Regional radiomics similarity networks (R2SN) in the human brain: reproducibility, small-world and biological basis

Abstract

BackgroundStructural covariance network (SCN) has been applied successfully to structural magnetic resonance imaging (MRI) study. However, most SCNs were constructed by the unitary marker, which was insensitive for the different disease phases. The aim of this study is to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis.MethodsRegional radiomics similarity network (R2SN) was constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject. We further assessed the small-world property of R2SN using the graph theory method, as well as the reproducibility in the different datasets and the reliability with test-retest analysis. The relationship between the R2SN and inter-regional co-expression of gene enriched was also explored, as well as the relationship with general intelligence.ResultsThe R2SN can be replicated in different datasets, also regardless of using different feature subsets. The R2SN showed high reliability with the test-retest analysis (ICC>0.7). Besides, the small-word property (σ>2) and the high correlation with the gene expression (R=0.24, P<0.001) and the general intelligence was found by R2SN.ConclusionR2SN provides a novel, reliable, and biologically plausible method to understand human morphological covariance based on structural MRI.Impact StatementImaging biomarkers are the cornerstone of modern radiology, and the development of valid biomarkers is crucial for optimizing individualized prediction in neurological disorders like AD. Thus, the development of the data mining method from neuroimaging is crucial for adding the biomarkers of disease. This study confirmed that R2SN provides a novel, robust and biologically plausible model and a new perspective for understanding the human brain, therefore. Thus, the R2SN has great promise in further study.

Standardizing workflows in imaging transcriptomics with the abagen toolbox

“…We introduce the abagen toolbox, an open-access software package designed to streamline processing and preparation of the AHBA for integration with neuroimaging data ( Markello et al, 2021c , available at https://github.com/rmarkello/abagen ; Markello, 2021b copy archived at swh:1:rev:2aeab5bd0f147fa76b488645e148a1c18095378d ). Supporting several workflows, abagen offers functionality for an array of analyses and has already been used in several peer-reviewed publications and preprints ( Shafiei et al, 2020 ; Hansen et al, 2021 ; Shafiei et al, 2021 ; Brown et al, 2021 ; Park et al, 2021 ; Valk et al, 2021 ; Zhao et al, 2020 ; Benkarim et al, 2020 ; Ding et al, 2021 ; Park et al, 2020 ; Lariviere et al, 2020 ; Martins et al, 2021 ). The primary workflow, used to generate regional gene expression matrices, integrates 17 distinct processing steps that have previously been employed by research groups throughout the published literature ( Table 1 ).…”

Standardizing workflows in imaging transcriptomics with the abagen toolbox

“…We introduce the abagen toolbox, an open-access software package designed to streamline processing and preparation of the AHBA for integration with neuroimaging data , available at https: //github.com/rmarkello/abagen). Supporting several workflows, abagen offers functionality for an array of analyses and has already been used in several peerreviewed publications and preprints (Benkarim et al 2020, Ding et al 2021, Hansen et al 2021, Lariviere et al 2020, Martins et al 2021, 2020, 2020, Valk et al 2021, Zhao et al 2020. The primary workflow, used to generate regional gene expression matrices, integrates 17 distinct processing steps that have previously been employed by research groups throughout the published literature (Table 1).…”

“…Creation of the toolbox has followed best-practices in software development, including version control, continuous integration testing, and modular code design. abagen has already been successfully used in many peer-reviewed publications (Benkarim et al 2020, Ding et al 2021, Hansen et al 2021, Lariviere et al 2020, Martins et al 2021, 2020, 2020, Valk et al 2021, Zhao et al 2020), and we continue to integrate new features as community needs emerge. To encourage further use by new research groups we provide comprehensive documentation on installing and working with the abagen toolbox online (https://abagen.readthedocs.io/).…”