ArticleGeroScience2024
Fingerprints of decreased cognitive performance on fractal connectivity dynamics in healthy aging.
Article in GeroScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Global scale-free brain activity as a potential neural signature of visual information processing in aging.Frontiers in aging neuroscience · 2026Article
- Whole-body networks: a holistic approach for studying aging.GeroScience · 2025Article
- Reduced temporal variability of cortical excitation/inhibition ratio in schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- Exploring the complexity of EEG patterns in Parkinson's disease.GeroScience · 2025Article
- Resting-state neural dynamics changes in older adults with post-COVID syndrome and the modulatory effect of cognitive training and sex.GeroScience · 2025Article
- Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.Frontiers in neuroscience · 2024Article
- Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals.Frontiers in neuroscience · 2024Article
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7 authors.
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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.
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