Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection

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.

Communicability distance reveals hidden patterns of Alzheimer’s disease

“…Finally, a robust structural connectome construction was performed for generating the connectivity matrices (R. E. Smith, Tournier, Calamante, & Connelly, 2015a). The pipeline here described has been used in recent structural connectivity studies, for example, see Amico and Goñi (2018); Lella, Amoroso, Diacono, et al (2019); Lella et al (2020); Tipnis, Amico, Ventresca, and Goni (2018). The output was a weighted connectivity matrix for each subject.…”

Communicability distance reveals hidden patterns of Alzheimer disease

“…Finally, a robust structural connectome construction was performed for generating the connectivity matrices [62]. The pipeline here described has been used in recent structural connectivity studies, for example [63,64,65,66]. The output was a weighted connectivity matrix for each subject.…”

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

“…To investigate the performance of an ML algorithm on the classification of patients in different classes such as AD vs or non-MCI, AD vs MCI, and MCI vs non-MCI, a dCDT method was investigated in [56], where data was collected via a memory assessment program. In [57], on the basis of communicability at the whole brain level, an ML framework was developed for the classification of AD by using DWI data. The detection performance of AD from NC was investigated by applying three state-of-the-art ML classifiers such as SVM, RF, and ANN.…”