Literature DB >> 15279055

Multiple structural brain measures obtained by three-dimensional magnetic resonance imaging to distinguish between schizophrenia patients and normal subjects.

Kazue Nakamura1, Yasuhiro Kawasaki, Michio Suzuki, Hirofumi Hagino, Kenzo Kurokawa, Tsutomu Takahashi, Lisha Niu, Mie Matsui, Hikaru Seto, Masayoshi Kurachi.   

Abstract

This study was designed to investigate the extent to which schizophrenia patients can be differentiated from normal subjects by structural brain measures. High-resolution magnetic resonance imaging scans were performed on 57 schizophrenia patients (30 males, 27 females) and 47 normal controls (25 males, 22 females). Significant enlargements of the left and right body of the lateral ventricle, the left and right sylvian fissure, and the third ventricle were observed in the male patients. Significant enlargements of the left inferior horn, and the left and right sylvian fissure, and a significant volume reduction of the right temporal lobe were observed in the female patients. Discriminant function analysis using brain anatomical measures as variables allowed correct classification of 80.0 percent of the male patients, 80.0 percent of the male controls, 77.8 percent of the female patients, and 86.4 percent of the female controls. These findings support the view that schizophrenia patients have structural deviations in multiple brain areas and that a combination of structural brain measures can distinguish between patients and controls.

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Year:  2004        PMID: 15279055     DOI: 10.1093/oxfordjournals.schbul.a007087

Source DB:  PubMed          Journal:  Schizophr Bull        ISSN: 0586-7614            Impact factor:   9.306


  17 in total

1.  Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals.

Authors:  Martin Rozycki; Theodore D Satterthwaite; Nikolaos Koutsouleris; Guray Erus; Jimit Doshi; Daniel H Wolf; Yong Fan; Raquel E Gur; Ruben C Gur; Eva M Meisenzahl; Chuanjun Zhuo; Hong Yin; Hao Yan; Weihua Yue; Dai Zhang; Christos Davatzikos
Journal:  Schizophr Bull       Date:  2018-08-20       Impact factor: 9.306

2.  Age prediction on the basis of brain anatomical measures.

Authors:  S A Valizadeh; J Hänggi; S Mérillat; L Jäncke
Journal:  Hum Brain Mapp       Date:  2016-11-03       Impact factor: 5.038

3.  A Systematic Characterization of Structural Brain Changes in Schizophrenia.

Authors:  Wasana Ediri Arachchi; Yanmin Peng; Xi Zhang; Wen Qin; Chuanjun Zhuo; Chunshui Yu; Meng Liang
Journal:  Neurosci Bull       Date:  2020-06-03       Impact factor: 5.203

Review 4.  [Neuroimaging in psychiatry: multivariate analysis techniques for diagnosis and prognosis].

Authors:  J Kambeitz; N Koutsouleris
Journal:  Nervenarzt       Date:  2014-06       Impact factor: 1.214

5.  Classification of first-episode schizophrenia patients and healthy subjects by automated MRI measures of regional brain volume and cortical thickness.

Authors:  Yoichiro Takayanagi; Tsutomu Takahashi; Lina Orikabe; Yuriko Mozue; Yasuhiro Kawasaki; Kazue Nakamura; Yoko Sato; Masanari Itokawa; Hidenori Yamasue; Kiyoto Kasai; Masayoshi Kurachi; Yuji Okazaki; Michio Suzuki
Journal:  PLoS One       Date:  2011-06-21       Impact factor: 3.240

6.  A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia.

Authors:  Oguz Demirci; Vincent P Clark; Vince D Calhoun
Journal:  Neuroimage       Date:  2008-02-15       Impact factor: 6.556

Review 7.  Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Authors:  Mohammad R Arbabshirani; Sergey Plis; Jing Sui; Vince D Calhoun
Journal:  Neuroimage       Date:  2016-03-21       Impact factor: 6.556

8.  Towards a brain-based predictome of mental illness.

Authors:  Barnaly Rashid; Vince Calhoun
Journal:  Hum Brain Mapp       Date:  2020-05-06       Impact factor: 5.038

Review 9.  Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification.

Authors:  Joel Weijia Lai; Candice Ke En Ang; U Rajendra Acharya; Kang Hao Cheong
Journal:  Int J Environ Res Public Health       Date:  2021-06-05       Impact factor: 3.390

10.  Mismatch negativity and cognitive performance for the prediction of psychosis in subjects with at-risk mental state.

Authors:  Yuko Higuchi; Tomiki Sumiyoshi; Tomonori Seo; Tomohiro Miyanishi; Yasuhiro Kawasaki; Michio Suzuki
Journal:  PLoS One       Date:  2013-01-17       Impact factor: 3.240

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