Nonlinear Z-score modeling for improved detection of cognitive abnormality

ARTFL/LEFFTDS consortium

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Introduction: Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency. Methods: In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance). Results: Corrected Z-scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted-R2. Discussion: Nonlinearly corrected Z-scores with respect to age, sex, and education with age-varying residual standard deviation allow for improved detection of non-normative extreme cognitive scores.

Original languageEnglish (US)
Pages (from-to)797-808
Number of pages12
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume11
DOIs
StatePublished - Dec 2019
Externally publishedYes

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Keywords

  • Generalized additive models
  • Heterogenous variance modeling
  • Neuropsychological testing scores
  • Nonlinear Z-score correction
  • Shape constrained additive models

ASJC Scopus subject areas

  • Clinical Neurology
  • Psychiatry and Mental health

Cite this

Nonlinear Z-score modeling for improved detection of cognitive abnormality. / ARTFL/LEFFTDS consortium.

In: Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, Vol. 11, 12.2019, p. 797-808.

Research output: Contribution to journalArticle

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title = "Nonlinear Z-score modeling for improved detection of cognitive abnormality",
abstract = "Introduction: Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency. Methods: In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance). Results: Corrected Z-scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted-R2. Discussion: Nonlinearly corrected Z-scores with respect to age, sex, and education with age-varying residual standard deviation allow for improved detection of non-normative extreme cognitive scores.",
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author = "{ARTFL/LEFFTDS consortium} and John Kornak and Julie Fields and Walter Kremers and Sara Farmer and Heuer, {Hilary W.} and Leah Forsberg and Danielle Brushaber and Amy Rindels and Hiroko Dodge and Sandra Weintraub and Lilah Besser and Brian Appleby and Yvette Bordelon and Jessica Bove and Patrick Brannelly and Christina Caso and Giovanni Coppola and Reilly Dever and Christina Dheel and Bradford Dickerson and Susan Dickinson and Sophia Dominguez and Kimiko Domoto-Reilly and Kelley Faber and Jessica Ferrall and Ann Fishman and Jamie Fong and Tatiana Foroud and Ralitza Gavrilova and Deb Gearhart and Behnaz Ghazanfari and Nupur Ghoshal and Jill Goldman and Jonathan Graff-Radford and Neill Graff-Radford and Grant, {Ian M.} and Murray Grossman and Dana Haley and John Hsiao and Robin Hsiung and Huey, {Edward D.} and David Irwin and David Jones and Lynne Jones and Kejal Kantarci and Anna Karydas and Daniel Kaufer and Diana Kerwin and David Knopman and Ruth Kraft",
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AU - ARTFL/LEFFTDS consortium

AU - Kornak, John

AU - Fields, Julie

AU - Kremers, Walter

AU - Farmer, Sara

AU - Heuer, Hilary W.

AU - Forsberg, Leah

AU - Brushaber, Danielle

AU - Rindels, Amy

AU - Dodge, Hiroko

AU - Weintraub, Sandra

AU - Besser, Lilah

AU - Appleby, Brian

AU - Bordelon, Yvette

AU - Bove, Jessica

AU - Brannelly, Patrick

AU - Caso, Christina

AU - Coppola, Giovanni

AU - Dever, Reilly

AU - Dheel, Christina

AU - Dickerson, Bradford

AU - Dickinson, Susan

AU - Dominguez, Sophia

AU - Domoto-Reilly, Kimiko

AU - Faber, Kelley

AU - Ferrall, Jessica

AU - Fishman, Ann

AU - Fong, Jamie

AU - Foroud, Tatiana

AU - Gavrilova, Ralitza

AU - Gearhart, Deb

AU - Ghazanfari, Behnaz

AU - Ghoshal, Nupur

AU - Goldman, Jill

AU - Graff-Radford, Jonathan

AU - Graff-Radford, Neill

AU - Grant, Ian M.

AU - Grossman, Murray

AU - Haley, Dana

AU - Hsiao, John

AU - Hsiung, Robin

AU - Huey, Edward D.

AU - Irwin, David

AU - Jones, David

AU - Jones, Lynne

AU - Kantarci, Kejal

AU - Karydas, Anna

AU - Kaufer, Daniel

AU - Kerwin, Diana

AU - Knopman, David

AU - Kraft, Ruth

PY - 2019/12

Y1 - 2019/12

N2 - Introduction: Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency. Methods: In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance). Results: Corrected Z-scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted-R2. Discussion: Nonlinearly corrected Z-scores with respect to age, sex, and education with age-varying residual standard deviation allow for improved detection of non-normative extreme cognitive scores.

AB - Introduction: Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency. Methods: In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance). Results: Corrected Z-scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted-R2. Discussion: Nonlinearly corrected Z-scores with respect to age, sex, and education with age-varying residual standard deviation allow for improved detection of non-normative extreme cognitive scores.

KW - Generalized additive models

KW - Heterogenous variance modeling

KW - Neuropsychological testing scores

KW - Nonlinear Z-score correction

KW - Shape constrained additive models

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