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Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight.
- Source :
-
Brain structure & function [Brain Struct Funct] 2020 Nov; Vol. 225 (8), pp. 2475-2493. Date of Electronic Publication: 2020 Sep 18. - Publication Year :
- 2020
-
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, which is necessary to obtain a clear understanding of their relative performance. 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 leverage the novel network feature extraction algorithms presented here. These techniques and toolbox can also be applied to other modalities due to their 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.
- Subjects :
- Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Child
Databases, Factual
Disease Progression
Female
Humans
Machine Learning
Magnetic Resonance Imaging
Male
Prognosis
Young Adult
Alzheimer Disease diagnostic imaging
Autistic Disorder diagnostic imaging
Brain diagnostic imaging
Brain Cortical Thickness
Cognitive Dysfunction diagnostic imaging
Subjects
Details
- Language :
- English
- ISSN :
- 1863-2661
- Volume :
- 225
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- Brain structure & function
- Publication Type :
- Academic Journal
- Accession number :
- 32945910
- Full Text :
- https://doi.org/10.1007/s00429-020-02136-0