Source-Based Morphometry: The Use of Independent Component Analysis to Identify Gray Matter Differences With Application to Schizophrenia

By Lai Xu, Karyn M. Groth, Godfrey D. Pearlson, David J. Schretlen, Vince D. Calhoun. 2008

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We present a multivariate alternative to the voxel‐based morphometry (VBM) approach called source‐based morphometry (SBM), to study gray matter differences between patients and healthy controls. The SBM approach begins with the same preprocessing procedures as VBM. Next, independent component analysis is used to identify naturally grouping, maximally independent sources. Finally, statistical analyses are used to determine the significant sources and their relationship to other variables. The identified “source networks,” groups of spatially distinct regions with common covariation among subjects, provide information about localization of gray matter changes and their variation among individuals. In this study, we first compared VBM and SBM via a simulation and then applied both methods to real data obtained from 120 chronic schizophrenia patients and 120 healthy controls. SBM identified five gray matter sources as significantly associated with schizophrenia. These included sources in the bilateral temporal lobes, thalamus, basal ganglia, parietal lobe, and frontotemporal regions. None of these showed an effect of sex. Two sources in the bilateral temporal and parietal lobes showed age‐related reductions. The most significant source of schizophrenia‐related gray matter changes identified by SBM occurred in the bilateral temporal lobe, while the most significant change found by VBM occurred in the thalamus. The SBM approach found changes not identified by VBM in basal ganglia, parietal, and occipital lobe. These findings show that SBM is a multivariate alternative to VBM, with wide applicability to studying changes in brain structure. Hum Brain Mapp, 2009. © 2008 Wiley‐Liss, Inc.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Signal Processing

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In Cognitive Neuroscience

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Christos Davatzikos

Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “Machine learning based methods Davatzikos (2004) Cuingnet et al. (2011) De Martino et al. (2008) Fan et al. (2007) Gaonkar and Davatzikos (2012) Xu et al. (2009) Van De Ville et al. (2007) Van De Ville et al. (2004) Mingoia et al. (2012) can offer diagnostics based on neuroimaging data.”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, neutral): “…Fan et al., 2007; Klöppel et al., 2008; Koutsouleris et al., 2009; Langs et al., 2011; Mingoia et al., 2012; Mouro-Miranda et al., 2005; Pereira et al., 1998; Richiardi et al., 2011; Sabuncu and Van Leemput, 2011; Vemuri et al., 2008; Venkataraman et al., 2012; Wang et al., 2007; Xu et al., 2009).”