Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden

Kim, Soyun; Adams, Jenna N.; Chappel-Farley, Miranda G.; Keator, David; Janecek, John; Taylor, Lisa; Mikhail, Abanoub; Hollearn, Martina; McMillan, Liv; Rapp, Paul; Yassa, Michael A.

doi:10.1016/j.neuropsychologia.2023.108727

Cited by 4 publications

(7 citation statements)

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“…Closing this diagnostic gap is therefore crucial. We demonstrated that, like previous work, performance on the MST is a reliable predictor MCI with an AUC of 0.81 (Kim et al, 2023, Belliart-Guérin et al, 2023). However, the predictive accuracy was significantly improved to an AUC of 0.94 when cognitive modeling techniques were applied.…”

Section: Discussionsupporting

confidence: 88%

“…Given that this task is resistant to practice effects and can be easily performed remotely, it has emerged as an ideal candidate for clinical use as a digital biomarker for AD. However, work investigating whether performance on the MST exceeds traditional neuropsychological tests in stratifying individuals with and without cognitive impairment has yielded mixed results (Belliart-Guérin and Planche, 2023; Kim et al, 2023). We have previously demonstrated that cognitive modeling can be applied on the MST and shown how multiple cognitive metrics can be inferred from these models, but we did not know whether these new metrics would aid the predictive value of the MST.…”

Section: Discussionmentioning

confidence: 99%

“…Cerebral Aβ deposition is present up to 20 years before clinical cognitive symptoms are detected, highlighting the need for more sensitive tasks (Sperling et al, 2011; Li et al, 2024). A recent study demonstrated that combining performance on multiple versions of the MST could modestly predict amyloid status in cognitively normal older adults (Kim et al, 2023). We found that, like this work, performance on the MST could modestly predict Aβ status (AUC=0.64).…”

Section: Discussionmentioning

confidence: 99%

“…Given that the hippocampus (and entorhinal cortex which serves as a gateway to the hippocampus) is one of the first affected by aging and AD (Small et al, 1999, 2011; Morrison and Hof, 2002; Sabuncu, 2011), its unsurprising that performance declines with age and AD (Ally et al, 2013; Stark et al, 2013). Further, work has demonstrated that the MST can predict early cognitive changes in AD and this task has been used in multiple clinical trials including A4 and HOPE4MCI (Papp et al, 2020; Belliart-Guérin and Planche, 2023; Kim et al, 2023; Mohs et al, 2024)…”

Section: Introductionmentioning

confidence: 99%

“…Given that the hippocampus (and entorhinal cortex which serves as a gateway to the hippocampus) is one of the first affected by aging and AD (Small et al, 1999(Small et al, , 2011Morrison and Hof, 2002;Sabuncu, 2011), its unsurprising that performance declines with age and AD (Ally et al, 2013;Stark et al, 2013). Further, work has demonstrated that the MST can predict early cognitive changes in AD and this task has been used in multiple clinical trials including A4 and HOPE4MCI (Papp et al, 2020;Belliart-Guérin and Planche, 2023;Kim et al, 2023;Mohs et al, 2024) The MST's traditional metrics are designed to be simple and robust, but obscure potentially useful aspects of memory performance. Cognitive modeling of individual's memory can give a richer understanding of mechanisms (Norman et al, 2001) and how these are altered by aging or cognitive impairments (Lee et al, 2020;Chwiesko et al, 2023;Mulhauser et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer’s Disease

Vanderlip,

Lee,

Stark

2024

Preprint

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AD related pathologies, such as beta-amyloid (Aβ) and phosphorylated tau (pTau), are evident decades before any noticeable decline in memory occurs. Identifying individuals during this asymptomatic phase is crucial for timely intervention. The Mnemonic Similarity Task (MST), a modified recognition memory task, is especially relevant for early AD screening, as it assesses hippocampal integrity, a region affected (both directly and indirectly) early in the progression of the disease. Further, strong inferences on the underlying cognitive mechanisms that support performance on this task can be made using Bayesian cognitive modeling. We assessed whether analyzing MST performance using a cognitive model could detect subtle changes in cognitive function and AD biomarker status prior to overt cognitive decline. We analyzed MST data from >200 individuals (young, cognitively healthy older adults, and individuals with MCI), a subset of which also had existing CSF Aβ and pTau data. Traditional performance scores and cognitive modeling using multinomial processing trees was applied to each participants MST data using Bayesian approach-es. We assessed how well each could predict age group, memory ability, MCI status, Aβ/pTau sta-tus using ROC analyses. Both approaches predicted age group membership equally, but cognitive modeling approaches exceeded traditional metrics in all other comparisons. This work establishes that cognitive modeling of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for both screening and monitoring older adults during the asymptomatic phase of AD.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer’s Disease

Vanderlip,

Lee,

Stark

2024

Preprint

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Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer’s spectrum

Vanderlip,

Stark

2024

Preprint

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Digital cognitive assessments, particularly those that can be done at home, present as low burden biomarkers for participants and patients alike, but their effectiveness in diagnosis of Alzheimer's or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk for cognitive decline. We analyzed >500 ADNI participants who underwent a brief digital cognitive assessment and amyloid/tau PET scans, examining their ability to distinguish cognitive status and predict cognitive decline. Performance on the digital cognitive assessment were superior to both cortical amyloid and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation. Digital assessments are effective in identifying at-risk individuals, supporting their utility as low-burden tools for early Alzheimer's detection and monitoring.

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Amyloid-β deposition in basal frontotemporal cortex is associated with selective disruption of temporal mnemonic discrimination

Vanderlip,

Taylor,

Kim

et al. 2024

Preprint

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Cerebral amyloid-beta (Aβ) accumulation, a hallmark pathology of Alzheimer’s disease (AD), precedes clinical impairment by two to three decades. However, it is unclear whether Aβ contributes to subtle memory deficits observed during the preclinical stage. The heterogenous emergence of Aβ deposition may selectively impact certain memory domains, which rely on distinct underlying neural circuits. In this context, we tested whether specific domains of mnemonic discrimination, a neural computation essential for episodic memory, exhibit specific deficits related to early Aβ deposition. We tested 108 cognitively unimpaired human older adults (66% female) who underwent 18F-florbetapir positron emission tomography (Aβ-PET), and a control group of 35 young adults, on a suite of mnemonic discrimination tasks taxing object, spatial, and temporal domains. We hypothesized that Aβ pathology would be selectively associated with temporal discrimination performance due to Aβ’s propensity to accumulate in the basal frontotemporal cortex, which supports temporal processing. Consistent with this hypothesis, we found a dissociation in which generalized age-related deficits were found for object and spatial mnemonic discrimination, while Aβ-PET levels were selectively associated with deficits in temporal mnemonic discrimination. Further, we found that higher Aβ-PET levels in medial orbitofrontal and inferior temporal cortex, regions supporting temporal processing, were associated with greater temporal mnemonic discrimination deficits, pointing to the selective vulnerability of circuits related to temporal processing early in AD progression. These results suggest that Aβ accumulation within basal frontotemporal regions may disrupt temporal mnemonic discrimination in preclinical AD, and may serve as a sensitive behavioral biomarker of emerging AD progression.

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Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden

Cited by 4 publications

References 96 publications

Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer’s Disease

Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer’s Disease

Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer’s spectrum

Amyloid-β deposition in basal frontotemporal cortex is associated with selective disruption of temporal mnemonic discrimination

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