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Original article
Disponible online el 29 de Enero de 2022
Frontal lobes dysfunction across clinical clusters of acute schizophrenia
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1
Filippo Corponia,b, Yana Zorkinac, Daniel Stahlb, Andrea Murrud,
Autor para correspondencia
amurru@clinic.cat

Corresponding author.
, Eduard Vietad, Alessandro Serrettie, Аnna Morozovac,f, Alexander Reznikf,g, Georgiy Kostyukf, Vladimir Pavlovich Chekhoninc,h
a School of Informatics, University of Edinburgh, Edinburgh, UK
b Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
c Department Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Moscow, Russia
d Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clínic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
e Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
f N.A. Alekseev Psychiatric Clinical Hospital №1, Moscow, Russia
g Moscow State University of Food Production Moscow, Russia
h Department of Medical Nanobiotechnology, Pirogov Russian National Research Medical University, Moscow, Russia
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Abstract
Introduction

Schizophrenia is a clinical construct comprising manifold phenotypes underlying heterogeneous biological underpinnings. The Positive and Negative Syndrome Scale (PANSS) represents the standard tool in the clinical characterization of patients affected by schizophrenia, allowing to detect different clinical profiles within the disorder. Frontal lobes are a key area of brain dysfunction in schizophrenia. We investigated whether different clinical profiles in acute schizophrenia show differences in frontal lobes dysfunction or not.

Methods

We defined PANSS-derived principal components in a sample of 516 acute patients. These components were used as clustering variables in a finite-mixture model. Frontal lobe impairment, measured with the frontal assessment battery (FAB) score, was adjusted for disease duration and compared across the clinical clusters with ANCOVA. A supervised-learning approach was then implemented to reveal most informative PANSS items.

Results

A three-cluster solution emerged: a first profile with high-moderate expression for the positive and excitability/hostility component; a second profile scoring high on depression/anxiety and low on other components; a third profile, comprising the majority of the study population (74%), with a heavy affection on the negative-disorganization dimensions. After controlling for disease duration, frontal lobe impairment significantly differed across the aforementioned clusters, with the third cluster being the most affected. Two PANSS items presented the highest predictive value for FAB total score.

Conclusions

Among negative and disorganization symptoms, “difficulty in abstract thinking” and “lack of spontaneity/flow in conversation” are specifically mapped to higher levels of frontal lobes dysfunction, hinting at similar features with other neurological disorders involving frontal lobes.

Keywords:
Schizophrenia
Frontal lobe
Precision medicine
Cluster analysis
Machine learning

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