Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-01T21:13:48.734Z Has data issue: false hasContentIssue false
  • English
  • Français

Discrepancy-Based Evidence for Loss of Thinking Abilities (DELTA): Development and Validation of a Novel Approach to Identifying Cognitive Changes

Published online by Cambridge University Press:  11 December 2019

Breton M. Asken Open the ORCID record for Breton M. Asken [Opens in a new window] ,
Kelsey R. Thomas Open the ORCID record for Kelsey R. Thomas [Opens in a new window] ,
Athene Lee Open the ORCID record for Athene Lee [Opens in a new window] ,
Jennifer D. Davis ,
Paul F. Malloy ,
Stephen P. Salloway ,
Stephen Correia  and
for the Alzheimer’s Disease Neuroimaging Initiative
Breton M. Asken*
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI02906, USA Department of Clinical and Health Psychology, University of Florida, Gainesville, FL32610, USA
Kelsey R. Thomas
Affiliation:
Research Service, Veterans Affairs San Diego Healthcare System, San Diego, CA92161, USA Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA92093, USA
Athene Lee
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI02906, USA Butler Hospital, Memory and Aging Program, Providence, RI02906, USA
Jennifer D. Davis
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI02906, USA Department of Psychiatry, Rhode Island Hospital, Providence, RI02905, USA
Paul F. Malloy
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI02906, USA Butler Hospital, Memory and Aging Program, Providence, RI02906, USA
Stephen P. Salloway
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI02906, USA Butler Hospital, Memory and Aging Program, Providence, RI02906, USA
Stephen Correia
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI02906, USA Mental Health and Behavioral Science Service, Providence VA Medical Center, Providence, RI02908, USA
for the Alzheimer’s Disease Neuroimaging Initiative
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI02906, USA Mental Health and Behavioral Science Service, Providence VA Medical Center, Providence, RI02908, USA
*
*Correspondence and reprint requests to: Breton M. Asken, Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, University of Florida, Department of Clinical and Health Psychology, Box G-BH, Division of Clinical Psychology, Providence, RI 02912, USA. Tel: +1 401 444 1929; Fax: +1 401 444 1911. E-mail: basken8@phhp.ufl.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

To develop and validate the Discrepancy-based Evidence for Loss of Thinking Abilities (DELTA) score. The DELTA score characterizes the strength of evidence for cognitive decline on a continuous spectrum using well-established psychometric principles for improving detection of cognitive changes.

Methods:

DELTA score development used neuropsychological test scores from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (two tests each from Memory, Executive Function, and Language domains). We derived regression-based normative reference scores using age, gender, years of education, and word-reading ability from robust cognitively normal ADNI participants. Discrepancies between predicted and observed scores were used for calculating the DELTA score (range 0–15). We validated DELTA scores primarily against longitudinal Clinical Dementia Rating-Sum of Boxes (CDR-SOB) and Functional Activities Questionnaire (FAQ) scores (baseline assessment through Year 3) using linear mixed models and secondarily against cross-sectional Alzheimer’s biomarkers.

Results:

There were 1359 ADNI participants with calculable baseline DELTA scores (age 73.7 ± 7.1 years, 55.4% female, 100% white/Caucasian). Higher baseline DELTA scores (stronger evidence of cognitive decline) predicted higher baseline CDR-SOB (ΔR2 = .318) and faster rates of CDR-SOB increase over time (ΔR2 = .209). Longitudinal changes in DELTA scores tracked closely and in the same direction as CDR-SOB scores (fixed and random effects of mean + mean-centered DELTA, ΔR2 > .7). Results were similar for FAQ scores. High DELTA scores predicted higher PET-Aβ SUVr (ρ = 324), higher CSF-pTau/CSF-Aβ ratio (ρ = .460), and demonstrated PPV > .9 for positive Alzheimer’s disease biomarker classification.

Conclusions:

Data support initial development and validation of the DELTA score through its associations with longitudinal functional changes and Alzheimer’s biomarkers. We provide several considerations for future research and include an automated scoring program for clinical use.

Keywords

DELTAAssessmentCognitive declinePsychometricsADNIBase rate
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 26 , Issue 5 , May 2020 , pp. 464 - 479
Copyright
Copyright © INS. Published by Cambridge University Press, 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, investigatorswithintheADNIcontributedtothedesignandimplementationofADNIand/orprovideddatabutdidnotparticipateinanalysisorwritingofthisreport. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

References

REFERENCES

Arbuthnott, K. & Frank, J. (2000). Trail making test, part B as a measure of executive control: Validation using a set-switching paradigm. Journal of Clinical and Experimental Neuropsychology, 22(4), 518–528. doi: 10.1076/1380-3395(200008)22:4;1-0;FT518CrossRefGoogle ScholarPubMed
Benjamin, D.J., Berger, J.O., Johannesson, M., Nosek, B.A., Wagenmakers, E.J., Berk, R., Bollen, K.A., Brembs, B., Brown, L., Camerer, C., Cesarini, D., Chambers, C.D., Clyde, M., Cook, T.D., De Boeck, P., Dienes, Z., Dreber, A., Easwaran, K., Efferson, C., Fehr, E., Fidler, F., Field, A.P., Forster, M., George, E.I., Gonzalez, R., Goodman, S., Green, E., Green, D.P., Greenwald, A.G., Hadfield, J.D., Hedges, L.V., Held, L., Hua Ho, T., Hoijtink, H., Hruschka, D.J., Imai, K., Imbens, G., Ioannidis, J.P.A., Jeon, M., Jones, J.H., Kirchler, M., Laibson, D., List, J., Little, R., Lupia, A., Machery, E., Maxwell, S.E., McCarthy, M., Moore, D.A., Morgan, S.L., Munafó, M., Nakagawa, S., Nyhan, B., Parker, T.H., Pericchi, L., Perugini, M., Rouder, J., Rousseau, J., Savalei, V., Schönbrodt, F.D., Sellke, T., Sinclair, B., Tingley, D., Van Zandt, T., Vazire, S., Watts, D.J., Winship, C., Wolpert, R.L., Xie, Y., Young, C., Zinman, J., & Johnson, V.E. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6–10. doi: 10.1038/s41562-017-0189-zCrossRefGoogle ScholarPubMed
Binder, L.M., Iverson, G.L., & Brooks, B.L. (2009). To err is human: “Abnormal” neuropsychological scores and variability are common in healthy adults. Archives of Clinical Neuropsychology, 24(1), 31–46.CrossRefGoogle Scholar
Bondi, M.W., Edmonds, E.C., Jak, A.J., Clark, L.R., Delano-Wood, L., McDonald, C.R., Nation, D.A., Libon, D.J., Au, R., Galasko, D., & Salmon, D.P. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer’s Disease, 42(1), 275–289.CrossRefGoogle ScholarPubMed
Bondi, M.W. & Smith, G.E. (2014). Mild cognitive impairment: A concept and diagnostic entity in need of input from neuropsychology. Journal of the International Neuropsychological Society, 20(2), 129–134.CrossRefGoogle ScholarPubMed
Boyle, P.A., Yu, L., Wilson, R.S., Leurgans, S.E., Schneider, J.A., & Bennett, D.A. (2018). Person-specific contribution of neuropathologies to cognitive loss in old age. Annals of Neurology, 83(1), 74–83.CrossRefGoogle ScholarPubMed
Brooks, B.L. & Iverson, G.L. (2010). Comparing actual to estimated base rates of “abnormal” scores on neuropsychological test batteries: Implications for interpretation. Archives of Clinical Neuropsychology, 25(1), 14–21.CrossRefGoogle ScholarPubMed
Brooks, B.L., Iverson, G.L., & White, T. (2009). Advanced interpretation of the Neuropsychological Assessment Battery with older adults: Base rate analyses, discrepancy scores, and interpreting change. Archives of Clinical Neuropsychology, 24(7), 647–657.CrossRefGoogle ScholarPubMed
Brooks, B.L., Sherman, E.M., Iverson, G.L., Slick, D.J., & Strauss, E. (2011). Psychometric foundations for the interpretation of neuropsychological test results. In The Little Black Book of Neuropsychology, (pp. 893–922). Boston, MA: Springer.CrossRefGoogle Scholar
Casaletto, K.B., Marx, G., Dutt, S., Neuhaus, J., Saloner, R., Kritikos, L., Miller, B., & Kramer, J.H. (2017). Is “Learning” episodic memory? Distinct cognitive and neuroanatomic correlates of immediate recall during learning trials in neurologically normal aging and neurodegenerative cohorts. Neuropsychologia, 102, 19–28.CrossRefGoogle Scholar
Crane, P.K., Carle, A., Gibbons, L.E., Insel, P., Mackin, R.S., Gross, A., Jones, R.N., Mukherjee, S., Curtis, S. M., Harvey, D., & Weiner, M. (2012). Development and assessment of a composite score for memory in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Brain Imaging and Behavior, 6(4), 502–516.CrossRefGoogle Scholar
Crawford, J.R., Moore, J.W., & Cameron, I.M. (1992). Verbal fluency: A NART-based equation for the estimation of premorbid performance. British Journal of Clinical Psychology, 31(3), 327–329.CrossRefGoogle ScholarPubMed
De Meyer, G., Shapiro, F., Vanderstichele, H., Vanmechelen, E., Engelborghs, S., De Deyn, P.P., Coart, E., Hansson, O., Minthon, L., Zetterberg, H., &Blennow, K. (2010). Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people. Archives of Neurology, 67(8), 949–956. doi: 10.1001/archneurol.2010.179CrossRefGoogle ScholarPubMed
Dotson, V.M., Kitner-Triolo, M.H., Evans, M.K., & Zonderman, A.B. (2009). Effects of race and socioeconomic status on the relative influence of education and literacy on cognitive functioning. Journal of the International Neuropsychological Society, 15(4), 580–589.CrossRefGoogle ScholarPubMed
Duff, K., Chelune, G.J., & Dennett, K. (2011). Predicting estimates of premorbid memory functioning: Validation in a dementia sample. Archives of Clinical Neuropsychology, 26(8), 701–705.CrossRefGoogle Scholar
Duff, K., Dalley, B., Suhrie, K.R., & Hammers, D.B. (2018). Predicting premorbid scores on the repeatable battery for the assessment of neuropsychological status and their validation in an elderly sample. Archives of Clinical Neuropsychology, 34(3), 395–402.CrossRefGoogle Scholar
Edmonds, E.C., Delano-Wood, L., Clark, L.R., Jak, A.J., Nation, D.A., McDonald, C.R., Libon, D.J., Au, R., Galasko, D., Salmon, D.P., & Bondi, M.W. (2015). Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors. Alzheimer’s & Dementia, 11(4), 415–424.CrossRefGoogle ScholarPubMed
Eppig, J.S., Edmonds, E.C., Campbell, L., Sanderson-Cimino, M., Delano-Wood, L., Bondi, M.W., & Alzheimer’s Disease Neuroimaging, I. (2017). Statistically derived subtypes and associations with cerebrospinal fluid and genetic biomarkers in mild cognitive impairment: A latent profile analysis. Journal of the International Neuropsychological Society, 23(7), 564–576. doi: 10.1017/S135561771700039XCrossRefGoogle ScholarPubMed
Gibbons, L.E., Carle, A.C., Mackin, R.S., Harvey, D., Mukherjee, S., Insel, P., Curtis, S.M., Mungas, D., Crane, P.K., & Alzheimer’s Disease Neuroimaging Initiative. (2012). A composite score for executive functioning, validated in Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment. Brain Imaging and Behavior, 6(4), 517–527.CrossRefGoogle Scholar
Hansson, O., Seibyl, J., Stomrud, E., Zetterberg, H., Trojanowski, J.Q., Bittner, T., Lifke, V., Corradini, V., Eichenlaub, U., Batrla, R., & Buck, K. (2018). CSF biomarkers of Alzheimer’s disease concord with amyloid-β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimer’s & Dementia, 14(11), 1470–1481.CrossRefGoogle ScholarPubMed
Houck, Z.M., Asken, B.M., Bauer, R.M., Kontos, A.P., McCrea, M.A., McAllister, T.W., Broglio, S.P., Clugston, J.R., & Care Consortium Investigators. (2019). Multivariate base rates of low scores and reliable decline on ImPACT in healthy collegiate athletes using Care Consortium norms. Journal of the International Neuropsychological Society, 25(9), 961–971.CrossRefGoogle Scholar
Ioannidis, J.P.A. (2018). The proposal to lower p value thresholds to .005. JAMA, 319(14), 1429–1430. doi: 10.1001/jama.2018.1536CrossRefGoogle ScholarPubMed
Iverson, G.L. & Brooks, B.L. (2011). Improving accuracy for identifying cognitive impairment. In Schoenberg, M.R. & Scott, J.G. (Eds.), The Little Black Book of Neuropsychology, (pp. 923–950). Boston, MA: Springer.CrossRefGoogle Scholar
Jack, C.R., Bennett, D.A., Blennow, K., Carrillo, M.C., Dunn, B., Haeberlein, S.B., Holtzman, D.M., Jagust, W., Jessen, F., Karlawish, J., & Liu, E. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14(4), 535–562.CrossRefGoogle Scholar
Jak, A.J., Bondi, M.W., Delano-Wood, L., Wierenga, C., Corey-Bloom, J., Salmon, D.P., & Delis, D.C. (2009). Quantification of five neuropsychological approaches to defining mild cognitive impairment. The American Journal of Geriatric Psychiatry, 17(5), 368–375.CrossRefGoogle ScholarPubMed
Jak, A.J., Preis, S.R., Beiser, A.S., Seshadri, S., Wolf, P.A., Bondi, M.W., & Au, R. (2016). Neuropsychological criteria for mild cognitive impairment and dementia risk in the Framingham Heart Study. Journal of the International Neuropsychological Society, 22(9), 937–943.CrossRefGoogle ScholarPubMed
James, B.D., Wilson, R.S., Boyle, P.A., Trojanowski, J.Q., Bennett, D.A., & Schneider, J.A. (2016). TDP-43 stage, mixed pathologies, and clinical Alzheimer’s-type dementia. Brain, 139(11), 2983–2993.CrossRefGoogle ScholarPubMed
Landau, S., Thomas, B., Thurfjell, L., Schmidt, M., Margolin, R., Mintun, M., Pontecorvo, M., Baker, S.L., Jagust, W.J., & Alzheimer’s Disease Neuroimaging Initiative. (2014). Amyloid PET imaging in Alzheimer’s disease: A comparison of three radiotracers. European Journal of Nuclear Medicine and Molecular Imaging, 41(7), 1398–1407.CrossRefGoogle ScholarPubMed
Manly, J.J. & Echemendia, R.J. (2007). Race-specific norms: Using the model of hypertension to understand issues of race, culture, and education in neuropsychology. Archives of Clinical Neuropsychology, 22(3), 319–325. doi: 10.1016/j.acn.2007.01.006CrossRefGoogle ScholarPubMed
McGurn, B., Starr, J., Topfer, J., Pattie, A., Whiteman, M., Lemmon, H.A., Whalley, L.J., & Deary, I. (2004). Pronunciation of irregular words is preserved in dementia, validating premorbid IQ estimation. Neurology, 62(7), 1184–1186.CrossRefGoogle ScholarPubMed
Morris, J.C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414.CrossRefGoogle ScholarPubMed
Mortamais, M., Ash, J.A., Harrison, J., Kaye, J., Kramer, J., Randolph, C., Pose, C., Albala, B., Ropacki, M., Ritchie, C.W., & Ritchie, K. (2017). Detecting cognitive changes in preclinical Alzheimer’s disease: A review of its feasibility. Alzheimer's & Dementia, 13(4), 468–492. doi: 10.1016/j.jalz.2016.06.2365CrossRefGoogle ScholarPubMed
Nelson, P.T., Dickson, D.W., Trojanowski, J.Q., Jack, C.R., Boyle, P.A., Arfanakis, K., Rademakers, R., Alafuzoff, I., Attems, J., Brayne, C., Coyle-Gilchrist, I.T., & Schneider, J.A. (2019). Limbic-predominant age-related TDP-43 encephalopathy (LATE): Consensus working group report. Brain, 142(6), 1503–1527. doi: 10.1093/brain/awz099CrossRefGoogle ScholarPubMed
Oltra-Cucarella, J., Sánchez-SanSegundo, M., Lipnicki, D.M., Sachdev, P.S., Crawford, J.D., Pérez-Vicente, J. A., Cabello-Rodríguez, L., Ferrer-Cascales, R., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Using base rate of low scores to identify progression from amnestic mild cognitive impairment to Alzheimer’s disease. Journal of the American Geriatrics Society, 66(7), 1360–1366.CrossRefGoogle ScholarPubMed
Ossenkoppele, R., Pijnenburg, Y.A., Perry, D.C., Cohn-Sheehy, B.I., Scheltens, N.M., Vogel, J.W., Kramer, J.H., van der Vlies, A.E., Joie, R.L., Rosen, H.J., & van der Flier, W.M. (2015). The behavioural/dysexecutive variant of Alzheimer’s disease: Clinical, neuroimaging and pathological features. Brain, 138(Pt 9), 2732–2749. doi: 10.1093/brain/awv191CrossRefGoogle ScholarPubMed
Pandya, S.Y., Clem, M.A., Silva, L.M., & Woon, F.L. (2016). Does mild cognitive impairment always lead to dementia? A review. Journal of the Neurological Sciences, 369, 57–62.CrossRefGoogle ScholarPubMed
Perry, D.C., Brown, J.A., Possin, K.L., Datta, S., Trujillo, A., Radke, A., Karydas, A., Kornak, J., Sias, A.C., Rabinovici, G.D., & Gorno-Tempini, M.L. (2017). Clinicopathological correlations in behavioural variant frontotemporal dementia. Brain, 140(12), 3329–3345.CrossRefGoogle ScholarPubMed
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., & Kokmen, E. (1999). Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 56(3), 303–308.CrossRefGoogle ScholarPubMed
Pfeffer, R.I., Kurosaki, T.T., Harrah, C.H. Jr, Chance, J.M., & Filos, S. (1982). Measurement of functional activities in older adults in the community. Journal of Gerontology, 37(3), 323–329.CrossRefGoogle Scholar
Phillips, J.S., Da Re, F., Dratch, L., Xie, S.X., Irwin, D.J., McMillan, C.T., Vaishnavi, S.N., Ferrarese, C., Lee, E.B., Shaw, L.M., & Trojanowski, J.Q. (2018). Neocortical origin and progression of gray matter atrophy in nonamnestic Alzheimer’s disease. Neurobiology of Aging, 63, 75–87. doi: 10.1016/j.neurobiolaging.2017.11.008CrossRefGoogle ScholarPubMed
Rivera Mindt, M., Byrd, D., Saez, P., & Manly, J. (2010). Increasing culturally competent neuropsychological services for ethnic minority populations: A call to action. The Clinical Neuropsychologist, 24(3), 429–453. doi: 10.1080/13854040903058960CrossRefGoogle ScholarPubMed
Sherwood, B., Zhou, A.X.H., Weintraub, S., & Wang, L. (2016). Using quantile regression to create baseline norms for neuropsychological tests. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2, 12–18.Google ScholarPubMed
Thomas, K.R., Eppig, J.S., Weigand, A.J., Edmonds, E.C., Wong, C.G., Jak, A.J., Delano-Wood, L., Galasko, D.R., Salmon, D.P., Edland, S.D., & Bondi, M.W. (2019). Artificially low mild cognitive impairment to normal reversion rate in the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s & Dementia 15(4), 561–569.CrossRefGoogle ScholarPubMed
Wennberg, A.M., Whitwell, J.L., Tosakulwong, N., Weigand, S.D., Murray, M.E., Machulda, M.M., Petrucelli, L., Mielke, M.M., Jack, C.R. Jr, Knopman, D.S., & Parisi, J.E. (2019). The influence of tau, amyloid, alpha-synuclein, TDP-43, and vascular pathology in clinically normal elderly individuals. Neurobiology of Aging, 77, 26–36.CrossRefGoogle ScholarPubMed
Supplementary material: File

Asken et al. supplementary materials

Asken et al. supplementary materials 1

Download Asken et al. supplementary materials(File)
File 19.1 KB
Supplementary material: File

Asken et al. supplementary materials

Asken et al. supplementary materials 2

Download Asken et al. supplementary materials(File)
File 59.7 KB