# Brain structural connectivity atrophy in Alzheimer's disease

arXiv: https://arxiv.org/abs/1709.02369 · HTML: https://mlsanity.com/paper/1709.02369/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Functional2Structural: Cross-Modality Brain Networks Representation Learning](https://mlsanity.com/paper/2205.07854/) (2022)

## Abstract (arXiv)

Analysis and quantification of brain structural changes, using Magnetic resonance imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Network-based models of the brain have shown that both local and global topological properties can reveal patterns of disease propagation. On the other hand, intra-subject descriptions cannot exploit the whole information context, accessible through inter-subject comparisons. To address this, we developed a novel approach, which models brain structural connectivity atrophy with a multiplex network and summarizes it within a classification score. On an independent dataset multiplex networks were able to correctly segregate, from normal controls (NC), AD patients and subjects with mild cognitive impairment that will convert to AD (cMCI) with an accuracy of, respectively, 0.86 ± 0.01 and 0.84 ± 0.01. The model also shows that illness effects are maximally detected by parceling the brain in equal volumes of 3000 mm3 ("patches"), without any a priori segmentation based on anatomical features. A direct comparison to standard voxel-based morphometry on the same dataset showed that the multiplex network approach had higher sensitivity. This method is general and can have twofold potential applications: providing a reliable tool for clinical trials and a disease signature of neurodegenerative pathologies.