Brain Network Construction and Classification Toolbox (BrainNetClass) (1906.09908v1)

Abstract: Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) can be downloaded from https://github.com/zzstefan/BrainNetClass.

• The paper introduces BrainNetClass, a MATLAB-based toolbox that automates brain network construction and individualized RS-fMRI classification.
• It integrates advanced functional connectivity methods like tHOFC, aHOFC, dHOFC, and sparse representations to capture non-linear brain interactions.
• The toolbox demonstrates enhanced classification performance in diagnosing MCI and distinguishing EO/EC states, offering reproducible neuroimaging biomarkers.

Analysis of Brain Network Construction and Classification Toolbox (BrainNetClass)

The paper, "Brain Network Construction and Classification Toolbox (BrainNetClass)", introduces BrainNetClass, a MATLAB-based open-source toolbox designed to enhance brain network construction methodologies and promote network-based individualized classification using resting-state functional MRI (RS-fMRI) data. BrainNetClass incorporates contemporary brain network algorithms to advance cognitive and clinical neuroscientific research. This reliable toolbox serves both expert and non-expert users in neuroimaging and machine learning domains by automating complex computational processes involved in functional connectomics studies.

Functional Connectivity Approaches

Traditional approaches employing Pearson’s correlation for functional connectivity (FC) offer simplicity but are limited to static and pairwise analysis of brain interactions. BrainNetClass overcomes these limitations by integrating high-order functional connectivity (HOFC) methods such as topographical profile similarity-based HOFC (tHOFC), associated HOFC (aHOFC), and dynamic HOFC (dHOFC). These methods capture complex and non-linear interactions among brain regions, effectively modeling them as multi-node networks.

Furthermore, sparse representation (SR) methods are introduced to produce biologically meaningful brain networks by minimizing false connections typically encountered in fully-connected networks. Innovations in SR, such as strength-weighted SR (WSR) and strength-weighted sparse group representation (WSGR), emphasize the retention of robust and biologically plausible FC links, optimizing parameter selection using nested cross-validation frameworks.

Classification and Toolbox Implementation

BrainNetClass facilitates robust machine learning classifications, employing support vector machines (SVM) to predict brain disorders. The toolbox automates feature extraction, selection, and classifier training using FC coefficients and local clustering properties, catering to the nuanced challenges of neuroimaging classification tasks. A notable inclusion is the rigorous cross-validation protocols—LOOCV and 10-fold—to ensure statistical robustness and generalizability of predictive models.

Experimental Validation

The toolbox's efficacy is demonstrated through two applications—diagnosing mild cognitive impairment (MCI) and distinguishing between eyes-open (EO) and eyes-closed (EC) states. In both cases, advanced network construction methods like SSGSR and SGR showcased enhanced classification accuracy compared to traditional PC-based methods. The toolbox’s outputs enable visualization of network features predictive of disease, aiding in the identification of potential biomarkers for clinical use.

Implications and Future Developments

BrainNetClass comprehensively addresses procedural and analytical gaps in existing methodologies, stipulating clear protocols for parameter sensitivity and model robustness testing, which enhance reproducibility and reliability of neuroimaging analyses. Future iterations could expand functionality to multi-class scenarios, introduce additional machine learning models, and explore a broader range of network properties to further enrich the characterization of brain connectivity patterns.

By standardizing sophisticated modeling techniques within a user-friendly interface, BrainNetClass stands poised to transform clinical neuroscientific research, fostering reproducible and interpretable connectomic insights. This toolbox represents a significant stride toward realizing the potential of RS-fMRI in personalized diagnosis and treatment planning, laying a foundational framework for future technological innovations in the domain.