Disease state prediction from resting state functional connectivity

By R. Craddock, Paul E. Holtzheimer, Xiaoping P. Hu, Helen S. Mayberg. 2009

427 citations recorded by the source corpus.

The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real‐time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional connectivity patterns that distinguish patients with depression from healthy volunteers. In addition, two feature selection algorithms were implemented (one filter method, one wrapper method) that incorporate reliability information into the feature selection process. These reliability feature selections methods were compared to two previously proposed feature selection methods. A support vector classifier was trained that reliably distinguishes healthy volunteers from clinically depressed patients. The reliability feature selection methods outperformed previously utilized methods. The proposed framework for applying support vector classification to functional connectivity data is applicable to other disease states beyond major depression. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “…have been widely used in the neuroimaging community to generate diagnostic scores on the basis of brain images Mouro-Miranda et al. (2005) Wang et al. (2007) Cuingnet et al. (2011) Craddock et al. (2009) Fan et al. (2007) Batmanghelich et al. (2012) Vemuri et al. (2008) Davatzikos et al. (2011).”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, positive): “…the most successful of such methods are SVMbased tools (Fan et al., 2007; Klöppel et al., 2008), which have been quite widely used in functional (Craddock et al., 2009; Davatzikos et al., 2005; De Martino et al., 2008; Mouro-Miranda et al., 2005; Wang et al., 2007) and structural (Cuingnet et…”
Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS) (2012, background): “…al., 2010a), neglect (Carter et al., 2010; He et al., 2007), and hemiparesis (Carter et al., 2010), and can differentiate normal controls from patients with Alzheimer's disease (Greicius et al., 2004; Li et al., 2002; Supekar et al., 2008; Wang et al., 2006) or depression (Craddock et al., 2009).”
Decoding brain states from fMRI connectivity graphs (2011, methodology): “A recent method related to our work proposed to use resting-state correlations between regions of interest as features for an SVM classifier (Craddock et al., 2009).”
Whole Brain Resting-State Analysis Reveals Decreased Functional Connectivity in Major Depression (2010, positive): “A last study did not show any FC differences between major depressive disorder (MDD) patients and controls using conventional statistics (Craddock et al., 2009).”
The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis (2012, background): “…they contain suf-ﬁcient information to capture individual characteristics such as age (Dosenbach et al., 2010), gender (Yan et al., 2011), intelligence quotient (IQ) (Li et al., 2009; van den Heuvel et al., 2009b), and disease state (Supekar et al., 2008; Craddock et al., 2009; Lo et al., 2010).”
The future of FMRI connectivity (2012, methodology): “…parcellation through high-dimensional ICA, or when using partial correlation or SEM applied to large numbers of nodes 33 Note that the study of changes in cognitive state has been an active and successful application of methods such as SEM for many years. univariate tests (Craddock et al., 2009).”
Functional and Effective Connectivity: A Review (2011, neutral): “We will return to this below.”
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).”
Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016, methodology): “These entail specifying regions of interest, extracting regional time courses, computing connectivity between regions, and identifying connections that relate to subject’s phenotypes [15, 30, 31, 32].”
State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016, neutral): “…Wang et al., 2007; Zhang et al., 2012; Chase, 2014), autism spectrum disorder (Monk et al., 2009; Khan et al., 2013), schizophrenia (Liang et al., 2006; Zhou et al., 2007; Garrity et al., 2007; Lynall et al., 2010), and depression (Anand et al., 2005; Greicius et al., 2007; Craddock et al., 2009).”
Predicting brain-age from multimodal imaging data captures cognitive impairment (2016, neutral): “…functional connectivity based on resting-state fMRI data (Craddock et al., 2013) also has the potential to provide clinically-relevant biomarkers (Craddock et al., 2009; Castellanos et al., 2013), as the data is easily acquired in a clinical setting (Greicius, 2008; Damoiseaux et al., 2012).”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, positive): “The weights of the corresponding brain functional connectome are used to characterize individual subjects behavior, cognition, and mental health (Craddock et al., 2009; Richiardi et al., 2010; Milazzo et al., 2014; Smith et al., 2015; Miller et al., 2016; Colclough et al., 2017; Dubois et al.,…”
A whole brain fMRI atlas generated via spatially constrained spectral clustering (2011, methodology): “This requires hypothesizing the shape, size, and location of functional areas based upon neuroanatomic literature or a meta-analysis of functional neuroimaging results [Craddock et al., 2009; Dosenbach et al., 2010].”