| 1. |
- Berron, David, et al.
(författare)
-
A remote digital memory composite to detect cognitive impairment in memory clinic samples in unsupervised settings using mobile devices
- 2024
-
Ingår i: npj Digital Medicine. - 2398-6352. ; 7:1
-
Tidskriftsartikel (refereegranskat)abstract
- Remote monitoring of cognition holds the promise to facilitate case-finding in clinical care and the individual detection of cognitive impairment in clinical and research settings. In the context of Alzheimer’s disease, this is particularly relevant for patients who seek medical advice due to memory problems. Here, we develop a remote digital memory composite (RDMC) score from an unsupervised remote cognitive assessment battery focused on episodic memory and long-term recall and assess its construct validity, retest reliability, and diagnostic accuracy when predicting MCI-grade impairment in a memory clinic sample and healthy controls. A total of 199 participants were recruited from three cohorts and included as healthy controls (n = 97), individuals with subjective cognitive decline (n = 59), or patients with mild cognitive impairment (n = 43). Participants performed cognitive assessments in a fully remote and unsupervised setting via a smartphone app. The derived RDMC score is significantly correlated with the PACC5 score across participants and demonstrates good retest reliability. Diagnostic accuracy for discriminating memory impairment from no impairment is high (cross-validated AUC = 0.83, 95% CI [0.66, 0.99]) with a sensitivity of 0.82 and a specificity of 0.72. Thus, unsupervised remote cognitive assessments implemented in the neotiv digital platform show good discrimination between cognitively impaired and unimpaired individuals, further demonstrating that it is feasible to complement the neuropsychological assessment of episodic memory with unsupervised and remote assessments on mobile devices. This contributes to recent efforts to implement remote assessment of episodic memory for case-finding and monitoring in large research studies and clinical care.
|
|
| 2. |
- Güsten, Jeremie, et al.
(författare)
-
Age impairs mnemonic discrimination of objects more than scenes : A web-based, large-scale approach across the lifespan
- 2021
-
Ingår i: Cortex. - : Elsevier BV. - 0010-9452. ; 137, s. 138-148
-
Tidskriftsartikel (refereegranskat)abstract
- Recent findings suggest that the effect of aging on recognition memory is modality-dependent, affecting memory for objects and scenes differently. However, the lifespan trajectory of memory decline in these domains remains unclear. A major challenge for assessing domain-specific trajectories is the need to utilize different types of stimuli for each domain (objects and scenes). We tested the large sample required to cover much of the adult lifespan using a large stimulus range via web-based assessments. 1554 participants (18–77 years) performed an online mnemonic discrimination task, tested on a pool of 2708 stimuli (Berron et al., 2018). Using corrected hit-rate (Pr) as a measure of performance, we show age-related decline in mnemonic discrimination in both domains, notably with a stronger decline in object memory, driven by a linear increase in the false recognition rate with advancing age. These data are the first to identify a linear age-related decline in mnemonic discrimination and a stronger, linear trajectory of decline in the object domain. Our data can inform basic and clinical memory research on the effects of aging on memory and help advancing the implementation of digital cognitive research tools.
|
|
| 3. |
- Güsten, Jeremie, et al.
(författare)
-
Bayesian modeling of item heterogeneity in dichotomous recognition memory data and prospects for computerized adaptive testing
- 2022
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1
-
Tidskriftsartikel (refereegranskat)abstract
- Most current models of recognition memory fail to separately model item and person heterogeneity which makes it difficult to assess ability at the latent construct level and prevents the administration of adaptive tests. Here we propose to employ a General Condorcet Model for Recognition (GCMR) in order to estimate ability, response bias and item difficulty in dichotomous recognition memory tasks. Using a Bayesian modeling framework and MCMC inference, we perform 3 separate validation studies comparing GCMR to the Rasch model from IRT and the 2-High-Threshold (2HT) recognition model. First, two simulations demonstrate that recovery of GCMR ability estimates with varying sparsity and test difficulty is more robust and that estimates improve from the two other models under common test scenarios. Then, using a real dataset, face validity is confirmed by replicating previous findings of general and domain-specific age effects (Güsten et al. in Cortex 137:138–148, https://doi.org/10.1016/j.cortex.2020.12.017, 2021). Using cross-validation we show better out-of-sample prediction for the GCMR as compared to Rasch and 2HT model. In addition, we present a hierarchical extension of the model that is able to estimate age- and domain-specific effects directly, without recurring to a two-stage procedure. Finally, an adaptive test using the GCMR is simulated, showing that the test length necessary to obtain reliable ability estimates can be significantly reduced compared to a non-adaptive procedure. The GCMR allows to model trial-by-trial performance and to increase the efficiency and reliability of recognition memory assessments.
|
|
