Abstract
Network-level analysis based on anatomical, pairwise similarities (e.g., cortical thickness) has been gaining increasing attention recently. However, there has not been a systematic study of the impact of spatial scale and edge definitions on predictive performance. In order to obtain a clear understanding of relative performance, there is a need for systematic comparison. In this study, we present a histogram-based approach to construct subject-wise weighted networks that enable a principled comparison across different methods of network analysis. We design several weighted networks based on three large publicly available datasets and perform a robust evaluation of their predictive power under four levels of separability. An interesting insight generated is that changes in nodal size (spatial scale) have no significant impact on predictive power among the three classification experiments and two disease cohorts studied, i.e., mild cognitive impairment and Alzheimer’s disease from ADNI, and Autism from the ABIDE dataset. We also release an open source python package called graynet to enable others to implement the novel network feature extraction algorithm, which is applicable to other modalities as well (due to its domain- and feature-agnostic nature) in diverse applications of connectivity research. In addition, the findings from the ADNI dataset are replicated in the AIBL dataset using an open source machine learning tool called neuropredict.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵* Data used in the preparation of this article was obtained from: 1) Alzheimer Disease Neuroimaging Initiative (ADNI) and 2) the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (www.loni.usc.edu/ADNI). The ADNI and AIBL researchers contributed data but did not participate in analysis or writing of this report.
This version has been revised extensively based on latest round of reviews from Brain Structure and Function. We have replicate the study on a 3rd dataset AIBL (another Alzheimer's dataset like ADNI), and show that our results replicate in that as well. This replication is achieved entirely with 100% open source tools: visualqc, graynet and neuropredict, further increasing the reuse and reproducibility of this study. We believe this is a big step forward for research in the study of single-subject morphological networks. Aug 2020: Introduction has been revised to communicate the focus of the paper more directly.
