Gradients of connectivity as graph Fourier bases of brain activity.

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity "coupled to" an underlying network in graph Fourier modes).

Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging.

Graph signal processing (GSP) is a framework that enables the generalization of signal processing to multivariate signals described on graphs. In this paper, we present an approach based on Graph Fourier Transform (GFT) and machine learning for the analysis of resting-state functional magnetic resonance imaging (rs-fMRI). For each subject, we use rs-fMRI time series to compute several descriptive statistics in regions of interest (ROI).

A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative.

This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a circular Fourier filter. Then, a novel normalization method based on predictive modeling using multivariate linear regression (MLR) is applied to the data in order to reduce the variability between OA and healthy subjects.

Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support.

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making.

Automatic classification of segmented MRI data combining Independent Component Analysis and Support Vector Machines.

This paper proposes a novel method for automatic classification of magnetic resonance images (MRI) based on independent component analysis (ICA). Our methodology consists of three processing steps. First, all the MRI scans are normalized and segmented into gray matter, white matter and cerebrospinal fluid.

Linear intensity normalization of DaTSCAN images using Mean Square Error and a model-based clustering approach.

The analysis of 3D SPECT brain images requires several pre-processing steps such as intensity normalization and brain feature extraction. In this sense, a new method for intensity normalization of I-ioflupane-SPECT (DaTSCAN) brain images based on minimization of the Mean Square Error (MSE) between the Gaussian Mixture Model (GMM)-based extracted features from each subject image and a template in the so-defined non-specific region is derived. Our approach to feature extraction consists of using the set of parameters that define the template features, such as weights, covariance matrices and mean vectors to model the remaining images by reducing, consequently their dimensionality.