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Der Nervenarzt

, Volume 91, Issue 1, pp 18–25 | Cite as

Bildgebung bei Schizophrenie

Eine Übersicht zu aktuellen Befunden und Entwicklungen
  • Igor NenadićEmail author
Leitthema

Zusammenfassung

Bildgebende Verfahren sind zentrale Methoden zur Erforschung dysfunktionaler neuronaler Netzwerke bei Schizophrenie. Die vorliegende Übersichtsarbeit stellt aktuelle Befunde zur Störung neuronaler Netzwerke auf struktureller und funktioneller Ebene dar und fasst aktuelle Entwicklungen zusammen. Neben großen multizentrischen Analysen haben vor allem methodische Neuerungen, z. B. die Magnetresonanz(MR)-Morphometrie, zu einem Erkenntnisgewinn der Differenzierung früher vs. später struktureller Alterationen geführt. Der Einsatz von „Machine-learning“-Verfahren hat zusätzlich zu Klassifikationsmodellen, etwa zur Abgrenzung der Schizophrenie von anderen Störungsbildern auf biologischer Ebene, auch die multivariate Prädiktion von Therapieansprechen erlaubt. Neuere Ansätze wie BrainAGE, ein Surrogatmarker für beschleunigte Hirnalterungsprozesse, geben zusätzlich zu Verlaufsstudien Einsicht in die Dynamik zwischen gestörter früher Hirnentwicklung und der Progression hirnstruktureller Veränderungen nach Erkrankungsbeginn.

Schlüsselwörter

Machine learning Magnetresonanztomographie Morphometrie Hirnstrukturelle Veränderungen Funktionelle MRT 

Brain imaging in schizophrenia

A review of current trends and developments

Abstract

Imaging methods have become the main approach for identifying dysfunctional neuronal networks in schizophrenia. This review article presents recent results of disorders of neuronal networks at structural and functional levels and summarizes the current developments. Large multicenter analyses have further established patterns of regional brain alterations, while novel methods in magnetic resonance (MR) morphometry have contributed to differentiating early from delayed brain structural changes. The use of machine learning approaches has not only enabled the establishment of classification models using biological data for future differential diagnostic use, it has also facilitated multivariate models for outcome prediction following therapeutic interventions. Novel methods, such as BrainAGE, a surrogate marker of accelerated brain aging processes, have added to longitudinal studies to gain insights into the brain structural dynamics from early brain developmental alterations to progressive structural brain changes after disease onset.

Keywords

Machine learning Magnetic resonance imaging Morphometry Structural brain alterations Functional MRI 

Notes

Danksagung

Der Autor dankt Simon Schmitt und Dr. Gianluca Mingoia für ihre Hilfe bei der Zusammenstellung der Abbildungen.

Einhaltung ethischer Richtlinien

Interessenkonflikt

I. Nenadić gibt an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden vom Autor keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

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Copyright information

© Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  1. 1.Klinik für Psychiatrie und PsychotherapiePhilipps Universität Marburg & Universitätsklinikum Gießen und Marburg (UKGM)MarburgDeutschland

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