Effects of Delay Duration on the WMS Logical Memory Performance of Older Adults with Probable Alzheimer's Disease, Probable Vascular Dementia, and Normal Cognition

Montgomery, Valencia; Stabler, Anthony; Lu, Lisa H.

doi:10.1093/arclin/acx005

Cited by 9 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, time elapsed since Logical Memory I, a metadata variable that was thought to have no apparent clinical relationship to cognitive status, was highly attended to by the other Logical Memory test features. However, recent literature 16 indicates that the time elapsed feature is indeed highly correlated to a significant distribution shift on other Logical Memory scores regardless of the subject's actual cognitive status. The model's ability to identify and respond to such variance in the input data contributes to its ability to respond to the complex input dataset in this work.…”

Section: Conclusion and Discussionmentioning

confidence: 95%

A Transformer Approach for Cognitive Impairment Classification and Prediction

Liu,

Weakley,

Zhang

et al. 2024

Alzheimer Disease &Amp; Associated Disorders

View full text Add to dashboard Cite

Introduction: Early classification and prediction of Alzheimer disease (AD) and amnestic mild cognitive impairment (aMCI) with noninvasive approaches is a long-standing challenge. This challenge is further exacerbated by the sparsity of data needed for modeling. Deep learning methods offer a novel method to help address these challenging multiclass classification and prediction problems. Methods: We analyzed 3 target feature-sets from the National Alzheimer’s Coordinating Center (NACC) dataset: (1) neuropsychological (cognitive) data; (2) patient health history data; and (3) the combination of both sets. We used a masked Transformer-encoder without further feature selection to classify the samples on cognitive status (no cognitive impairment, aMCI, AD)—dynamically ignoring unavailable features. We then fine-tuned the model to predict the participants’ future diagnosis in 1 to 3 years. We analyzed the sensitivity of the model to input features via Feature Permutation Importance. Results: We demonstrated (1) the masked Transformer-encoder was able to perform prediction with sparse input data; (2) high multiclass current cognitive status classification accuracy (87% control, 79% aMCI, 89% AD); (3) acceptable results for 1- to 3-year multiclass future cognitive status prediction (83% control, 77% aMCI, 91% AD). Conclusion: The flexibility of our methods in handling inconsistent data provides a new venue for the analysis of cognitive status data.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 95%

A Transformer Approach for Cognitive Impairment Classification and Prediction

Liu,

Weakley,

Zhang

et al. 2024

Alzheimer Disease &Amp; Associated Disorders

View full text Add to dashboard Cite

show abstract

“…Recall was unlikely to differ between the stories solely based on the difference in delay duration (Montgomery, Harris, Stabler, & Lu, 2017). Participants were told that the purpose of the second testing session was for another component of a broader research study.…”

Section: Methodsmentioning

confidence: 99%

A Brief Period of Wakeful Rest after Learning Enhances Verbal Memory in Stroke Survivors

Evans

Stolwyk

Wong

2021

J Int Neuropsychol Soc

View full text Add to dashboard Cite

Objective: Accumulating evidence suggests that wakeful rest (a period of minimal cognitive stimulation) enhances memory in clinical populations with memory impairment. However, no study has previously examined the efficacy of this technique in stroke survivors, despite the high prevalence of post-stroke memory difficulties. We aimed to investigate whether wakeful rest enhances verbal memory in stroke survivors and healthy controls. Method: Twenty-four stroke survivors and 24 healthy controls were presented with two short stories; one story was followed by a 10-minute period of wakeful rest and the other was followed by a 10-minute visual interference task. A mixed factorial analysis of variance (ANOVA) with pairwise comparisons was used to compare participants’ story retention at two time points. Results: After 15–30 minutes, stroke survivors (p = .002, d = .73), and healthy controls (p = .001, d = .76) retained more information from the story followed by wakeful rest, compared with the story followed by an interference task. While wakeful rest remained the superior condition in healthy controls after 7 days (p = .01, d = .58), the beneficial effect was not maintained in stroke survivors (p = .35, d = .19). Conclusions: Wakeful rest is a promising technique, which significantly enhanced verbal memory after 15–30 minutes in both groups; however, no significant benefit of wakeful rest was observed after 7 days in stroke survivors. Preliminary findings suggest that wakeful rest enhances early memory consolidation processes by protecting against the effects of interference after learning in stroke survivors.

show abstract

“…The screening tool should be an instrument with a demonstrated ability to differentiate CN from MCI. The screening tool should also account for demographic parameters such as age, sex, race, ethnicity, education, and have a high correlation with the cognitive classification and diagnostic standards used in ADNI 3 (which is based on MMSE, 35 Clinical Dementia Rating, 36 and Wechsler Logical Memory Test II–Delayed Story Recall) 37 . The longitudinal assessment tools need to show sensitivity to changes over time that correlate with the imaging and CSF biomarker‐confirmed AD population (i.e., AD BM+/MCI) compared to the AD biomarker‐negative normal controls (i.e., AD BM–/CNs).…”

Section: Adni 3 Ppsb Landscape Assessment Processmentioning

confidence: 99%

“…As ADNI has continued investigations over time into the biology, pro- (which is based on MMSE, 35 Clinical Dementia Rating, 36 and Wechsler Logical Memory Test II-Delayed Story Recall). 37 The longitudinal assessment tools need to show sensitivity to changes over time that correlate with the imaging and CSF biomarker-confirmed AD population (i.e., AD BM+/MCI) compared to the AD biomarker-negative normal controls (i.e., AD BM-/CNs).…”

Section: Cewg Landscape Assessmentmentioning

confidence: 99%

The Alzheimer's Disease Neuroimaging Initiative and the role and contributions of the Private Partners Scientific Board (PPSB)

Albala,

Appelmans,

Burress

et al. 2023

Alzheimer's & Dementia

View full text Add to dashboard Cite

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Private Partners Scientific Board (PPSB) encompasses members from industry, biotechnology, diagnostic, and non‐profit organizations that have until recently been managed by the Foundation for the National Institutes of Health (FNIH) and provided financial and scientific support to ADNI programs. In this article, we review some of the major activities undertaken by the PPSB, focusing on those supporting the most recently completed National Institute on Aging grant, ADNI3, and the impact it has had on streamlining biomarker discovery and validation in Alzheimer's disease. We also provide a perspective on the gaps that may be filled with future PPSB activities as part of ADNI4 and beyond.Highlights The Private Partners Scientific board (PPSB) continues to play a key role in enabling several Alzheimer's Disease Neuroimaging Initiative (ADNI) activities. PPSB working groups have led landscape assessments to provide valuable feedback on new technologies, platforms, and methods that may be taken up by ADNI in current or future iterations.

show abstract

Effects of Delay Duration on the WMS Logical Memory Performance of Older Adults with Probable Alzheimer's Disease, Probable Vascular Dementia, and Normal Cognition

Cited by 9 publications

References 16 publications

A Transformer Approach for Cognitive Impairment Classification and Prediction

A Transformer Approach for Cognitive Impairment Classification and Prediction

A Brief Period of Wakeful Rest after Learning Enhances Verbal Memory in Stroke Survivors

The Alzheimer's Disease Neuroimaging Initiative and the role and contributions of the Private Partners Scientific Board (PPSB)

Contact Info

Product

Resources

About