DiagnosisofBipolarDisorderbasedonPrincipal - PowerPoint PPT Presentation

Diagnosis
of
Bipolar
Disorder
based
on
Principal
 Component
Analysis
and
Support
Vector
Machines
 over
the
MRI
deforma@on
Jacobian
 M.
Termenon,
M.
Graña,
A.
Besga,

J.
Echeveste,
J.
M.
Pérez,
 A.
Gonzalez‐Pinto
 Grupo
de
Inteligencia
Computacional,
UPV/EHU,
 
Unidad
de
Inves@gación
en
Psiquiatría,
Hospital
San@ago
Apostol,
Vitoria‐ Gasteiz.

 Departamento
de
Resonancia
Magné@ca,
Osatek‐Vitoria.

 Servicio
de
Neurología,
Hospital
San@ago
Apostol,
Vitoria‐Gasteiz.
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2013,
Milkow,
Poland
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Contents
 • Introduc@on
and
mo@va@on
 • Materials
 • Methods
 • Results
 • Conclusions
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Introduc@on
 • Bipolar
disorder
(BD)

 – psychiatric
disorder

 • at
least
one
episode
of
mania
or
hypomania

 • or
a
mixed
episode

<‐>
a
depressive
episode,

 • changes
in
mood
states
and
psycho@c
symptoms.
 • 
It
is
associated
with
cogni@ve,
affec@ve
and
func@onal
 impairment.
 • 
A
diagnosis
BD
 – symptoms,

 – course
of
illness
and,

 – family
history,

 – neuroimaging

 • iden@fied
several
regions
that
are
affected
by
the
disease


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2013,
Milkow,
Poland
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Introduc@on
 • we
compare
brain
structural
MRI
of
healthy
controls
with
 pa@ents
with
bipolar
disorder,

 – to
discriminate
between
both
groups

 – selec@ng
relevant
informa@on
embedded
in
the
images.

 • Machine
Learning
(ML)

 – of
feature
vectors
extracted
from
the
deforma@on
of
the
 structural
MRI
images

 – computer
aided
diagnosis
(CAD)
tools.

 • Preprocessing,

 – registra@on
of
the
volumes,

 – affine
and
non‐linear
registra@ons
to
a
standard
MNI
template.
 • The
Jacobian
of
the
deforma@on
at
each
voxel
will
be
used
to
extract
 the
relevant
features

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2013,
Milkow,
Poland
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Materials
 • Pa@ents
recruited
at
the
psychiatric
unit
at
Alava
University
Hospital,
 Vitoria
(Spain)

 • All
pa@ents
were
living
independently
in
the
community.

 – clinical
evalua@on,

 – a
cogni@ve
and
a
neuropsychological
evalua@on,

 – and
brain
imaging
(MRI).

 • Forty
men
and
women
elderly
subjects
were
included
in
the
present
study.

 – The
healthy
control
group
included
20
subjects
without
memory
complaints

 • (mean
age
74.10
(SD:8.03
years))

 – and
BD
group
included
20
subjects
fulfilling
DSM
IV’s
criteria

 • (mean
age
70.37
(SD:
9.07
years)).

 • Subjects
with
psychiatric
disorders
(i.e.
major
depression)
or
other
 condi@ons
(i.e.
brain
tumors)
were
not
considered
for
this
study.
 • Structural
MRI
3D
data
(T1‐weighted).

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2013,
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Poland
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Methods
 • Image
preprocessing
 – Affine
and
elas@c
registra@on
 – Tensor
deforma@on
map
 – Jacobian
at
each
voxel
 • Feature
selec@on
 – Voxel
sites
with
dis@nc@ve
values
of
the
deforma@on
 Jacobian
 – Dimension
reduc@on:
PCA
 • Classifica@on:
 – Linear
SVM
 – Valida@on:
Leave
One
Out

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2013,
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Poland
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Methods
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2013,
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Poland
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Methods
 • Feature
selec@on
 – Compute
voxel‐wise
means
of
each
class

 • healthy
controls
 • BD
pa@ents
 – Compute
histogram
of
class
means
differences
 – Select
the
tail
percen@le
according
to
a
threshold

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2013,
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Methods
 • Classifica@on
performance
measures
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Results
 Voxel
loca@ons
for
feature
selec@on
with
threshold
0.4

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Results
 3
first
PCA
before
(lel)
and
aler
(right)
feature
selec@on
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2013,
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Poland
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Results
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2013,
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Results
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2013,
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Poland
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Conclusions
 • Free
of
circularity
 – Feature
selec@on
is
performed
in
each
LOO
step
 – PCA
is
unsupervised
 • Selected
voxels
are
located
in

 – thalamus
and
angular
gyrus,

 • precuneous
cortex,
precentral
and
postcentral
gyrus,
supramarginal
gyrus,
 rightlateral
ventricle,
superior
parietal
lobe,
inferior
temporal
gyrus
and
 cerebellum.
 • Thalamus
is
one
of
the
most
relevant
biomarkers
in
bipolar
disorder

 • Also
superior
parietal
lobe
,
precuneous
cortex,
precentral
gyrus
and
 Cerebellum.

 • Valida@on
of
the
approach

 – with
no
disease
 a
priori
 informa@on
we
find
brain
discriminant
regions
 consistent
with
known
biomarkers
of
BD

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2013,
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Poland
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