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[EN] Brain charts for the human lifespan have been recently proposed to build dynamic models of brain anatomy in normal aging and various neurological conditions. They offer new possibilities to quantify neuroanatomical ...[+]
[EN] Brain charts for the human lifespan have been recently proposed to build dynamic models of brain anatomy in normal aging and various neurological conditions. They offer new possibilities to quantify neuroanatomical changes from preclinical stages to death, where longitudinal MRI data are not available. In this study, we used brain charts to model the progression of brain atrophy in progressive supranuclear palsy-Richardson syndrome. We combined multiple datasets (n = 8170 quality controlled MRI of healthy subjects from 22 cohorts covering the entire lifespan, and n = 62 MRI of progressive supranuclear palsy-Richardson syndrome patients from the Four Repeat Tauopathy Neuroimaging Initiative (4RTNI)) to extrapolate lifetime volumetric models of healthy and progressive supranuclear palsy-Richardson syndrome brain structures. We then mapped in time and space the sequential divergence between healthy and progressive supranuclear palsy-Richardson syndrome charts. We found six major consecutive stages of atrophy progression: (i) ventral diencephalon (including subthalamic nuclei, substantia nigra, and red nuclei), (ii) pallidum, (iii) brainstem, striatum and amygdala, (iv) thalamus, (v) frontal lobe, and (vi) occipital lobe. The three structures with the most severe atrophy over time were the thalamus, followed by the pallidum and the brainstem. These results match the neuropathological staging of tauopathy progression in progressive supranuclear palsy-Richardson syndrome, where the pathology is supposed to start in the pallido-nigro-luysian system and spreads rostrally via the striatum and the amygdala to the cerebral cortex, and caudally to the brainstem. This study supports the use of brain charts for the human lifespan to study the progression of neurodegenerative diseases, especially in the absence of specific biomarkers as in PSP.; Planche et al. combined multiple MRI datasets to extrapolate lifetime volumetric models for brain structures in healthy aging and progressive supranuclear palsy-Richardson syndrome. They proposed a descriptive MRI staging scheme for progressive supranuclear palsy-Richardson syndrome, comprising six major consecutive stages of atrophy progression that closely align with the neuropathological staging of tauopathy progression.; Graphical abstract
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The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository created by the C-MIND study of Normal Brain Development. A listing of the participating sites and a complete listing of ...[+]
The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository created by the C-MIND study of Normal Brain Development. A listing of the participating sites and a complete listing of the study investigators can be found at https://research.cchmc.org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). This is supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke. A listing of the participating sites and a complete listing of the study investigators can be found at https://nda.nih.gov/. The ICBM data used in the preparation of this manuscript were supported by Human Brain Project and Canadian Institutes of Health Research. The IXI data used in the preparation of this manuscript were supported by the U.K. Engineering and Physical Sciences Research Council (http://www.brain-development.org/). The ABIDE data used in the preparation of this manuscript were supported by ABIDE funding resources listed at http://fcon_1000.projects.nitrc.org/indi/abide/. The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from private partners as well as nonprofit partners listed at: https://ida.loni.usc.edu/collaboration/access/appLicense.jsp. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of California, Los Angeles. This research was also supported by NIH grants and the Dana Foundation. The AIBL study of ageing was funded by the Common-wealth Scientific Industrial Research Organization, Science Industry Endowment Fund, National Health and Medical Research Council of Australia, Alzheimer's Association, Alzheimer's Drug Discovery Foundation, and an anonymous foundation. See www.aibl.csiro.au for further details. The ADHAD, DLBS and SALD data used in the preparation of this article were obtained from http://fcon_1000.projects.nitrc.org. The ISYB data were downloaded from https://www.scidb.cn. The MIRIAD dataset is made available through the support of the UK Alzheimer's Society. The original data collection was funded through an unrestricted educational grant from GlaxoSmithKline. PPMI is a public-private partnership funded by The Michael J. Fox Foundation for Parkinson's Research and funding partners that can be found at https://www.ppmi-info.org. The Amsterdam open MRI collection AOMIC ID-1000/PIOP1/PIOP2 data used in the preparation of this article were obtained from https://nilab-uva.github.io/AOMIC.github.io/. The Calgary preschool MRI dataset was available at https://osf.io/axz5r/and supported by University of Calgary. CamCAN (https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/) funding was provided by the UK Biotechnology and Biological Sciences Research Council, together with support from the UK Medical Research Council and University of Cambridge, UK. The Pixar database and related funding's were available at https://openneuro.org/datasets/ds000228/versions/1.1.0., Data used in the preparation of this work were also obtained from the DecNef Project Brain Data Repository (https://bicr-resource.atr.jp/srpbsopen/) gathered by a consortium as part of the Japanese Strategic Research Program for the Promotion of Brain Science (SRPBS) supported by the Japanese Advanced Research and Development Programs for Medical Innovation.
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