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Record W4280562410 · doi:10.1101/2022.04.20.22274087

Topographical differences in white matter hyperintensity burden and cognition in aging, MCI, and AD

2022· preprint· en· W4280562410 on OpenAlexafffund
Farooq Kamal, Cassandra Morrison, Josefina Maranzano, Yashar Zeighami, Mahsa Dadar

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité du Québec à Trois-RivièresMontreal Neurological Institute and HospitalMcGill UniversityDouglas Mental Health University Institute
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationPfizerBiogenBioClinicaF. Hoffmann-La RocheUniversity of Southern CaliforniaEli Lilly and CompanyU.S. Department of DefenseMeso Scale DiagnosticsAlzheimer's Disease Neuroimaging InitiativeNovartis Pharmaceuticals CorporationAlzheimer SocietyBristol-Myers SquibbNational Institute on AgingAlzheimer Society Research ProgramAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsCognitionHyperintensityCognitive declinePsychologyAlzheimer's Disease Neuroimaging InitiativeNeuroimagingAssociation (psychology)DementiaPathologicalAlzheimer's diseaseHealthy agingEpisodic memoryDiseaseCognitive agingExecutive functionsCognitive impairmentClinical psychologyGerontologyMedicinePsychiatryInternal medicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Abstract Background White matter hyperintensities (WMHs) are pathological changes that develop with increased age and are associated with cognitive decline. Most research on WMHs has neglected to examine regional differences and instead focuses on using a whole-brain approach. This study examined regional WMH differences between normal controls (NCs), people with mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Another goal was to examine whether WMH burden was associated with declines in different cognitive domains in each of the groups. Methods Participants were selected from the Alzheimer’s Disease Neuroimaging Initiative and included if they had at least one WMH measurement and cognitive scores examining global cognition, executive functioning, and memory. MCI and AD participants were included only if they were amyloid positive. A total of 1573 participants with 7381 follow-ups met inclusion criteria. Linear mixed-effects models were completed to examine group differences in WMH burden and the association between WMH burden and cognition in aging, MCI, and AD. Results People with MCI and AD had increased total and regional WMH burden compared to cognitively healthy older adults. An association between WMH and cognition was observed for global cognition, executive functioning, and memory in NCs in all regions of interest. A steeper decline (stronger association between WMH and cognition) was observed in MCI compared to NCs for all cognitive domains in all regions. A steeper decline was observed in AD compared to NCs for global cognition in only the temporal region. Conclusion These results suggest WMH burden increases from aging to AD. A strong association is observed between all cognitive domains of interest and WMH burden in healthy aging and MCI, while those with AD only had a few associations between WMH and memory and WMH and global cognition. These findings suggest that WMH burden is associated with changes in cognition in healthy aging and early cognitive decline, but other biological changes may have a stronger impact on cognition with AD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.299
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2022
Admission routes2
Has abstractyes

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