Abstract
We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations.
Highlights
Over the past years, functional magnetic resonance imaging has been widely used for the identification of brain regions that are related to both functional segregation and integration
We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel Principal Component Analysis (PCA)
We assess the performance of two metrics that are widely used for the construction of FCN from functional magnetic resonance imaging (fMRI), namely the Euclidean distance and the cross correlation metric
Summary
Functional magnetic resonance imaging (fMRI) has been widely used for the identification of brain regions that are related to both functional segregation and integration. The conventional analysis relies on the identification of the activated voxels based on functional response models and multivariate statistics between experimental conditions (e.g. resting-state vs task-stimulated activity). We focus on the construction of functional connectivity networks (FCN) based on resting-state fMRI (rsfMRI) recordings. In rsfMRI, there is no stimuli and the assessment of functional integration is more complex and not so straightforward compared to task-related experiments (Khosla et al 2019). In the SBA (Cole et al 2010), the (averaged) fMRI signals of the regions of interest (ROIs) are correlated with each other; correlations above a threshold are considered functional connections between seeds/ROIs. Even though the SBA has been proved extremely useful in identifying functional networks of specific brain regions (Greicius et al 2003; Cognitive Neurodynamics (2021) 15:585–608
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