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      Grey matter network trajectories across the Alzheimer’s disease continuum and relation to cognition

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          Abstract

          Biomarkers are needed to monitor disease progression in Alzheimer’s disease. Grey matter network measures have such potential, as they are related to amyloid aggregation in cognitively unimpaired individuals and to future cognitive decline in predementia Alzheimer’s disease. Here, we investigated how grey matter network measures evolve over time within individuals across the entire Alzheimer’s disease cognitive continuum and whether such changes relate to concurrent decline in cognition. We included 190 cognitively unimpaired, amyloid normal (controls) and 523 individuals with abnormal amyloid across the cognitive continuum (preclinical, prodromal, Alzheimer’s disease dementia) from the Alzheimer’s Disease Neuroimaging Initiative and calculated single-subject grey matter network measures (median of five networks per individual over 2 years). We fitted linear mixed models to investigate how network measures changed over time and whether such changes were associated with concurrent changes in memory, language, attention/executive functioning and on the Mini-Mental State Examination. We further assessed whether associations were modified by baseline disease stage. We found that both cognitive functioning and network measures declined over time, with steeper rates of decline in more advanced disease stages. In all cognitive stages, decline in network measures was associated with concurrent decline on the Mini-Mental State Examination, with stronger effects for individuals closer to Alzheimer’s disease dementia. Decline in network measures was associated with concurrent cognitive decline in different cognitive domains depending on disease stage: In controls, decline in networks was associated with decline in memory and language functioning; preclinical Alzheimer’s disease showed associations of decline in networks with memory and attention/executive functioning; prodromal Alzheimer’s disease showed associations of decline in networks with cognitive decline in all domains; Alzheimer’s disease dementia showed associations of decline in networks with attention/executive functioning. Decline in grey matter network measures over time accelerated for more advanced disease stages and was related to concurrent cognitive decline across the entire Alzheimer’s disease cognitive continuum. These associations were disease stage dependent for the different cognitive domains, which reflected the respective cognitive stage. Our findings therefore suggest that grey matter measures are helpful to track disease progression in Alzheimer’s disease.

          Abstract

          Biomarkers are needed to monitor Alzheimer’s disease progression. Dicks et al. report that grey matter networks decline faster in more advanced stages of the clinical continuum and that they are associated with cognitive decline in distinct domains, reflecting the cognitive stage. These measures may aid in monitoring Alzheimer’s disease progression.

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          Inference from Iterative Simulation Using Multiple Sequences

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            "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician.

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              Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

              An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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                Author and article information

                Contributors
                Journal
                Brain Commun
                Brain Commun
                braincomms
                Brain Communications
                Oxford University Press
                2632-1297
                2020
                20 August 2020
                20 August 2020
                : 2
                : 2
                : fcaa177
                Affiliations
                Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC , 1081 HV Amsterdam, The Netherlands
                Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC , 1081 HV Amsterdam, The Netherlands
                Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC , 1081 HV Amsterdam, The Netherlands
                Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC , 1081 HV Amsterdam, The Netherlands
                Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC , 1081 HV Amsterdam, The Netherlands; Institutes of Neurology & Healthcare Engineering, UCL London, London WC1E, UK
                Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC , 1081 HV Amsterdam, The Netherlands
                Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC , 1081 HV Amsterdam, The Netherlands
                Author notes

                *Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

                Correspondence to: Ellen Dicks, PhD Alzheimer Center and Department of Neurology, Amsterdam UMC, PO Box 7057, 1007 MB Amsterdam, The Netherlands E-mail: e.dicks@ 123456amsterdamumc.nl ; ellendicks.ac@ 123456gmail.com
                Author information
                http://orcid.org/0000-0003-2496-1626
                http://orcid.org/0000-0001-7420-6384
                http://orcid.org/0000-0002-2612-1797
                Article
                fcaa177
                10.1093/braincomms/fcaa177
                7751002
                33376987
                01c767b6-e312-4dbd-83d5-40fcb814af5c
                © The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 22 October 2019
                : 23 June 2020
                : 02 July 2020
                Page count
                Pages: 15
                Funding
                Funded by: ZonMw Memorabel Grant Program;
                Award ID: 73305056
                Funded by: Alzheimer Center Amsterdam;
                Funded by: Stichting Alzheimer Nederland, DOI 10.13039/501100010969;
                Funded by: Stichting VUmc Fonds;
                Categories
                Original Article
                AcademicSubjects/MED00310
                AcademicSubjects/SCI01870

                alzheimer’s disease,single-subject grey matter networks,graph theory,longitudinal,cognition

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