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

Kašpárek, Tomáš; Mareček, Radek; Schwarz, Daniel; Přikryl, Radovan; Vaníček, Jiří; Mikl, Michal; Češková, Eva

doi:10.1002/hbm.20865

Cited by 46 publications

(46 citation statements)

References 84 publications

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“…A toolbox for VBM (http://dbm.neuro.uni-jena.de/vbm8/) running within the Statistical Parametric Mapping software package version 8 (SPM8; http://www.fil.ion.ucl.ac.uk/spm) was used (Ashburner and Friston, 2000;Xu et al, 2009;Kasparek et al, 2010). Data quality procedures included visual inspection and checking for sample homogeneity which led to the exclusion of one NonDI-participant from subsequent analyses (details on data preprocessing are provided as supplementary material).…”

Section: Data Pre-processingmentioning

confidence: 99%

Regional gray matter volume and structural network strength in somatic vs. non-somatic delusional disorders

Huber

Lepping

Kirchler

et al. 2018

Progress in Neuro-Psychopharmacology and Biological Psychiatry

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Section: Data Pre-processingmentioning

confidence: 99%

Regional gray matter volume and structural network strength in somatic vs. non-somatic delusional disorders

Huber

Lepping

Kirchler

et al. 2018

Progress in Neuro-Psychopharmacology and Biological Psychiatry

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“…Most of these studies utilised univariate statistical approaches and focused on localising the affected brain regions. In contrast to univariate methods, multivariate approaches effectively characterised the interrelationships among different brain regions and contributed to the understanding of structural covariance patterns of morphological abnormalities caused by normal aging and dementia [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Structural Interactions within the Default Mode Network Identified by Bayesian Network Analysis in Alzheimer’s Disease

et al. 2013

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Alzheimer’s disease (AD) is a well-known neurodegenerative disease that is associated with dramatic morphological abnormalities. The default mode network (DMN) is one of the most frequently studied resting-state networks. However, less is known about specific structural dependency or interactions among brain regions within the DMN in AD. In this study, we performed a Bayesian network (BN) analysis based on regional grey matter volumes to identify differences in structural interactions among core DMN regions in structural MRI data from 80 AD patients and 101 normal controls (NC). Compared to NC, the structural interactions between the medial prefrontal cortex (mPFC) and other brain regions, including the left inferior parietal cortex (IPC), the left inferior temporal cortex (ITC) and the right hippocampus (HP), were significantly reduced in the AD group. In addition, the AD group showed prominent increases in structural interactions from the left ITC to the left HP, the left HP to the right ITC, the right HP to the right ITC, and the right IPC to the posterior cingulate cortex (PCC). The BN models significantly distinguished AD patients from NC with 87.12% specificity and 81.25% sensitivity. We then used the derived BN models to examine the replicability and stability of AD-associated BN models in an independent dataset and the results indicated discriminability with 83.64% specificity and 80.49% sensitivity. The results revealed that the BN analysis was effective for characterising regional structure interactions and the AD-related BN models could be considered as valid and predictive structural brain biomarker models for AD. Therefore, our study can assist in further understanding the pathological mechanism of AD, based on the view of the structural network, and may provide new insights into classification and clinical application in the study of AD in the future.

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“…The lack of gliosis in the brain shows that schizophrenia is not a result of neurodegeneration, but as a result of the damage of neurons caused before the 20 th week of embryogenesis 27 . Reduced grey matter volume has also been reported in patients with schizophrenia undergoing MR 28 .…”

Section: Journal Of Psychiatry • January 2014mentioning

confidence: 76%

Morphometric characteristics of craniofacial features in patients with schizophrenia

Ahmad¹

2014

J Psychiatry

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Background: High incidence of craniofacial dysmorphology in patients with schizophrenia confirms the developmental disruptions during embryonic period. Anthropometry of head and face can give us several clues regarding the maldevelopment of brain. The aim of this study was to assess various dimensions of skull in Schizophrenics. Methods: One hundred and one (101) male and female patients that have been diagnosed with chronic schizophrenia were selected from a psychiatric hospital and 101 healthy controls, which were mostly the paramedical staff of another hospital, were included in the study. The age for both the groups was between 20 to 60 years. The head length, width and height and coronal, axial and sagittal arc lengths, and also body height and weight and BMI of participants were measured. Results: Mean anthropometric measurements of head length, width and height were 18.92, 14.08 and 12.69 in patients and 18.90, 13.89 and 13.38 centimeter in control, also axial, coronal and sagittal arc lengths were 55.85, 35.21 and 33.00 in patients and 55.70, 34.54 and 32.19 centimeter in control group, respectively. Compared to controls the patients had significantly increased coronal (P< 0.022) and Sagittal (P< 0.001) arc lengths. The patients also showed significantly lesser head height (P< 0.001) as compared with controls. Conclusion: The linear measurements of skull in patients with Schizophrenia do not significantly differ from normal populations and/or have slightly differences. Future studies may investigate whether head can be an anatomical marker for patients with schizophrenia.

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

Abstract: Multivariate analysis of gray matter volume seems to be a suitable method for characterization of the pattern of changes at the beginning of the illness in schizophrenia subjects.

Cited by 46 publications

References 84 publications

Regional gray matter volume and structural network strength in somatic vs. non-somatic delusional disorders

Regional gray matter volume and structural network strength in somatic vs. non-somatic delusional disorders

Structural Interactions within the Default Mode Network Identified by Bayesian Network Analysis in Alzheimer’s Disease

Morphometric characteristics of craniofacial features in patients with schizophrenia

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