Evidence map›Paper›PMID 41225656›Full record

ArticleAlzheimer's research & therapy2025

Generalizable MRI normative modelling to detect age-inappropriate neurodegeneration.

Thomas D Parker, Richard A I Bethlehem, Jakob Seidlitz, Simon R White, Michael C B David, Magdalena A Kolanko, Joshua D Bernstock, Lena Dorfschmidt, Niall Bourke, Anastasia Gailly de Taurines and 16 more

Abstract read
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Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

26 authors.

Thomas D ParkerDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK. t.parker@imperial.ac.uk.
Richard A I BethlehemDepartment of Psychology, University of Cambridge, Downing Pl, Cambridge, CB2 3EB, UK.
Jakob SeidlitzThe Lifespan Brain Institute of The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, 19104, USA.
Simon R WhiteMRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK.
Michael C B DavidDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Magdalena A KolankoDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Joshua D BernstockDepartment of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Lena DorfschmidtThe Lifespan Brain Institute of The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, 19104, USA.
Niall BourkeDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Anastasia Gailly de TaurinesDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Jessica A HainDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Martina Del GiovaneDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Neil S N GrahamDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Karl A ZimmermanDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Ethan J F LostyDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Michael SchöllDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK.
Meera SrikrishnaWallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 405 30, Gothenburg, Sweden.
Paresh A MalhotraDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Maneesh C PatelImperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Rd, London, W6 8RF, UK.
Gregory ScottDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Aaron F Alexander-BlochThe Lifespan Brain Institute of The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, 19104, USA.
Edward T BullmoreDepartment of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK.
David J SharpDepartment of Brain Sciences. Burlington Danes, Imperial College London, The Hammersmith Hospital, London, W12 0HS, UK.
Alzheimer’s Disease Neuroimaging Initiative
Frontotemporal Lobar Degeneration Neuroimaging Initiative
Lifespan Brain Chart Consortium

Funding

National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8M
Research Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Katherine P Rankin · 2019 to 2026
$36.9M
Research Education ComponentP30AG062421 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI BRADFORD C DICKERSON · 2019 to 2026
$36.5M
UCSD Shiley-Marcos Alzheimer's Disease Research Center P30P30AG062429 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DOUGLAS R GALASKO · 2019 to 2026
$34.9M
Wisconsin Alzheimer's Disease Research CenterP30AG062715 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Sanjay Asthana · 2019 to 2026
$34.5M
Research Education ComponentP30AG062677 · NIA · MAYO CLINIC ROCHESTER · PI KEJAL KANTARCI · 2019 to 2026
$33.5M
Research Education ComponentP30AG066514 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Margaret Sewell · 2020 to 2026
$31.0M
Yale Alzheimer Disease Research CenterP30AG066508 · NIA · YALE UNIVERSITY · PI STEPHEN M STRITTMATTER · 2020 to 2026
$30.2M
Research Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PHILIP L DE JAGER · 2020 to 2026
$30.1M
Research Education ComponentP30AG066468 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI C. Elizabeth Shaaban · 2020 to 2026
$29.4M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
University of Washington Alzheimer's Disease Research CenterP30AG066509 · NIA · UNIVERSITY OF WASHINGTON · PI Amanda D. Boyd · 2020 to 2026
$29.0M
NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062677NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066462NIA NIH HHS P30 AG066468NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066508NIA NIH HHS P30 AG066509NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG066518NIA NIH HHS P30 AG066519NIA NIH HHS P30 AG066530NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072946NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072973NIA NIH HHS P30 AG072975NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072977NIA NIH HHS P30 AG072978NIA NIH HHS P30 AG072979NIA NIH HHS P30 AG086401NIA NIH HHS P30 AG086404NIA NIH HHS R01 AG079280NIA NIH HHS U24 AG072122
6 · The paper itself

Abstract

backgroundDetermining whether MRI brain scans demonstrate atrophy that is beyond "normal for age" is challenging. Automated measurements of structural metrics in individual brain regions have shown promise as biomarkers of neurodegeneration, yet widely available reference standards that aid interpretation at the individual level are lacking. Normative modelling, enabling standardized "brain charts", represents a significant step in addressing this challenge by generating individualized age- and sex- adjusted centile scores derived from large, aggregated datasets for MRI-derived quantitative metrics.

methodsUsing normative data from 56,173 participants across the life course, we have developed regional cortical thickness and amygdala/hippocampal volume brain charts (adjusted for total intracranial volume) that can be applied at the individual level. At the group level, we investigate whether regional centile scores relate to cognitive performance (mini-mental state examination) and discriminate individuals with neuropathological evidence of Alzheimer's disease (n = 351) from propensity-matched controls from the National Alzheimer's Coordinating Center (NACC) dataset. In addition, we explored the relationships between disease stage, cognition, regional tau deposition and regional centile scores in amyloid-β-PET-positive individuals with Alzheimer's disease dementia (n = 39) and mild cognitive impairment (n = 71) from the Alzheimer's Disease Neuroimaging Initiative-3 (ADNI-3). We then extended this approach to phenotypes of frontotemporal lobar degeneration using the Neuroimaging in Frontotemporal Dementia dataset (n = 113).

resultsWe demonstrate BrainChart's application to illustrative individual cases. At the group level, we show that in Alzheimer's disease, regional centile scores from brain charting predicted cognitive performance, temporal lobe tau PET tracer uptake and discriminated disease groups from propensity matched cognitively normal controls in independent cohorts. Distinct patterns of age-inappropriate cortical atrophy were also evident in different clinical phenotypes of frontotemporal lobar degeneration from the Neuroimaging in Frontotemporal Dementia dataset.

conclusionsRegional centile scores derived from an extensive normative dataset represent a generalizable method for objectively identifying atrophy in neurodegenerative diseases and can be applied to determine neurodegenerative atrophy at the individual level.

Indexed as

AgingAlzheimer DiseaseBrainMagnetic Resonance ImagingNeurodegenerative DiseasesAgedAged, 80 and overAtrophyFemaleHumansMaleMiddle AgedPositron-Emission TomographyAlzheimer’s diseaseBrainChartDementiaFrontotemporal lobar degenerationMRINeurodegenerationNormative modelling

Identifiers

PMID41225656
PMCPMC12613731

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.