FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease

Om Roy; Yashar Moshfeghi; Agustin Ibanez; Francisco Lopera; Mario A Parra; Keith M Smith

doi:10.1162/netn_a_00411

FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease

Netw Neurosci. 2024 Dec 10;8(4):1467-1490. doi: 10.1162/netn_a_00411. eCollection 2024.

Authors

Om Roy¹, Yashar Moshfeghi¹, Agustin Ibanez^{2

3}, Francisco Lopera⁴, Mario A Parra⁵, Keith M Smith¹

Affiliations

¹ Computer and Information Sciences, University of Strathclyde, Glasgow, UK.
² Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile.
³ Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
⁴ Neuroscience Group of Antioquia, Medicine School, University of Antioquia, Medellín, Colombia.
⁵ Psychological Sciences and Health, University of Strathclyde, Glasgow, UK.

Abstract

Measuring transient functional connectivity is an important challenge in electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high-temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a methodology to overcome these problems called filter average short-term (FAST) functional connectivity. First, a long-term, stable, functional connectivity is averaged across an entire study cohort for a given pair of visual short-term memory (VSTM) tasks. The resulting average connectivity matrix, containing information on the strongest general connections for the tasks, is used as a filter to analyze the transient high-temporal resolution functional connectivity of individual subjects. In simulations, we show that this method accurately discriminates differences in noisy event-related potentials (ERPs) between two conditions where standard connectivity and other comparable methods fail. We then apply this to analyze an activity related to visual short-term memory binding deficits in two cohorts of familial and sporadic Alzheimer's disease (AD)-related mild cognitive impairment (MCI). Reproducible significant differences were found in the binding task with no significant difference in the shape task in the P300 ERP range. This allows new sensitive measurements of transient functional connectivity, which can be implemented to obtain results of clinical significance.

Keywords: Alzheimer’s disease; Dynamic functional connectivity; EEG; FAST; P300; Working memory.

Plain language summary

Filter average short-term (FAST) connectivity is an EEG analysis method that enhances detection of dynamic functional connectivity changes during cognitive events like event-related potentials, effectively handling EEG noise and maximizing temporal resolution. It reduces the required trial numbers for reliable analysis, particularly beneficial for studying tasks such as working memory. FAST connectivity complements traditional methods by focusing on temporal connectivity patterns, showing superior performance in simulations compared with standard measures. Applied to Alzheimer’s datasets, it identifies significant differences in brain activity during visual short-term memory tasks, highlighting its potential for understanding neurological conditions.