Abstract
Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation‐based functional network and group‐level comparisons. We introduce a “Brain Network Construction and Classification (BrainNetClass)” toolbox to promote more advanced brain network construction methods to the filed, including some state‐of‐the‐art methods that were recently developed to capture complex and high‐order interactions among brain regions. The toolbox also integrates a well‐accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. BrainNetClass is a MATLAB‐based, open‐source, cross‐platform toolbox with both graphical user‐friendly interfaces and a command line mode targeting cognitive neuroscientists and clinicians for promoting reliability, reproducibility, and interpretability of connectome‐based, computer‐aided diagnosis. It generates abundant classification‐related results from network presentations to contributing features that have been largely ignored by most studies to grant users the ability of evaluating the disease diagnostic model and its robustness and generalizability. We demonstrate the effectiveness of the toolbox on real resting‐state functional MRI datasets. BrainNetClass (v1.0) is available at https://github.com/zzstefan/BrainNetClass.
Highlights
Functional connectivity (FC) based on resting-state functional MRI (RS-fMRI) is one of the major methods for brain functional studies
While many studies focused on the group-level differences in brain functional network between patients and healthy controls based on statistical inference, an emerging trend is to utilize machine learning techniques to learn diagnostic features from the brain networks to conduct indi
The mostly and second mostly selected parameters were only selected for 16 and 17 times (Ì34% and 36% across all the LOOCV runs), indicating that the model robustness should be further investigated and the result in Fig. 5(d) and Table 4 should be carefully interpreted because the performance and the identified contributing features from SGR were not derived from the model with the same parameters
Summary
Functional connectivity (FC) based on resting-state functional MRI (RS-fMRI) is one of the major methods for brain functional studies. It describes the functional interactions among anatomically separated brain regions, often interpreted as information exchange and remote communication, or functional integration (Allen et al, 2014; Hutchison et al, 2013; Leonardi et al, 2013; Van Dijk et al, 2009; Thomas Yeo et al, 2011). We have witnessed a broad application of brain FC network-based disease studies (Badhwar et al, 2017; Fornito et al, 2015). While many studies focused on the group-level differences in brain functional network between patients and healthy controls based on statistical inference, an emerging trend is to utilize machine learning techniques to learn diagnostic features from the brain networks to conduct indi-
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