Journal Article10.1037/neu0000847
Neural network process simulations support a distributed memory system and aid design of a novel computer adaptive digital memory test for preclinical and prodromal Alzheimer's disease.
John L. Stricker,Nick Corriveau-Lecavalier,Daniela A. Wiepert,Hugo Botha,David T. Jones,Nikki H. Stricker +5 more
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TL;DR: A mirrored cascaded architecture addresses the limitations of traditional feed forward neural networks, provides support for a distributed memory system, and emphasizes the importance of context to avoid interference.
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Abstract: OBJECTIVE
Growing evidence supports the importance of learning as a central deficit in preclinical/prodromal Alzheimer's disease. The aims of this study were to conduct a series of neural network simulations to develop a functional understanding of a distributed, nonmodular memory system that can learn efficiently without interference. This understanding is applied to the development of a novel digital memory test.
METHOD
Simulations using traditional feed forward neural network architectures to learn simple logic problems are presented. The simulations demonstrate three limitations: (a) inefficiency, (b) an inability to learn problems consistently, and (c) catastrophic interference when given multiple problems. A new mirrored cascaded architecture is introduced to address these limitations, with support provided by a series of simulations.
RESULTS
The mirrored cascaded architecture demonstrates efficient and consistent learning relative to feed forward networks but also suffers from catastrophic interference. Addition of context values to add the capability of distinguishing features as part of learning eliminates the problem of interference in the mirrored cascaded, but not the feed forward, architectures.
CONCLUSIONS
A mirrored cascaded architecture addresses the limitations of traditional feed forward neural networks, provides support for a distributed memory system, and emphasizes the importance of context to avoid interference. These process models contributed to the design of a digital computer-adaptive word list learning test that places maximum stress on the capability to distinguish specific episodes of learning. Process simulations provide a useful method of testing models of brain function and contribute to new approaches to neuropsychological assessment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Citations
Repurposing Artificial Intelligence Tools for Disease Modeling: Case Study of Face Recognition Deficits in Neurodegenerative Diseases.
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TL;DR: In this paper , the authors evaluate whether degrading the architecture of artificial intelligence (AI) face recognition algorithms can model deficits in diseases such as prosopagnosia, autism, Alzheimer's disease, and dementias.
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Stricker Learning Span criterion validity: a remote self-administered multi-device compatible digital word list memory measure shows similar ability to differentiate amyloid and tau PET-defined biomarker groups as in-person Auditory Verbal Learning Test
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TL;DR: The Stricker Learning Span (SLS) is a computer-adaptive digital word list memory test specifically designed for remote assessment and self-administration on a web-based multi-device platform as mentioned in this paper .
Continuous Associations Between Remote Self-Administered Cognitive Measures and Imaging Biomarkers of Alzheimers Disease
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TL;DR: This cross-sectional study investigates the association between a remote, self-administered cognitive assessment (Mayo Test Drive) and Alzheimer's disease-related imaging biomarkers in 684 adults, demonstrating its ability to detect subtle cognitive changes and criterion validity.
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Mayo Normative Studies: regression-based normative data for remote self-administration of the Stricker Learning Span, Symbols Test and Mayo Test Drive Screening Battery Composite and validation in individuals with Mild Cognitive Impairment and dementia
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TL;DR: This study develops regression-based normative data for remote cognitive assessments, validating its use in cognitively unimpaired, mild cognitive impairment, and dementia individuals, and finds typical normative models suitable for remote self-administered measures, with device type and response input source not significantly impacting variance.
Psychometric and adherence considerations for high-frequency, smartphone-based cognitive screening protocols in older adults
Louisa I. Thompson,Alyssa N. De Vito,Zachary J. Kunicki,Sheina Emrani,Jennifer Strenger,Caroline O. Nester,Karra Harrington,Nelson Roque,Masood Manoocheri,Stephen Salloway,Stephen Correia,Richard N. Jones,Martin J. Sliwinski +12 more
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