Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI

By Hui Shen, Lubin Wang, Yadong Liu, Dewen Hu. NeuroImage, 2010

367 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e83c2cf5b7320f596a8de44d6812eb76cc65362a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI (2018, methodology): “Shen et al. (2010) used whole-brain functional connectivity MRI (fcMRI) pattern analysis to discriminate schizophrenic patients from healthy controls, demonstrating the potential of whole-brain fcMRI in the diagnosis of schizophrenia.”
Cognitive Network Neuroscience (2015, methodology): “…cohorts in an effort to develop neurodiagnostics (Fekete et al., 2013; Bassett, Nelson, et al., 2012; Richiardi et al., 2012; Chen et al., 2011; Shen et al., 2010; Supekar et al., 2008) by exploiting robust techniques from machine learning (Figure 5; see Richiardi, Achard, Bunke, & Van De…”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, neutral): “Correlation-based feature selection (CFS) is a simple algorithm which ranks features based on a hypothesis that good feature subsets contain features highly correlated with the classification (Hall, 1999; Shen et al., 2010; Tang et al., 2012; Su et al., 2013; Challis et al., 2015).”
Clinical applications of the functional connectome (2013, neutral): “As summarized in Table 1, the literature on predictive modeling on functional connectivity (as of 2/1/2013) addresses schizophrenia (n = 9 studies, Bassett et al., 2012; Du et al., 2012; Fan et al., 2011; Liu et al., 2012; Shen et al., 2010; Tang et al., 2012; Venkataraman et al., 2010, 2012; Yu et ”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, methodology): “For example, previous studies showed the use of functional connectivitybased features for classification of schizophrenia and bipolar patients at the individual level (Arbabshirani et al., 2013b; Shen et al., 2010; Su et al., 2013).”
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, positive): “Machine-learning algorithms have been successfully deployed in the automated classification of altered FC patterns related to SZ (Arbabshirani et al., 2013; Du et al., 2012; Shen et al., 2010; Tang et al., 2012; Watanabe et al., 2014).”
Resting state fMRI: A review of methods and clinical applications (2012, positive): “fMRI has been applied to distinguish patients with psychiatric diseases from controls, including major depressive disorder(75) and schizophrenia.(76,77) RS-fMRI has also been used to identify patients with autism(78) and attention deficit/hyperactivity disorder.”
Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. (2012, methodology): “…increasing interest in multivariate pattern analysis methods to categorize psychiatric patients from healthy controls using struc- tural or functional brain images (Klöppel et al., 2008; Craddock et al., 2009; Desikan et al., 2009; Shen et al., 2010; Zhou et al., 2010a; Ardekani et al., 2011).”