Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls

Kawasaki, Yasuhiro; Suzuki, Michio; Kherif, Ferath; Takahashi, Tsutomu; Zhou, Shiyu; Nakamura, Kazue; Matsui, Mié; Sumiyoshi, Tomiki; Seto, Hikaru; Kurachi, Masayoshi

doi:10.1016/j.neuroimage.2006.08.018

Cited by 165 publications

(109 citation statements)

References 47 publications

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“…These discriminating patterns are generated by means of input features; in structural MRI most common features are so-called brain tissue densities (obtained from voxel based morphometry). Frequently used methods to create these classification models are: support vectors machine (SVM) Fan et al, 2005Fan et al, , 2007Fan et al, , 2008Ingalhalikar et al, 2010;Koutsouleris et al, 2009;Pohl and Sabuncu, 2009); Discriminant Function Analysis (Karageorgiou et al, 2011;Kasparek et al, 2011;Leonard et al, 1999;Liu et al, 2004;Nakamura et al, 2004;Takayanagi et al, 2010Takayanagi et al, , 2011; and some other methods (Caprihan et al, 2008;Kawasaki et al, 2007;Sun et al, 2009). Although considerable accuracies have been achieved ranging from 70.5% to 91.8%, these were often obtained from relatively small data sets and without testing the model in validation samples.…”

Section: Introductionmentioning

confidence: 99%

Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples

et al. 2012

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Section: Introductionmentioning

confidence: 99%

Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples

et al. 2012

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“…However, VBM analyses are of limited value for individual diagnosis, since they measure group differences and are not equipped to classify individuals. Towards this goal, high-dimensional pattern classification methods have been pursued in recent years (Davatzikos, 2004;Duchesne et al, 2006;Fan et al, 2006a;Fan et al, 2007a;Fan et al, 2005Fan et al, , 2006bFan et al, 2007b;Lao et al, 2004;Liu et al, 2004;Timoner et al, 2002), and have shown great potential in a variety of neuroimaging studies (Davatzikos et al, in press, 2006;Davatzikos et al, 2005a;Davatzikos et al, 2005b;Fan et al, 2008a;Fan et al, 2005Fan et al, , 2006bFan et al, 2007b;Kawasaki et al, 2007;Mourao-Miranda et al, 2005;Yoon et al, 2007). Unlike VBM-type methods, which are mass univariate and don't consider statistical associations among different brain regions, high-dimensional pattern classification methods are multivariate, thereby leading to better group separation, which is critical for individual diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

et al. 2008

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This work builds upon previous studies that reported high sensitivity and specificity in classifying individuals with mild cognitive impairment (MCI), which is often a prodromal phase of Alzheimer's Disease (AD), via pattern classification of MRI scans. The current study integrates MRI and PET 15 O water scans from 30 participants in the Baltimore Longitudinal Study of Aging, and tests the hypothesis that joint evaluation of structure and function can yield higher classification accuracy than either alone. Classification rates of up to 100% accuracy were achieved via leave-one-out cross validation, whereas conservative estimates of generalization performance in new scans, evaluated via bagging cross-validation, yielded an area under the receiver operating characteristic (ROC) curve equal to 0.978 (97.8%), indicating excellent diagnostic accuracy. Spatial maps of regions determined to contribute the most to the classification implicated many temporal, prefrontal, orbitofrontal, and parietal regions. Detecting complex patterns of brain abnormality in early stages of cognitive impairment has pivotal importance for the detection and management of AD. KeywordsAlzheimer's Disease; MCI; high-dimensional Pattern Classification; MRI; PET; voxel-based analysis; diagnosis of AD Amnestic mild cognitive impairment (MCI) is often a prodromal stage to Alzheimer's Disease (AD), as individuals with MCI may convert to AD at an annual rate as high as 15% . Therefore, MCI is frequently considered to be a good target for early AD diagnosis, and for therapeutic interventions. MRI and PET imaging can potentially be used as diagnostic tools that assess brain structure and function in a direct and objective way and have potential utility for diagnosis, prognosis, and evaluation of disease progression and treatment effects.Many imaging studies have found loss of grey matter (GM) and metabolic abnormalities in MCI. Most of the earlier studies were based on volumetric measurements of regions of interest (ROI's) (Chetelat et al., 2002;Convit et al., 2000;Dickerson et al., 2001;Fox et al., 1996a;Kaye et al., 1997;Killiany et al., 2000), such as the hippocampus and the entorhinal cortex, and have confirmed GM atrophy in regions that are known to be affected by AD. However, the pattern of AD pathology is complex and evolves as the disease progresses, starting mainly in the hippocampus and entorhinal cortex, and subsequently spreading throughout temporal and orbitofrontal cortex, posterior cingulate, and association cortex generally (Braak et al., 1998). Therefore, measuring volumes or average PET signals of a few structures cannot capture Voxel-based morphometry (VBM) has been proposed by a number of investigators as a more comprehensive way of measuring the spatial distribution of brain atrophy in MCI and AD, by evaluating images region by region instead of making a priori assumptions about specific ROIs. A variety of VBM-type methods exist (Ashburner and Friston, 2000;Chung et al., 2001;Davatzikos et al., 2001;Davatzikos et al., 1996...

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“…Several multivariate morphological studies (based on different techniques of feature extraction and pattern classification, used primarily to classify the groups of subjects based on morphological patterns) were already performed with subjects suffering from schizophrenia [Fan et al, 2007;Fan et al, 2008;Kawasaki et al, 2007]. Recently, the adoption of a powerful multivariate technique-Independent Component Analysis-for morphological data was published-the authors called the technique ''Source-based Morphometry'' (SBM).…”

Section: Introductionmentioning

confidence: 99%

Source‐based morphometry of gray matter volume in men with first‐episode schizophrenia

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et al. 2009

Human Brain Mapping

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Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls

Cited by 165 publications

References 47 publications

Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples

Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

Source‐based morphometry of gray matter volume in men with first‐episode schizophrenia

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