Multi-Dimensional Fractal Vulnerability Study Of Alzheimer's Brain Networks

Alzheimer’s disease (AD) is associated with widespread disruption of functional brain networks. While graph-theoretic studies have characterized altered connectivity patterns in Alzheimer’s disease, most of the analyses rely on single-scale representations and fixed threshold choices, therefore obscuring critical structural transitions. In this study, the multi-scale fractal vulnerability of EEG-derived functional networks in Alzheimer’s disease is investigated using a density-controlled graph framework. Resting-state EEG recordings from AD subjects are converted into functional connectivity networks, systematically sparsified across a range of network densities, and analyzed using the box-counting fractal dimension (FD), restricted to the largest connected component (LCC). Fractal structure in AD networks degrades non-monotonically with decreasing density, and a transitional density regime (~15-25%) was identified within the analyzed Alzheimer’s disease cohort, corresponding to a regime of heightened structural vulnerability. These findings accentuate the importance of multi-scale analysis for understanding brain network organization.

@article{Ting2026,
  title     = {Multi-Dimensional Fractal Vulnerability Study Of Alzheimer's Brain Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Tin Tin Ting and Neha Hema Raj and Geetha N K and Thangaraj C and Olusegun D. Samuel},
  doi       = {10.14569/IJACSA.2026.0170661},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170661}
}