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Using Neurotransmitter Vulnerability to Discriminate Schizophrenia Patients from Healthy Controls

Poster No:

1450 

Submission Type:

Abstract Submission 

Authors:

Lisa Hahn1,2, Florian Raabe1, Daniel Keeser1,3, Moritz Rossner1,4, John Fanning1,2, Clara Vetter1, Alkomiet Hasan5, Peter Falkai1, Nikolaos Koutsouleris1,2,6

Institutions:

1Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany, 2Max-Planck Institute of Psychiatry, Munich, Germany, 3Clinical Radiology, Ludwig-Maximilian University, Munich, Germany, 4Systasy Bioscience GmbH, Munich, Germany, 5Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg, Ausgburg, Germany, 6Institute of Psychiatry, Psychology and Neurosciences, King’s College London, London, United Kingdom

First Author:

Lisa Hahn  
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University|Max-Planck Institute of Psychiatry
Munich, Germany|Munich, Germany

Co-Author(s):

Florian Raabe  
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University
Munich, Germany
Daniel Keeser  
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University|Clinical Radiology, Ludwig-Maximilian University
Munich, Germany|Munich, Germany
Moritz Rossner  
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University|Systasy Bioscience GmbH
Munich, Germany|Munich, Germany
John Fanning  
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University|Max-Planck Institute of Psychiatry
Munich, Germany|Munich, Germany
Clara Vetter  
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University
Munich, Germany
Alkomiet Hasan  
Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg
Ausgburg, Germany
Peter Falkai  
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University
Munich, Germany
Nikolaos Koutsouleris  
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University|Max-Planck Institute of Psychiatry|Institute of Psychiatry, Psychology and Neurosciences, King’s College London
Munich, Germany|Munich, Germany|London, United Kingdom
E-Poster

Introduction:

Aside to common psychotic symptoms such as hallucinations, delusions and disorganized thinking, schizophrenia (SCZ) is characterized by structural alterations such as volume reductions in the temporal, frontal, and parietal lobes. Here, we evaluated if the co-localization of these alterations with the distribution of specific neurotransmitter systems, i.e. an indication of neurotransmitter vulnerability) can be utilized to discriminate SCZ patients from healthy controls (HC).

Methods:

Maps of grey matter volume (GMV) were derived from T1-weighted structural magnetic resonance imaging for 445 SCZ patients (mean age = 34.0 ± 11.3, 103 females) and 416 HC (mean age = 33.5 ± 11.3, 108 females) from the Multimodal Imaging in Chronic Schizophrenia Study (MIMICSS; part of the PsyCourse study), the Center for Biomedical Research Excellence (COBRE; part of the COIN study) cohort, Mind Clinical Imaging Consortium (MCIC; part of the COIN study) cohort, the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study, and the Munich cohort (internal dataset). Two linear classifiers utilizing a leave-site-out cross-validation (CV) design (CV1: 10x5, CV2: 1x10) for the discrimination of SCZ patients and HC were created. The preprocessing steps computed within the CV framework included offset correction using the global mean, correction for age and sex using partial correlations, the model-specific feature computation, and standardization. The model-specific features either entailed whole-brain correlation coefficients between GMV and 29 nuclear-imaging derived neurotransmitter maps (e.g., receptor and transporter density) from a healthy volunteer population computed using the JuSpace toolbox or 29 eigenvariates computed using principal component analysis (PCA) as a control model.

Results:

The first classifier (using whole-brain correlation coefficients) discriminated SCZ from HC with a balanced accuracy of 64.1 % and area under the curve of 0.68 (sensitivity = 64.7 %, specificity = 63.6 %). The control classifier (using PCA eigenvariates) performed similarly, discriminating SCZ from HC with a balanced accuracy of 66.4 % and area under the curve of 0.72 (sensitivity = 75.2 %, specificity = 57.5 %).

Conclusions:

SCZ patients were distinguishable from HC based on the association of structural alterations with specific neurotransmitter systems. These findings suggest that this indication of neurotransmitter vulnerability might serve as a diagnostic biomarker.

Modeling and Analysis Methods:

Classification and Predictive Modeling 1

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Transmitter Systems

Novel Imaging Acquisition Methods:

Anatomical MRI 2

Keywords:

Machine Learning
Neurotransmitter
Positron Emission Tomography (PET)
Schizophrenia
STRUCTURAL MRI

1|2Indicates the priority used for review
Supporting Image: Figure1.png
   ·Model Performance for Neutrotransmitter and PCA Classifiers
 

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