Publication: Automatic design of neuromarkers for obsessive compulsive disorder characterisation
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2014-07
Defense date
2014-07-15
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Abstract
This bacherlor thesis proposes a new paradigm to discover biomarkers capable
of characterizing obsessive-compulsive disorder (OCD) by means of machine
learning methods. These biomarkers, named neuromarkers, will be obtained
through the analysis of sets of magnetic resonance images of the brains of
OCD patients and healthy control subjects.
The design of the neuromarkers stems from a method for the automatic
discovery of clusters of voxels, distributed in separate brain regions, relevant
to OCD. This method was recently published by Dr. Emilio Parrado
Hernández, Dr. Vanessa Gómez Verdejo and Dr. Manel Martínez Ramón.
With these clusters as a starting point, we will de ne the neuromarkers as
a set of measurements describing features of these individual regions. Then
we will perform a selection of these neuromarkers, using state of the art
feature selection techniques, to arrive at a reduced, relevant and intuitive
set.
The results will be sent to Dr. Carles Soriano Mas at the Bellvitge University
Hospital in Barcelona, Spain. His feedback will be used to determine
the e cacy of our neuromarkers and their usefulness for psychiatric analysis.
The main goal of the project is to come up with a set of neuromarkers for
OCD characterisation that are easy to interpret and handle by the psychiatric
community.
A paper presenting the methods and results described in this bachelor
thesis, of which the student is the main author, has been submitted and accepted
for presentation in the 2014 European Congress of Machine Learning
(ECML/PKDD 2014). The ECML reported a 23.8% paper acceptance rate
for 2014.
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Keywords
Obsessive compulsive disorder (OCD), Neuromarkers, Machine learning methods, Brain