Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-29T09:18:35.988Z Has data issue: false hasContentIssue false

Semantic intrusion errors as a function of age, amyloid, and volumetric loss: a confirmatory path analysis

Published online by Cambridge University Press:  18 January 2021

D. Diane Zheng
Affiliation:
Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
Rosie E. Curiel Cid
Affiliation:
Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA 1Florida Alzheimer’s Disease Research Center, Miami Beach, FL, USA
Ranjan Duara
Affiliation:
1Florida Alzheimer’s Disease Research Center, Miami Beach, FL, USA Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
Marcela Kitaigorodsky
Affiliation:
Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
Elizabeth Crocco
Affiliation:
Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
David A. Loewenstein*
Affiliation:
Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA 1Florida Alzheimer’s Disease Research Center, Miami Beach, FL, USA
*
Correspondence should be addressed to: David A. Loewenstein, Professor of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1695 NW 9th Avenue, Suite 3202, Miami, Florida33136, USA. Phone: (305) 355-9080; Fax: 1 (305) 355-9076. Email: [email protected].

Abstract

Objective:

To examine the direct and indirect effects of age, APOE ϵ4 genotype, amyloid positivity, and volumetric reductions in AD-prone brain regions as it relates to semantic intrusion errors reflecting proactive semantic interference (PSI) and the failure to recover from proactive semantic interference (frPSI) on the Loewenstein-Acevedo Scales of Semantic Interference and Learning (LASSI-L), a cognitive stress test that has been consistently more predictive of preclinical and prodromal Alzheimer’s disease (AD) than traditional list-learning tests.

Design:

Cross-sectional study.

Setting:

1Florida Alzheimer’s Disease Research Center baseline study.

Participants:

Two-hundred and twelve participants with Mini-Mental State Examination (MMSE) score above 16 and a broad array of cognitive diagnoses ranging from cognitively normal (CN) to dementia, of whom 58% were female, mean age of 72.1 (SD 7.9).

Measures:

Participants underwent extensive clinical and neuropsychological evaluations, MR and amyloid Positron Emission Tomography/Computer/Computer Tomography (PET/CT) imaging, and analyses of APOE ϵ4 genotype. Confirmatory path analyses were conducted in the structural equation modeling framework that estimated multiple equations simultaneously while controlling for important covariates such as sex, education, language of evaluation, and global cognitive impairment.

Results:

Both amyloid positivity and decreased brain volumes in AD-prone regions were directly related to LASSI-L Cued B1 and Cued B2 intrusions (sensitive to PSI and frPSI effects) even after controlling for covariates. APOE ϵ4 status did not evidence direct effects on these LASSI-L cognitive markers, but rather exerted their effects on amyloid positivity, which in turn related to PSI and frPSI. Similarly, age did not have a direct relationship with LASSI-L scores, but exerted its effects indirectly through amyloid positivity and volumes of AD-prone brain regions.

Conclusions:

Our study provides insight into the relationships among age, APOE ϵ4, amyloid, and brain volumetric reductions as it relates to semantic intrusion errors. The investigation expands our understanding of the underpinnings of PSI and frPSI intrusions in a large cohort.

Type
Original Research Article
Copyright
© International Psychogeriatric Association 2021

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.)

References

Arbuckle, J. L., Marcoulides, G. A. and Schumacker, R. E. (1996). Full information estimation in the presence of incomplete data. Advanced Structural Equation Modeling: Issues and Techniques, 243, 277.Google Scholar
Beekly, D. L. et al. (2007). The National Alzheimer’s Coordinating Center (NACC) database: the uniform data set. Alzheimer Disease & Associated Disorders, 21, 249258.CrossRefGoogle ScholarPubMed
Benedict, R. H., Schretlen, D., Groninger, L. and Brandt, J. (1998). Hopkins Verbal Learning Test–Revised: Normative data and analysis of inter-form and test-retest reliability. The Clinical Neuropsychologist, 12, 4355.CrossRefGoogle Scholar
Capp, K. E. et al. (2019). Semantic intrusion error ratio distinguishes between cognitively impaired and cognitively intact African American older adults. Journal of Alzheimer’s Disease, 16. Google Scholar
Crocco, E., Curiel, R. E., Acevedo, A., Czaja, S. J. and Loewenstein, D. A. (2014). An evaluation of deficits in semantic cueing and proactive and retroactive interference as early features of Alzheimer’s disease. The American Journal of Geriatric Psychiatry, 22, 889897.CrossRefGoogle ScholarPubMed
CurielCid, R. E. et al. (2013). A new scale for the evaluation of proactive and retroactive interference in mild cognitive impairment and early Alzheimer’s disease. Aging, 1, 1000102.Google Scholar
Curiel Cid, R. E. et al. (2019). A cognitive stress test for prodromal Alzheimer’s disease: Multiethnic generalizability. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 11, 550559.Google ScholarPubMed
Curiel Cid, R. et al. (2020). A novel method of evaluating semantic intrusion errors to distinguish between amyloid positive and negative groups on the Alzheimer’s disease continuum. Journal of Psychiatric Research, 124, 131136.CrossRefGoogle ScholarPubMed
Desikan, R. S. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31, 968980.Google ScholarPubMed
Dickerson, B. et al. (2011). Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology, 76, 13951402.CrossRefGoogle ScholarPubMed
Ferreira, D., Pereira, J. B., Volpe, G. and Westman, E. (2019). Subtypes of Alzheimer’s disease display distinct network abnormalities extending beyond their pattern of brain atrophy. Frontiers in Neurology, 10, 524.CrossRefGoogle ScholarPubMed
Folstein, M. F., Folstein, S. E. and Mchugh, P. R. (1975). “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189198.Google ScholarPubMed
Jack, C. R. et al. (2017). Defining imaging bio-marker cut points for brain aging and Alzheimer’s disease. Alzheimer’s & Dementia, 13, 205216.CrossRefGoogle Scholar
Lizarraga, G. et al. (2016). A Web Platform for data acquisition and analysis for Alzheimer’s disease. SoutheastCon 2016, Norfolk, VA, 2016, pp. 1–5.CrossRefGoogle Scholar
Loewenstein, D. A. et al. (2012). An investigation of PreMCI: subtypes and longitudinal outcomes. Alzheimer’s & Dementia, 8, 172179.CrossRefGoogle ScholarPubMed
Loewenstein, D. A. et al. (2016). A novel cognitive stress test for the detection of preclinical Alzheimer disease: discriminative properties and relation to amyloid load. The American Journal of Geriatric Psychiatry, 24, 804813.CrossRefGoogle ScholarPubMed
Loewenstein, D. A. et al. (2017a). Recovery from proactive semantic interference and MRI volume: A replication and extension study. Journal of Alzheimer’s Disease, 59, 131139.CrossRefGoogle ScholarPubMed
Loewenstein, D. A. et al. (2017b). Recovery from proactive semantic interference in mild cognitive impairment and normal aging: relationship to atrophy in brain regions vulnerable to Alzheimer’s disease. Journal of Alzheimer’s Disease, 56, 11191126.CrossRefGoogle ScholarPubMed
Loewenstein, D. A. et al. (2018a). Utilizing semantic intrusions to identify amyloid positivity in mild cognitive impairment. Neurology, 91, e976e984.CrossRefGoogle ScholarPubMed
Loewenstein, D. A., Curiel, R. E., Duara, R. and Buschke, H. (2018b). Novel cognitive paradigms for the detection of memory impairment in preclinical Alzheimer’s disease. Assessment, 25, 348359.CrossRefGoogle ScholarPubMed
Lucas, J. A. et al. (1998). Mayo’s older Americans normative studies: category fluency norms. Journal of Clinical and Experimental Neuropsychology, 20, 194200.CrossRefGoogle ScholarPubMed
Mackinnon, D. P., Fairchild, A. J. and Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593614.CrossRefGoogle ScholarPubMed
Matias-Guiu, J. A. et al. (2018). Comparison between FCSRT and LASSI-L to detect early stage Alzheimer’s disease. Journal of Alzheimer’s Disease, 61, 103111.CrossRefGoogle ScholarPubMed
Matías-Guiu, J. A. et al. (2017). Validation of the Spanish version of the LASSI-L for diagnosing mild cognitive impairment and Alzheimer’s disease. Journal of Alzheimer’s Disease, 56, 733742.CrossRefGoogle ScholarPubMed
Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43, 24122414.CrossRefGoogle ScholarPubMed
Murray, M. E. et al. (2011). Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. The Lancet Neurology, 10, 785796.CrossRefGoogle ScholarPubMed
Muthen, L. K. and Muthen, B. O. (1998–2012). Mplus user’s guide. 7th ed. Los Angeles, CA: Muthen & Muthen.Google Scholar
Petersen, R. C. et al. (2014). Mild cognitive impairment: a concept in evolution. Journal of Internal Medicine, 275, 214228.CrossRefGoogle ScholarPubMed
Reitan, R. M. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271276.CrossRefGoogle Scholar
Rowe, C. C. et al. (2008). Imaging of amyloid β in Alzheimer’s disease with 18F-BAY94-9172, a novel PET tracer: proof of mechanism. The Lancet Neurology, 7, 129135.CrossRefGoogle ScholarPubMed
Rowe, C. C. et al. (2017). 18F-Florbetaben PET beta-amyloid binding expressed in Centiloids. European Journal of Nuclear Medicine and Molecular Imaging, 44(12), 20532059. doi: 10.1007/s00259-017-3749-6.CrossRefGoogle Scholar
Sánchez, S. M. et al. (2017). Failure to recover from proactive semantic interference and abnormal limbic connectivity in asymptomatic, middle-aged offspring of patients with late-onset Alzheimer’s disease. Journal of Alzheimer’s Disease, 60, 11831193.CrossRefGoogle ScholarPubMed
SAS Institute (2019). SAS 9.4. Cary, NC: SAS Institute Inc.Google Scholar
Seibyl, J. et al. (2016). Impact of training method on the robustness of the visual assessment of 18F-Florbetaben PET scans: results from a phase-3 study. Journal of Nuclear Medicine, 57, 900906.CrossRefGoogle ScholarPubMed
Smith, S. M. et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23, S208S219.CrossRefGoogle ScholarPubMed
Storandt, M., Mintun, M. A., Head, D. and Morris, J. C. (2009). Cognitive decline and brain volume loss as signatures of cerebral amyloid-β peptide deposition identified with Pittsburgh compound B: cognitive decline associated with Aβ deposition. Archives of Neurology, 66, 14761481.CrossRefGoogle Scholar
Torres, V. L. et al. (2019). Types of errors on a semantic interference task in mild cognitive impairment and dementia. Neuropsychology, 33, 670.CrossRefGoogle ScholarPubMed
Wechsler, D. (2008). Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV). San Antonio, TX: Pearson.Google Scholar
Yuan, K.-H. and Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30, 165200.CrossRefGoogle Scholar