Amnestic Mild Cognitive Impairment is Characterized by the Inability to Recover from Proactive Semantic Interference across Multiple Learning Trials

Different aspects of failing to recover from proactive semantic interference predicts rate of progression from amnestic mild cognitive impairment to dementia

Introduction

This study investigated the role of proactive semantic interference (frPSI) in predicting the progression of amnestic Mild Cognitive Impairment (aMCI) to dementia, taking into account various cognitive and biological factors.

Methods

The research involved 89 older adults with aMCI who underwent baseline assessments, including amyloid PET and MRI scans, and were followed longitudinally over a period ranging from 12 to 55 months (average 26.05 months).

Results

The findings revealed that more than 30% of the participants diagnosed with aMCI progressed to dementia during the observation period. Using Cox Proportional Hazards modeling and adjusting for demographic factors, global cognitive function, hippocampal volume, and amyloid positivity, two distinct aspects of frPSI were identified as significant predictors of a faster decline to dementia. These aspects were fewer correct responses on a frPSI trial and a higher number of semantic intrusion errors on the same trial, with 29.5% and 31.6 % increases in the likelihood of more rapid progression to dementia, respectively.

Discussion

These findings after adjustment for demographic and biological markers of Alzheimer's Disease, suggest that assessing frPSI may offer valuable insights into the risk of dementia progression in individuals with aMCI.

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

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) 2023 APA, all rights reserved).

Salient Cognitive Paradigms to Assess Preclinical Alzheimer's Disease

Despite the growing emphasis to identify early biological markers that can detect the progressive accumulation of brain pathology in the complex pathophysiologic cascade that occurs in Alzheimer's disease (AD), we continue to employ the same neuropsychological paradigms that were developed to detect dementia or frank cognitive impairment. It has become increasingly clear that we cannot expect to measure clinically meaningful change in relationship to these emerging preclinical biomarkers using these traditional cognitive assessment paradigms, nor will we advance the efforts to identify the earliest cognitive changes that emerge in AD. Over the last decade, a few novel promising cognitive assessment paradigms have emerged that have shown promise in identifying subtle cognitive deficits in AD which aids in early detection and monitoring of meaningful cognitive change over time. Some of these cognitive assessment paradigms are reviewed here, including semantic interference, semantic intrusion errors, memory binding, and binding of face and name associations. These paradigms may be useful for AD clinical trials focused on secondary prevention if there is sufficient rigor to suggest that they correlate with AD biomarkers, having robust sensitivity, specificity, and predictive utility among culturally and linguistically diverse populations at-risk for AD.