Literature DB >> 19833216

Distinct pattern of brain structural deficits in subsyndromes of schizophrenia delineated by psychopathology.

Igor Nenadic1, Heinrich Sauer, Christian Gaser.   

Abstract

Brain morphological changes are among the best-studied potential endophenotypes in schizophrenia and linked to genetic liability and expression of disease phenotype. Yet, there is considerable heterogeneity across individual subjects making its use as a disease-specific marker difficult. In this study we consider psychopathological variability of disease phenotype to delineate subsyndromes of schizophrenia, link them to distinct brain morphological patterns, and use a classification approach to test specificity of achieved discrimination. We first applied voxel-based morphometry (VBM) to compare 99 patients with DSM-IV schizophrenia (stable psychopathology and antipsychotic medication) with 113 matched healthy controls, then delineated three subgroups within the patient cohort based on psychopathology pattern and compared differential patterns of grey matter abnormalities. Finally, we tested accuracy of assigning any individual MRI scan to either the control group or any of the three patient subgroups. While VBM analysis showed overlap of brain structural deficits mostly in prefrontal areas, the disorganised subsyndrome showed stronger deficits in medial temporal and cerebellar regions, the paranoid/hallucinatory subsyndrome showed additional effects in the superior temporal cortex, and the negative subsyndrome showed stronger deficits in the thalamus. Using an automated algorithm, we achieved 95.8% accuracy classifying any given scan to one of the subgroups. Patterns of psychopathology are meaningful parameters in reducing heterogeneity of brain morphological endophenotypes in schizophrenia.

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Year:  2009        PMID: 19833216     DOI: 10.1016/j.neuroimage.2009.10.014

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  31 in total

1.  Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers.

Authors:  Nikolaos Koutsouleris; Eva M Meisenzahl; Stefan Borgwardt; Anita Riecher-Rössler; Thomas Frodl; Joseph Kambeitz; Yanis Köhler; Peter Falkai; Hans-Jürgen Möller; Maximilian Reiser; Christos Davatzikos
Journal:  Brain       Date:  2015-05-01       Impact factor: 13.501

2.  Heterogeneity of structural brain changes in subtypes of schizophrenia revealed using magnetic resonance imaging pattern analysis.

Authors:  Tianhao Zhang; Nikolaos Koutsouleris; Eva Meisenzahl; Christos Davatzikos
Journal:  Schizophr Bull       Date:  2014-09-26       Impact factor: 9.306

Review 3.  Decoding patterns of human brain activity.

Authors:  Frank Tong; Michael S Pratte
Journal:  Annu Rev Psychol       Date:  2011-09-19       Impact factor: 24.137

4.  Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

Authors:  Nicolas Honnorat; Aoyan Dong; Eva Meisenzahl-Lechner; Nikolaos Koutsouleris; Christos Davatzikos
Journal:  Schizophr Res       Date:  2017-12-21       Impact factor: 4.939

5.  HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.

Authors:  Erdem Varol; Aristeidis Sotiras; Christos Davatzikos
Journal:  Neuroimage       Date:  2016-02-23       Impact factor: 6.556

Review 6.  A systems neuroscience perspective of schizophrenia and bipolar disorder.

Authors:  Sophia Frangou
Journal:  Schizophr Bull       Date:  2014-03-08       Impact factor: 9.306

7.  Distinct Patterns of Cerebral Cortical Thinning in Schizophrenia: A Neuroimaging Data-Driven Approach.

Authors:  Genichi Sugihara; Naoya Oishi; Shuraku Son; Manabu Kubota; Hidehiko Takahashi; Toshiya Murai
Journal:  Schizophr Bull       Date:  2017-07-01       Impact factor: 9.306

Review 8.  [Brain imaging in schizophrenia : A review of current trends and developments].

Authors:  Igor Nenadić
Journal:  Nervenarzt       Date:  2020-01       Impact factor: 1.214

9.  Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Authors:  Ganesh B Chand; Dominic B Dwyer; Guray Erus; Aristeidis Sotiras; Erdem Varol; Dhivya Srinivasan; Jimit Doshi; Raymond Pomponio; Alessandro Pigoni; Paola Dazzan; Rene S Kahn; Hugo G Schnack; Marcus V Zanetti; Eva Meisenzahl; Geraldo F Busatto; Benedicto Crespo-Facorro; Christos Pantelis; Stephen J Wood; Chuanjun Zhuo; Russell T Shinohara; Haochang Shou; Yong Fan; Ruben C Gur; Raquel E Gur; Theodore D Satterthwaite; Nikolaos Koutsouleris; Daniel H Wolf; Christos Davatzikos
Journal:  Brain       Date:  2020-03-01       Impact factor: 13.501

10.  Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia.

Authors:  Dominic B Dwyer; Carlos Cabral; Lana Kambeitz-Ilankovic; Rachele Sanfelici; Joseph Kambeitz; Vince Calhoun; Peter Falkai; Christos Pantelis; Eva Meisenzahl; Nikolaos Koutsouleris
Journal:  Schizophr Bull       Date:  2018-08-20       Impact factor: 9.306

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