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.
journal_name
Brain Struct Functjournal_title
Brain structure & functionauthors
Raamana PR,Strother SC,Australian Imaging Biomarkers, Lifestyle flagship study of ageing, for The Alzheimer’s Disease Neuroimaging Initiative.doi
10.1007/s00429-020-02136-0subject
Has Abstractpub_date
2020-11-01 00:00:00pages
2475-2493issue
8eissn
1863-2653issn
1863-2661pii
10.1007/s00429-020-02136-0journal_volume
225pub_type
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