Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight

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 cl...

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Vydáno v:Brain structure & function Ročník 225; číslo 8; s. 2475 - 2493
Hlavní autoři: Raamana, Pradeep Reddy, Strother, Stephen C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
Témata:
Biomedical and Life Sciences
Biomedicine
Cell Biology
Neurology
Neurosciences
Original Article
Histogram distance
Cortical thickness
Magnetic resonance imaging
Mild cognitive impairment
Graph theory
Model comparison
Early prognosis
Alzheimer
ISSN:1863-2653, 1863-2661, 1863-2661
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Shrnutí: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 .
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ISSN:1863-2653
1863-2661
1863-2661
DOI:10.1007/s00429-020-02136-0