Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction

03/06/2017
by   Mathilde Ménoret, et al.
0

Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/24/2020

Torus Probabilistic Principal Component Analysis

One of the most common problems that any technique encounters is the hig...
research
01/29/2018

Nonlinear Dimensionality Reduction on Graphs

In this era of data deluge, many signal processing and machine learning ...
research
10/27/2014

Estimating the intrinsic dimension in fMRI space via dataset fractal analysis - Counting the `cpu cores' of the human brain

Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive ...
research
03/09/2022

Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding

Graph Signal Processing is a promising framework to manipulate brain sig...
research
06/05/2021

Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments

Multi-regional interaction among neuronal populations underlies the brai...
research
05/01/2020

Simultaneous Non-Gaussian Component Analysis (SING) for Data Integration in Neuroimaging

As advances in technology allow the acquisition of complementary informa...
research
06/06/2013

Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

Functional magnetic resonance imaging (fMRI) produces data about activit...

Please sign up or login with your details

Forgot password? Click here to reset