfarct dementia. Areas of high signal intensity affecting hippocampal and sylvian cortex were also present in five Alzheimer and two multiinfarct dementia patients, but absent in controls. Discrete, small foci of deep white-matter hyperintensity are not characteristic of Alzheimer's disease nor do they appear to imply a vascular cause for the dementing illness. The frequently observed "halo" of periventricular hyperintensity in Alzheimer's disease may be of diagnostic importance. High-signal abnormalities in specific cortical regions are likely to reflect disease processes localized to those structures.
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              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.
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                Author and article information

                Contributors
                Journal
                Aging Brain
                Aging Brain
                Aging Brain
                Elsevier
                2589-9589
                28 April 2023
                2023
                28 April 2023
                : 3
                : 100075
                Affiliations
                [a ]Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
                [b ]Department of Neurology, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
                [c ]Department of Neuroradiology, All India Institute of Medical Sciences, New Delhi, India
                [d ]Department of Neuroradiology, Kings College Hospital, London, UK
                [e ]Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
                Author notes
                [* ]Corresponding author at: Director’s Cell, Rajendra Institute of Medical Sciences, Ranchi 834009, Jharkhand, India. drkameshwarprasad@ 123456gmail.com
                [1]

                Former Professor & Head of Department of Neurology, Chief, Neurosciences Centre & Director, Clinical Epidemiology Unit, All India Institute of Medical Sciences, New Delhi, India-110029.

                Article
                S2589-9589(23)00012-9 100075
                10.1016/j.nbas.2023.100075
                10173278
                9b052038-6e9b-493f-b789-c176bc86b08b
                © 2023 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 3 January 2023
                : 7 April 2023
                : 11 April 2023
                Categories
                Article

                aging,population-based study,neuroimaging,multimodal mri

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