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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

[1] Ichimiya T, Okubo Y, Suhara T, Sudo Y. Reduced volume of the

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.

http://dx.doi.org/10.1016/j.eurpsy.2016.01.1810

EV826

Neuroimaging biomarker of major

depressive disorder

N. Ichikawa

1 ,

, Y. Okamoto

1

, G. Okada

1

, G. Lisi

2

, N. Yahata

3

,

J. Morimoto

2

, M. Kawato

4

, K. Matsuo

5

, H. Yamagata

5

,

Y. Watanabe

5

, S. Yamawaki

1

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.

http://dx.doi.org/10.1016/j.eurpsy.2016.01.1811

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

https://www.github.com/keyinst/keypy .

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.

http://dx.doi.org/10.1016/j.eurpsy.2016.01.1812

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.