European Psychiatry - Volume 33 - March 2016

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S614 24th European Congress of Psychiatry / European Psychiatry 33S (2016) S349–S805
Conclusion This report presents a case of cerebellar lesions pre-
senting with neuropsychiatric symptomatology. As in this case,
cerebellar pathologies such as mega cisterna magna that could
have a role in development psychotic symptoms should be paid
attention.
Disclosure of interest The authors have not supplied their decla-
ration of competing interest.
Reference
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cerebellar vermis in neuroleptic-naive schizophrenia. Biol Psy-
chiatry 2001;49:20–7.
[2] Kollias SS, Ball Jr WS, Prenger EC. Cystic malformations of the
posterior fossa: differential diagnosis clarified through embry-
ologic analysis. Radiographics 1993;13:1211–31.
EV826
Neuroimaging biomarker of major
depressive disorder
N. Ichikawa ∗, Y. Okamoto , G. Okada , G. Lisi , N. Yahata ,
J. Morimoto , M. Kawato , K. Matsuo , H. Yamagata ,
Y. Watanabe , S. Yamawaki
1 Hiroshima University Graduate School of Biomedical & Health
Sciences, Psychiatry and Neurosciences, Hiroshima, Japan
2 ATR Computational Neuroscience Labs, Department of Brain Robot
Interface, Kyoto, Japan
3 National Institute of Radiological Sciences, Molecular Imaging
Center, Chiba, Japan
4 ATR, Brain Information Communication research Laboratory Group,
Kyoto, Japan
5 Yamaguchi University Graduate School of Medicine, Division of
Neuropsychiatry, Department of Neuroscience, Ube, Japan
∗ Corresponding author.
Introduction Recent studies have shown that it is important
to understand the brain mechanism specifically by focusing on
the common and unique functional connectivity in each disorder
including depression.
Objectives To specify the biomarker of major depressive disorder
(MDD), we applied the sparse machine learning algorithm to clas-
sify several types of affective disorders using the resting state fMRI
data collected in multiple sites, and this study shows the results of
depression as a part of those results.
Aims The aimof this study is to understand some specific pattern
of functional connectivity in MDD, which would support diagno-
sis of depression and development of focused and personalized
treatments in the future.
Methods The neuroimaging data from patients with major
depressive disorder (MDD, n = 100) and healthy control adults (HC:
n = 100) from multiple sites were used for the training dataset. A
completely separate dataset (n = 16)was kept aside for testing. After
all preprocessing of fMRI data, based on one hundred and forty
anatomical region of interests (ROIs), 9730 functional connectivi-
ties during resting states were prepared as the input of the sparse
machine-learning algorithm.
Results As results, 20 functional connectivities were selected
with the classification performance of Accuracy: 83.0% (Sensitiv-
ity: 81.0%, Specificity: 85.0%). The test data, which was completely
separate from the training data, showed the performance accuracy
of 83.3%.
Conclusions The selected functional connectivities based on the
sparse machine learning algorithm included the brain regions
which have been associated with depression.
Disclosure of interest The authors have not supplied their decla-
ration of competing interest.
EV827
Keypy – An open source library for
EEG microstate analysis
P. Milz
The KEY Institute for Brain-Mind Research, Department of Psychiatry,
Psychotherapy and Psychosomatics, University Hospital of
Psychiatry, Zurich, Switzerland
The brain’s electric field configuration reflects its momentary,
global functional state. The fluctuations of these states can be ana-
lyzed at millisecond resolution by the EEG microstate analysis.
This analysis reportedly allowed the detection of brain state dura-
tion, occurrence, and sequence aberrations in psychiatric disorders
such as schizophrenia, dementia, and depression. Several existing
software solutions implement the microstate analysis, but they all
require extensive user-interaction. This represents a major obsta-
cle to time-efficient automated analyses and parameter exploration
of large EEG datasets. Scriptable programming languages such as
Python provide ameans to efficiently automate such analysiswork-
flows.
For this reason, I developed the KEY EEG Python Library keypy. This
library implements all steps necessary to compute the microstate
analysis based on artefact free segments of EEG. It includes func-
tions to carry out the necessary preprocessing (data loading,
filtering, average referencing), modified k-means clustering based
microstate identification, principal component based mean com-
putation (across recording runs, conditions, participants, and or
participant groups), and to retrieve the microstate class based
statistics necessary to compare microstate parameters between
groups and/or conditions. Keypy is an open source library and freely
available from
Keypy provides a platform for automated microstate analysis of
large-scale EEG datasets from psychiatric patient populations and
their comparison to healthy controls. It is easily applicable and
allows efficient identification of deviant brain states in clinical con-
ditions.
Disclosure of interest The authors have not supplied their decla-
ration of competing interest.
EV828
Agenesis of the corpus callosum in a
patient with bipolar disorder
J. Nogueira∗, R. Ribeiro , J. Vieira , R. Sousa , S. Mendes , B. Ribeiro ,
M. Salta , B. Barata , A. Gamito , R. Mendes
Centro Hospitalar de Setúbal, Psiquiatria e Saúde Mental, Setúbal,
Portugal
∗ Corresponding author.
Background The corpus callosum (CC) is the largest white matter
structure in the brain, which plays a crucial role in interhemi-
spheric communication. Agenesis of the CC is a rare development
anomaly, with unknown cause. It could be asymptomatic or asso-
ciated with mental retardation and neurologic symptoms. Some
case reports, post-mortem studies and image studies have linked
thickness reduction and agenesis of CC with psychotic symptoms,
mainly in schizophrenia patients. Lately, anatomical abnormalities
in the CC have been reported in patients with Bipolar Disorder (BD).
Case report A 52-year-old woman was brought to the emergency
room by the authorities after being physically aggressive to her
13-year-old daughter and inappropriate behavior in public. At the
emergency department her mood was elevated with emotional
lability, dispersible attention, slight increase ofmotor activity, pres-
sured and difficult to interrupt speech, grandious and self-referent
delusional ideas.
Her past history revealed hippomaniac episodes characterized by
periods of excessive shopping and hyperphagia. In 2008, she had a
major depressive episode.