PhD Scientific Days 2023

Neurosciences I.

Resting-state Fractal Brain Connectivity is Associated with Impaired Cognitive Performance in Healthy Aging

Text of the abstract

Introduction: Aging affects cognitive functions even in the absence of ongoing pathologies. The neurophysiological basis of age-related cognitive decline (CD), however, is not completely understood. Alterations in both functional brain connectivity and in the fractal scaling of neuronal dynamics have been linked to aging and cognitive performance. Recently, fractal connectivity (FrC) has been proposed – combining the two concepts – for capturing long-term interactions among brain regions. Aims: FrC was shown to be influenced by increased mental workload, however no prior studies investigated how resting-state FrC relates to cognitive performance and plausible CD in healthy aging. Methods: We recruited 19 healthy elderly (HE) and 24 young control (YC) participants, who underwent resting-state electroencephalography (EEG) measurements and comprehensive cognitive evaluation using 7 tests of the Cambridge Neurophysiological Test Automated Battery. FrC networks were reconstructed from EEG data using the recently introduced multiple-resampling cross-spectral analysis (MRCSA). Results: Elderly individuals could be characterized with increased response latency and reduced performance in 4-4 tasks, respectively, with both reaction time and accuracy being affected in two tasks. Auto- and cross-spectral exponents – characterizing regional fractal dynamics and FrC, respectively – were found reduced in HE when compared to YC over most of the cortex. Additionally, fractal scaling of frontoparietal connections expressed an inverse relationship with task performance in visual memory and sustained attention domains in elderly, but not in young individuals. Conclusions: Our results confirm that the fractal nature of brain connectivity – as captured by MRCSA – is affected in healthy aging. Furthermore, FrC appears as a sensitive neurophysiological marker of age-related CD. Funding: I acknowledge funding from ÚNKP-22-3-1-SE-25, Ministry of Innovation and Technology; National Research, Development and Innovation Fund. I acknowledge further support from the SE250+ Scholarship (121389/DIDIT/2022), Semmelweis University, Budapest, Hungary.