Evidence map›Paper›PMID 41975557›Full record

ArticleJournal of the International Neuropsychological Society : JINS2026

Psychometric modeling of Boston process approach data for dementia prediction in the Framingham Heart Study.

Brandon Frank, Ashita Gurnani, Landon Hurley, Calvin Guan, Stacy L Andersen, Sherral A Devine, Maureen K O'Connor, Andrew Budson, Chunyu Liu, Honghuang Lin and 8 more

Abstract read
In one paragraph

Article in Journal of the International Neuropsychological Society : JINS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

18 authors.

Brandon FrankNeurology, https://ror.org/04v00sg98VA Boston Healthcare System, USA.ORCID https://orcid.org/0000-0002-0548-9074
Ashita GurnaniDepartment of Neurology, https://ror.org/05qwgg493Boston University Chobanian & Avedisian School of Medicine, USA.
Landon HurleyNeurology, https://ror.org/04v00sg98VA Boston Healthcare System, USA.
Calvin GuanThe Framingham Heart Study, USA.
Stacy L AndersenDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, USA.ORCID https://orcid.org/0000-0002-2442-6386
Sherral A DevineThe Framingham Heart Study, USA.
Maureen K O'ConnorAlzheimer's Disease Research Center, https://ror.org/05qwgg493Boston University Chobanian & Avedisian School of Medicine, USA.
Andrew BudsonNeurology, https://ror.org/04v00sg98VA Boston Healthcare System, USA.
Chunyu LiuDepartment of Biostatistics, Boston University School of Public Health.
Honghuang LinDepartment of Medicine, University of Massachusetts Chan Medical School, USA.
Sanford AuerbachDepartment of Neurology, https://ror.org/05qwgg493Boston University Chobanian & Avedisian School of Medicine, USA.
Yulin LiuDepartment of Neurology, https://ror.org/05qwgg493Boston University Chobanian & Avedisian School of Medicine, USA.
David J LibonGeriatric and Gerontology, New Jersey Institute for Successful Aging, and Department of Psychology, Rowan University School of Osteopathic Medicine, USA.ORCID https://orcid.org/0000-0001-6617-6066
Catherine C PriceClinical and Health Psychology, University of Florida, USA.
Lindsay FarrerAlzheimer's Disease Research Center, https://ror.org/05qwgg493Boston University Chobanian & Avedisian School of Medicine, USA.
Jesse MezAlzheimer's Disease Research Center, https://ror.org/05qwgg493Boston University Chobanian & Avedisian School of Medicine, USA.
Alvin AngThe Framingham Heart Study, USA.
Rhoda AuAlzheimer's Disease Research Center, https://ror.org/05qwgg493Boston University Chobanian & Avedisian School of Medicine, USA.

Funding

TBDU19AG068753 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Rhoda Au, Lindsay A. Farrer · 2020 to 2026
$42.4M
FRAMINGHAM HEART STUDY - YEAR 5 EXAM75N92019D00031 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · 2019 to 2024
$29.8M
Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approachesU01AG068221 · NIA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI DESTEFANO, ANITA L, LIN, HONGHUANG · 2021 to 2024
$2.5M
Digital Cognitive Assessment of Preclinical Alzheimer's Disease and Related DementiasR01AG083735 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Rhoda Au, Vijaya B. Kolachalama · 2024 to 2026
$2.4M
Cognitive heterogeneity in those with high Alzheimer's Disease RiskR01AG062109 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Rhoda Au, Vijaya B. Kolachalama · 2025 to 2026
$1.6M
Precision Brain Health Monitoring for Alzheimer's Disease Risk Detection in the Framingham StudyR01AG072654 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Rhoda Au, LEE E. GOLDSTEIN · 2025 to 2026
$1.1M
Perioperative Cognitive Anesthesia Network (PeCAN) Program for Alzheimer’s Disease and Related DementiasK07AG066813 · NIA · UNIVERSITY OF FLORIDA · PI PRICE, CATHERINE E · 2020 to 2024
$810k
Digital Markers of Cognition Across the Spectrum of Preclinical Cognitive Impairment to DementiaK01AG057798 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI ANDERSEN, STACY · 2018 to 2022
$596k
CSRD VA I01 CX002400CSRD VA IK2 CX002625NHLBI NIH HHS 75N92019D00031NIA NIH HHS K01 AG057798NIA NIH HHS K07 AG066813NIA NIH HHS R01 AG062109NIA NIH HHS R01 AG072654NIA NIH HHS R01 AG083735NIA NIH HHS U01 AG068221NIA NIH HHS U19 AG068753
6 · The paper itself

Abstract

objectivesNeuropsychological (NP) tests are multi-domain in execution. Reliance on a single score representing specific domains obscures the detection of subtle cognitive changes and increases risk of inaccurate assessment. Rooted in the Boston Process Approach (BPA), the Framingham Heart Study (FHS) captures multi-dimensional errors and process features within and across NP tests. We examined these BPA variables in community-dwelling older adults.

methodsWe analyzed data from 2363 dementia-free participants aged 60 and above. Exploratory and confirmatory factor analyses used Kemeny covariance structures. Measurement invariance was estimated across age, sex, and education groups. We assessed the impact of demographics on latent factors, and the ability of these factors to predict future conversion to all-cause dementia. We trained machine learning (ML) models to compare NP and BPA data.

resultsParticipants were older adults (mean age 71.5 ± 8.7 years), primarily female (54.2%), and non-Hispanic White (96.5%). The bifactor model was the only model with adequate fit (CFI = 0.96, RMSEA = 0.03). General and specific factors captured ability for accurate and strategic responses, test-specific variance, and nuanced executive and semantic processes distributed across tests. Higher general ability and stronger verbatim story recall were associated with a reduced likelihood of developing all-cause dementia (general: OR = 0.15, 95% CI [0.12-0.86]; recall: OR = 0.24, 95% CI [0.23-0.90]) over a median of 5.2 years. With NP/BPA data, ML models identified >99% of 222 converters.

conclusionsThis study highlights the strengths of NP/BPA data. Multidimensional cognitive features may enhance sensitivity to early changes predictive of incipient dementia.

Indexed as

AgingDementiaMachine LearningNeuropsychological TestsPsychometricsAgedAged, 80 and overFemaleHumansMaleMiddle AgedPredictive Learning ModelsBoston process approachdementiaFramingham Heart Studymachine learningmeasurement invarianceneuropsychologystructural equation modeling

Identifiers

PMID41975557
PMCPMC13082455

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Registered trials

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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.