Fingerprints of decreased cognitive performance on fractal connectivity dynamics in healthy aging

Abstract

Analysis of brain functional connectivity (FC) could provide insight in how and why cognitive functions decline even in healthy aging (HA). Despite FC being established as fluctuating over time even in the resting state (RS), dynamic functional connectivity (DFC) studies involving healthy elderly individuals and assessing how these patterns relate to cognitive performance are yet scarce. In our recent study we showed that fractal temporal scaling of functional connections in RS is not only reduced in HA, but also predicts increased response latency and reduced task solving accuracy. However, in that work we did not address changes in the dynamics of fractal connectivity (FrC) strength itself and its plausible relationship with mental capabilities. Therefore, here we analyzed RS electroencephalography recordings of the same subject cohort as previously, consisting of 24 young and 19 healthy elderly individuals, who also completed 7 different cognitive tasks after data collection. Dynamic fractal connectivity (dFrC) analysis was carried out via sliding-window detrended cross-correlation analysis (DCCA). A machine learning method based on recursive feature elimination was employed to select the subset of connections most discriminative between the two age groups, identifying 56 connections that allowed for classifying participants with an accuracy surpassing 92%. Mean of DCCA was found generally increased, while temporal variability of FrC decreased in the elderly when compared to the young group. Finally, dFrC indices expressed an elaborate pattern of associations—assessed via Spearman correlation—with cognitive performance scores in both groups, linking fractal connectivity strength and variance to increased response latency and reduced accuracy in the elderly population. Our results provide further support for the relevance of FrC dynamics in understanding age-related cognitive decline and might help to identify potential targets for future intervention strategies.

Multiscale Detrended Cross-Correlation Coefficient: Estimating Coupling in Nonstationary Neurophysiological Signals

“…The calculation of MDC 3 using different scaling exponents would add another layer of complexity to the interpretation of the outputs. Recently, a real-time algorithm for the estimation of DCCC was presented (50,51), which can be extended for MDC 3 as well. This means that MDC 3 can be used in brain-computer interfaces or clinical monitoring of patients, where constant tracking of network dynamics is needed.…”

Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance

“…Furthermore, in case of multivariate signals, performing DCCA in a pairwise manner yields SPD matrices (as demonstrated below). Recent studies also indicate that the concept of DCCA could be utilized effectively to capture relevant functional connectivity patterns in the human brain (Chen et al, 2018 ; Ide and Chiang-shan, 2018 ; Kaposzta et al, 2023 ). Therefore, these notions render DCCA as a potentially attractive candidate in providing input for Riemannian geometry-based classification schemes of neural data.…”