The document presents a predictive and population model for Alzheimer's disease based on structural neuroimaging, focusing on the volumetric degeneration of gray matter. It establishes parameters for evaluating individual states against the population model, aiming for early diagnosis. The developed system shows high accuracy and sensitivity in detecting early Alzheimer's using MRI analysis.

1. Predic've
and
Popula'onal
Model
for
Alzheimer's
Disease
using
Structural
Neuroimaging
®
Domingo
López
Rodríguez,
Antonio
García
Linares
Brain
Dynamics.
University
of
Málaga.
Málaga.
ObjecEves:
This
paper
aims
populaEonal
modeling
of
volumetric
degeneraEon
of
the
gray
maIer
due
to
Alzheimer's
disease,
to
establish
the
parameters
of
degeneraEon,
and
to
contrast
the
state
of
an
individual
with
respect
to
that
model.
In
this
way,
you
can
get
an
early
diagnosis
of
the
disease.
PopulaEonal
Model
Above:
Leh:
Different
evoluEons
of
parahippocampal
areas
in
controls
(red)
and
in
AD
paEents
(blue).
Right:
StaEsEcal
differences
of
gray
maIer
volume
loss
between
controls
and
AD.
Red
indicates
more
difference.
In
the
populaEonal
model,
the
regional
differences
between
healthy
and
AD
paEents
is
presented
(p-­‐value
of
the
gray
maIer
volume
difference)
in
the
table
aside.
STRUCTURE
Paracentral)Lobe)(Right)
Paracentral)Lobe)(Left)
Postcentral)Gyrus)(Right)
Postcentral)Gyrus)(Left)
Precentral)Gyrus)(Right)
Angular)Gyrus)(Right)
Angular)Gyrus)(Left)
Calcarine)Sulcus)(Left)
Cuneus)(Left)
Inferior)Occipital)Gyrus)(Right)
50
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
60
0.000
0.000
0.000
0.002
0.000
0.000
0.000
0.000
0.000
0.005
70
0.000
0.000
0.000
0.000
0.000
0.001
0.011
0.043
0.043
0.008
80
0.030
0.029
0.003
0.019
0.050
0.016
0.003
0.134
0.057
0.007
Classifier
and
PredicEve
System
Decision
Rule
Example:
IF
(AGE
>
55)
AND
(TEMPORAL
MIDDLE
GYRUS
THICKNESS
<=
2.448985)
THEN
CLASS
=
AD
[PROBABILITY
=
0.946]
Parameter
Accuracy
Sensitivity
Specificity
Value
91.48%
90.80%
92.30%
Conclusions:
We
have
developed
a
system
capable
of
detecEng
early
AD
using
MRI
analysis
with
a
high
success
rate,
non-­‐invasive,
fast
and
objecEve.