Voxel-based morphometry comparison between first episodes of psychosis with and without evolution to schizophrenia

Molina, Vicente; Sanz, Javier; Villa, Rocío; Pérez, Javier; González, David Andrés; Sarramea, Fernando; Ballesteros, Alejandro; Galindo, Gemma; Hernandez, Jessica

doi:10.1016/j.pscychresns.2009.09.003

Cited by 12 publications

(4 citation statements)

References 58 publications

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“…In our study, insula volume was found decreased bilaterally in patients with schizophrenia compared with normal controls, consistent with other reports (2,17,18). Decreased insula volumes were reported even in first episode schizophrenia cases (19)(20)(21).…”

Section: Discussionsupporting

confidence: 93%

A combined VBM and DTI study of schizophrenia: bilateral decreased insula volume and cerebral white matter disintegrity corresponding to subinsular white matter projections unlinked to clinical symptomatology

Onay¹,

Eser²,

Ulasoglu-Yildiz³

et al. 2017

Diagn Interv Radiol

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Section: Discussionsupporting

confidence: 93%

A combined VBM and DTI study of schizophrenia: bilateral decreased insula volume and cerebral white matter disintegrity corresponding to subinsular white matter projections unlinked to clinical symptomatology

Onay¹,

Eser²,

Ulasoglu-Yildiz³

et al. 2017

Diagn Interv Radiol

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“…The selected studies are listed in table 1. [12], [16], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48].…”

Section: Methodsmentioning

confidence: 99%

Autistic Disorders and Schizophrenia: Related or Remote? An Anatomical Likelihood Estimation

et al. 2010

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Shared genetic and environmental risk factors have been identified for autistic spectrum disorders (ASD) and schizophrenia. Social interaction, communication, emotion processing, sensorimotor gating and executive function are disrupted in both, stimulating debate about whether these are related conditions. Brain imaging studies constitute an informative and expanding resource to determine whether brain structural phenotype of these disorders is distinct or overlapping. We aimed to synthesize existing datasets characterizing ASD and schizophrenia within a common framework, to quantify their structural similarities. In a novel modification of Anatomical Likelihood Estimation (ALE), 313 foci were extracted from 25 voxel-based studies comprising 660 participants (308 ASD, 352 first-episode schizophrenia) and 801 controls. The results revealed that, compared to controls, lower grey matter volumes within limbic-striato-thalamic circuitry were common to ASD and schizophrenia. Unique features of each disorder included lower grey matter volume in amygdala, caudate, frontal and medial gyrus for schizophrenia and putamen for autism. Thus, in terms of brain volumetrics, ASD and schizophrenia have a clear degree of overlap that may reflect shared etiological mechanisms. However, the distinctive neuroanatomy also mapped in each condition raises the question about how this is arrived in the context of common etiological pressures.

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“…While many did not find significant differences between poor and good outcome patients (Molina et al, 2010, van Haren et al, 2003), others reported contradictory findings. A 1-year follow-up study found that first-episode schizophrenia (FE-SZ) patients with clinical deterioration had a smaller area of internal capsule compared to those with stable psychopathology, but no differences in either its volume or in any of the other 32 regions of interest (ROI) studied (Wobrock et al, 2009).…”

Section: Introductionmentioning

confidence: 95%

Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients

et al. 2017

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Structural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners.This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course.The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n = 67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n = 97); University of São Paulo (Brazil) (n = 64); University of Cantabria, Santander (Spain) (n = 107); and University of Melbourne (Australia) (n = 54). All images were acquired on 1.5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7 years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either “continuous” (n = 94) or “remitting” (n = 118).Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p < 0.001). In the single- or multi-center models, illness course could not be predicted with significant accuracy. However, when reducing heterogeneity by restricting the analyses to male patients only, classification accuracy improved in some samples.This study provides proof of concept that combining multi-center MRI data to create a well performing classification model is possible. However, to create complex multi-center models that perform accurately, each center should contribute a sample either large or homogeneous enough to first allow accurate classification within the single-center.

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Voxel-based morphometry comparison between first episodes of psychosis with and without evolution to schizophrenia

Cited by 12 publications

References 58 publications

A combined VBM and DTI study of schizophrenia: bilateral decreased insula volume and cerebral white matter disintegrity corresponding to subinsular white matter projections unlinked to clinical symptomatology

A combined VBM and DTI study of schizophrenia: bilateral decreased insula volume and cerebral white matter disintegrity corresponding to subinsular white matter projections unlinked to clinical symptomatology

Autistic Disorders and Schizophrenia: Related or Remote? An Anatomical Likelihood Estimation

Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients

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