Abstract
Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer’s disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions.
Highlights
Alzheimer’s disease (AD) is the most widespread neurodegenerative disorder and is a growing health problem
The second one was a feature importance analysis aimed at evaluating the effectiveness of communicability in identifying the brain regions whose connectivity is more related to AD
The entire procedure was repeated ten times, with different permutations of the training and test examples, in order to obtain a better generalization of the performance
Summary
Alzheimer’s disease (AD) is the most widespread neurodegenerative disorder and is a growing health problem It is mainly characterized by short-term memory loss in its earlier stages, followed by a progressive decline in other cognitive and behavioural functions as the disease advances [1]. A number of studies provided evidence that the decline due to AD is related to a disrupted connectivity among brain regions, caused by white matter (WM) degeneration, e.g., [2,3]. Due to their homogeneous chemical composition, conventional MRI is not able to highlight the structure of the.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.