Longitudinal assessment of the conversion of mild cognitive impairment into
Alzheimer’s dementia: Observations and mechanisms from neuropsychological
testing and electrophysiology
Authors: Dunstan Dominic M , Barvas Edoardo , Guttmann Susanna , Frusciante Roberto , Viti Beatrice , Volpini Mirco , Cannuccia Milena , Monaldini Chiara , Tamagnini Francesco , Goodfellow Marc , Tait Luke 
Published: January 2025

Abstract

INTRODUCTION
Elucidating and better understanding functional biomarkers of
Alzheimer’s disease (AD) is crucial. By analysing a detailed longitudinal
dataset, this study aimed to create a model-based toolset to characterise
and understand the conversion of mild cognitive impairment (MCI) to
AD.

METHODS
EEG, MRI, and neuropsychological data were collected from participants
in San Marino: AD (n = 10), MCI (n = 20), and controls (n = 11). Across two
additional years, MCI participants were classified as converters or
non-converters.

RESULTS
We identified the Stroop Color and Word Test as the largest
differentiator for MCI conversion (ROC AUC = 0.795). This was underpinned by
disconnectivity in working memory and attention networks. Unsupervised
clustering of EEG spectra also differentiated MCI conversion (ROC AUC =
0.710) and was underpinned by reduced excitatory and enhanced inhibitory
synaptic efficacy in (prodromal) AD. Combining electrophysiological and
neuropsychological assessments increased the accuracy of the differentiation
(ROC AUC = 0.880) in comparison to each measure considered
individually.

CONCLUSION
Combining electrophysiological and neuropsychological assessment with
mathematical models can inform the development of non-invasive, low-cost
tools for the early diagnosis of AD.

Highlights

We analysed longitudinal changes in EEG and neuropsychological
assessments in MCI

Stroop Color and Word Test error scores were lower in MCI
converters

The degree of impairment was found to be correlated with
functional disconnectivity

Unsupervised clustering of EEG spectra characterised patterns
associated with disease

Mathematical modelling revealed reduced excitatory synaptic
efficacy in (prodromal) AD

Research in Context
Systematic review:The authors used PubMed to
review the literature on the use of inexpensive modalities, including EEG
and neurophysiological testing, for characterising the progression of MCI to
AD. Although promising, existing work suggests the full potential of these
methods as tools for understanding prodromal AD is still lacking.
Interpretation:A novel application of a
clustering algorithm to EEG spectra revealed different patient diagnoses
could largely be characterised by their cluster assignment. We also found
differences in a particular neuropsychological test, the Stroop Color and
Word Test. Using mathematical modelling we found there were both network and
synaptic mechanisms that underlie these differences.
Future directions:Using the methods described
herein to build markers for testing MCI to AD conversion on a large
independent cohort will be crucial to understanding the full impact and
applicability of these approaches. This may ultimately lead to a better
characterisation and understanding of the diagnosis and prognosis of
AD.