Evidence map›Paper›PMID 39318783›Full record

ArticleBrain communications2024

3T sodium-MRI as predictor of neurocognition in nondemented older adults: a cross sectional study.

Elaine Lui, Vijay K Venkatraman, Sue Finch, Michelle Chua, Tie-Qiang Li, Bradley P Sutton, Christopher E Steward, Bradford Moffat, Elizabeth V Cyarto, Kathryn A Ellis and 4 more

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In one paragraph

Article in Brain communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Elaine LuiDepartment of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia.
Vijay K VenkatramanDepartment of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia.
Sue FinchStatistical Consulting Centre, University of Melbourne, Parkville, 3010 Victoria, Australia.ORCID https://orcid.org/0000-0003-4261-0504
Michelle ChuaDepartment of Medical Imaging, The Royal Melbourne Hospital, Parkville, 3050 Victoria, Australia.
Tie-Qiang LiDepartment of Clinical Science, Intervention and Technology, Karolinska Institute, 171 77 Stockholm, Sweden.
Bradley P SuttonBeckman Institute for Advance Science and Technology, University of Illinois at Urbana Champaign, Champaign, IL 61801, USA.ORCID https://orcid.org/0000-0002-8443-0408
Christopher E StewardDepartment of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia.
Bradford MoffatDepartment of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia.
Elizabeth V CyartoAustralian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Queensland 4059, Australia.
Kathryn A EllisAcademic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Melbourne, 3010 Victoria, Australia.
Christopher C RoweDepartment of Molecular Imaging and Therapy, Austin Health, Melbourne, 3084 Victoria, Australia.
Colin L MastersThe Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, 3052 Victoria, Australia.ORCID https://orcid.org/0000-0003-3072-7940
Nicola T LautenschlagerAcademic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Melbourne, 3010 Victoria, Australia.
Patricia M DesmondDepartment of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dementia is a burgeoning global problem. Novel magnetic resonance imaging (MRI) metrics beyond volumetry may bring new insight and aid clinical trial evaluation of interventions early in the Alzheimer's disease course to complement existing imaging and clinical metrics. To determine whether: (i) normalized regional sodium-MRI values (Na-SI) are better predictors of neurocognitive status than volumetry (ii) cerebral amyloid PET status improves modelling. Nondemented older adult (>60 years) volunteers of known Alzheimer's Disease Assessment Scale (ADAS-Cog11), Mini-Mental State Examination (MMSE) and Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neurocognitive test scores, ApolipoproteinE (APOE) e4 +/- cerebral amyloid PET status were prospectively recruited for 3T sodium-MRI brain scans. Left and right hippocampal, entorhinal and precuneus volumes and Na-SI (using the proportional intensity scaling normalization method with field inhomogeneity and partial volume corrections) were obtained after segmentation and co-registration of 3D-T1-weighted proton images. Descriptive statistics, correlation and best-subset regression analyses were performed. In our 76 nondemented participants (mean(standard deviation) age 75(5) years; woman 47(62%); cognitively unimpaired 54/76(71%), mildly cognitively impaired 22/76(29%)), left hippocampal Na-SI, not volume, was preferentially in the best models for predicting MMSE (Odds Ratio (OR) = 0.19(Confidence Interval (CI) = 0.07,0.53),

Indexed as

3-Teslaneurocognitionnondementedsodium-MRI

Identifiers

PMID39318783
PMCPMC11420980

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