P002 Understanding aging in terms of memory: Beyond excitation-inhibition balance
Srishty Aggarwal*1
1Department of Physics, Indian Institute of Science, Bangalore, India, 560012
*Email: srishtya@iisc.ac.in
Introduction
Recently, non-linear dynamic techniques like Higuchi’s fractal dimension (HFD) have gained prominence to understand neural complexity.We previously demonstrated that HFD increased with aging and was inversely dependent on changes in power and slope of power spectral density (PSD)[1]. However, findings regarding changes in HFD with aging are inconsistent in literature[1],[2], leading to their ambiguous interpretability in neural mechanisms. Moreover, while age-related reduction in PSD slope and power in the gamma band (30-70 Hz) showed a shift towards lesser inhibition with aging[3], the reason for the slowing down of center frequency of gamma with aging is not clear. These emphasize on the need for a theoretical model that extends beyond excitation-inhibition (E-I) balance to explain HFD and aging.
Methods
We propose a two-parameter model based on stochastic fractional differentiation, that exhibits power-law scaling and long-range dependencies, the important characteristics of neurophysiological signals. In this model, one parameter governs E-I balance, while the other, ‘the order of differentiation’, captures the influence of past states. The decrease in order of differentiation indicates an increased weightage to the past memory states, which could be the effect of change in long-term plasticity.
Results
The model shows that the order of differentiation is inversely related to HFD. Thus, the previously observed increase in HFD with aging is due to greater memory accumulation over time in elderly population. Further, itdepicts that the memory accumulation, not just the change in E-I balance, is the primary reason for the age-related reduction in stimulus-induced gamma power, decrease in gamma center frequency[3], and flattening of spectral slopes at low frequency[4].Our model successfully accounts for the observed changes in HFD across different stimulus conditions, including transients and sustained oscillations. It also reproduces the observed dependence of HFD on both peak power and spectral slope. Additionally, it offers a unified framework that simultaneously captures changes in oscillatory peaks and slopes showing its advancement over previous models that typically address only one of these aspects.
Discussion
The presentmodel highlights the presence of two components of neural activity: memory and E-I balance. By demonstrating that these components contributedifferentlyto brain dynamics, our findings provide a new perspective on how neural complexity evolves with aging and stimulus-driven processes.The model’s simplicity in terms of its parameter space and ability to explain a wide range of empirical findings makes it a promising framework for unravelling the intricate mechanisms of brain function.
Acknowledgements
I would like to thank my advisors Prof. Banibrata Mukhopadhyay, Department of Physics and Prof. Supratim Ray, Centre for Neuroscience for useful discussions and comments for the present work.
References
[1] S. Aggarwal and S. Ray, Jun. 16, 2024,bioRxiv. doi: 10.1101/2024.06.15.599168.
[2] F. M. Smits, C. Porcaro, C. Cottone, A. Cancelli, P. M. Rossini, and F. Tecchio,PLOS ONE, vol. 11, no. 2, p. e0149587, Feb. 2016, doi: 10.1371/journal.pone.0149587.
[3] D. V. P. S. Murtyet al.,NeuroImage, vol. 215, p. 116826, Jul. 2020, doi: 10.1016/j.neuroimage.2020.116826.
[4] S. Aggarwal and S. Ray,Cerebral Cortex Communications, vol. 4, no. 2, p. tgad011, May 2023, doi: 10.1093/texcom/tgad011.
