Literature DB >> 29080229

Individualized prediction of schizophrenia based on the whole-brain pattern of altered white matter tract integrity.

Yu-Jen Chen1, Chih-Min Liu2,3, Yung-Chin Hsu1, Yu-Chun Lo1,4, Tzung-Jeng Hwang2,3, Hai-Gwo Hwu2,3, Yi-Tin Lin2, Wen-Yih Isaac Tseng1,3,5,6.   

Abstract

BACKGROUND: A schizophrenia diagnosis relies on characteristic symptoms identified by trained physicians, and is thus prone to subjectivity. This study developed a procedure for the individualized prediction of schizophrenia based on whole-brain patterns of altered white matter tract integrity.
METHODS: The study comprised training (108 patients and 144 controls) and testing (60 patients and 60 controls) groups. Male and female participants were comparable in each group and were analyzed separately. All participants underwent diffusion spectrum imaging of the head, and the data were analyzed using the tract-based automatic analysis method to generate a standardized two-dimensional array of white matter tract integrity, called the connectogram. Unique patterns in the connectogram that most accurately identified schizophrenia were systematically reviewed in the training group. Then, the diagnostic performance of the patterns was individually verified in the testing group by using receiver-operating characteristic curve analysis.
RESULTS: The performance was high in men (accuracy = 0.85) and satisfactory in women (accuracy = 0.75). In men, the pattern was located in discrete fiber tracts, as has been consistently reported in the literature; by contrast, the pattern was widespread over all tracts in women. These distinct patterns suggest that there is a higher variability in the microstructural alterations in female patients than in male patients.
CONCLUSIONS: The individualized prediction of schizophrenia is feasible based on the different whole-brain patterns of tract integrity. The optimal masks and their corresponding regions in the fiber tracts could serve as potential imaging biomarkers for schizophrenia. Hum Brain Mapp 39:575-587, 2018.
© 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  diffusion magnetic resonance imaging; diffusion spectrum imaging; individualized prediction; schizophrenia; tract-based automatic analysis; white matter tracts

Mesh:

Substances:

Year:  2017        PMID: 29080229      PMCID: PMC6866439          DOI: 10.1002/hbm.23867

Source DB:  PubMed          Journal:  Hum Brain Mapp        ISSN: 1065-9471            Impact factor:   5.038


  46 in total

1.  Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo.

Authors:  T G Reese; O Heid; R M Weisskoff; V J Wedeen
Journal:  Magn Reson Med       Date:  2003-01       Impact factor: 4.668

2.  Shaving diffusion tensor images in discriminant analysis: a study into schizophrenia.

Authors:  M W A Caan; K A Vermeer; L J van Vliet; C B L M Majoie; B D Peters; G J den Heeten; F M Vos
Journal:  Med Image Anal       Date:  2006-09-11       Impact factor: 8.545

Review 3.  Diffusion tensor imaging of the brain.

Authors:  Andrew L Alexander; Jee Eun Lee; Mariana Lazar; Aaron S Field
Journal:  Neurotherapeutics       Date:  2007-07       Impact factor: 7.620

4.  Estimation of fiber orientation and spin density distribution by diffusion deconvolution.

Authors:  Fang-Cheng Yeh; Van Jay Wedeen; Wen-Yih Isaac Tseng
Journal:  Neuroimage       Date:  2011-01-11       Impact factor: 6.556

5.  Automatic whole brain tract-based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy.

Authors:  Yu-Jen Chen; Yu-Chun Lo; Yung-Chin Hsu; Chun-Chieh Fan; Tzung-Jeng Hwang; Chih-Min Liu; Yi-Ling Chien; Ming H Hsieh; Chen-Chung Liu; Hai-Gwo Hwu; Wen-Yih Isaac Tseng
Journal:  Hum Brain Mapp       Date:  2015-06-05       Impact factor: 5.038

6.  A large deformation diffeomorphic metric mapping solution for diffusion spectrum imaging datasets.

Authors:  Yung-Chin Hsu; Ching-Han Hsu; Wen-Yih Isaac Tseng
Journal:  Neuroimage       Date:  2012-07-24       Impact factor: 6.556

Review 7.  The neuropathology of schizophrenia. A critical review of the data and their interpretation.

Authors:  P J Harrison
Journal:  Brain       Date:  1999-04       Impact factor: 13.501

8.  Alterations of white matter integrity related to motor activity in schizophrenia.

Authors:  Sebastian Walther; Andrea Federspiel; Helge Horn; Nadja Razavi; Roland Wiest; Thomas Dierks; Werner Strik; Thomas J Müller
Journal:  Neurobiol Dis       Date:  2011-02-03       Impact factor: 5.996

9.  Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative.

Authors:  J I Nurnberger; M C Blehar; C A Kaufmann; C York-Cooler; S G Simpson; J Harkavy-Friedman; J B Severe; D Malaspina; T Reich
Journal:  Arch Gen Psychiatry       Date:  1994-11

Review 10.  Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring.

Authors:  Klaas E Stephan; Karl J Friston; Chris D Frith
Journal:  Schizophr Bull       Date:  2009-01-20       Impact factor: 9.306

View more
  5 in total

1.  Elucidating the relationship between white matter structure, demographic, and clinical variables in schizophrenia-a multicenter harmonized diffusion tensor imaging study.

Authors:  Johanna Seitz-Holland; Suheyla Cetin-Karayumak; Matcheri Keshavan; Marek Kubicki; Joanne D Wojcik; Amanda Lyall; James Levitt; Martha E Shenton; Ofer Pasternak; Carl-Fredrik Westin; Madhura Baxi; Sinead Kelly; Raquelle Mesholam-Gately; Mark Vangel; Godfrey Pearlson; Carol A Tamminga; John A Sweeney; Brett A Clementz; David Schretlen; Petra Verena Viher; Katharina Stegmayer; Sebastian Walther; Jungsun Lee; Tim Crow; Anthony James; Aristotle Voineskos; Robert W Buchanan; Philip R Szeszko; Anil K Malhotra; Yogesh Rathi
Journal:  Mol Psychiatry       Date:  2021-01-22       Impact factor: 13.437

2.  Functional Connectivity During Visuospatial Processing in Schizophrenia: A Classification Study Using Lasso Regression.

Authors:  Stéphane Potvin; Charles-Édouard Giguère; Adrianna Mendrek
Journal:  Neuropsychiatr Dis Treat       Date:  2021-04-14       Impact factor: 2.570

3.  Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants.

Authors:  Lung-Hao Lee; Chang-Hao Chen; Wan-Chen Chang; Po-Lei Lee; Kuo-Kai Shyu; Mu-Hong Chen; Ju-Wei Hsu; Ya-Mei Bai; Tung-Ping Su; Pei-Chi Tu
Journal:  Eur Psychiatry       Date:  2021-12-23       Impact factor: 5.361

4.  Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics.

Authors:  Chang-Le Chen; Tzung-Jeng Hwang; Yu-Hung Tung; Li-Ying Yang; Yung-Chin Hsu; Chih-Min Liu; Yi-Tin Lin; Ming-Hsien Hsieh; Chen-Chung Liu; Yi-Ling Chien; Hai-Gwo Hwu; Wen-Yih Isaac Tseng
Journal:  Neuroimage Clin       Date:  2022-04-06       Impact factor: 4.891

5.  Alterations of cerebellar white matter integrity and associations with cognitive impairments in schizophrenia.

Authors:  Xuebin Chang; Xiaoyan Jia; Yulin Wang; Debo Dong
Journal:  Front Psychiatry       Date:  2022-09-26       Impact factor: 5.435

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.