Discovering brain regions relevant to obsessive-compulsive ...miguel/MLG/adjuntos/MEDIA14.pdf ·...

Discovering brain regions relevant to obsessive-compulsive disorder identification

through bagging and transductionEmilio Parrado-Hernández1, V. Gómez-Verdejo1,

M. Martínez-Ramón1, J. Shawe-Taylor2, P. Alonso3,4, J. Pujol5, J. M. Menchón3,4, N. Cardoner3,4, C. Soriano-Mas3,4

1Signal Processing and Communications, Universidad Carlos III de Madrid, Spain2CSML and Computer Science, University College London, UK

3Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain4 CIBERSAM, Carlos III Health Institute, Spain

5CRC Hospital del Mar, Barcelona, Spain

Intl. Workshop on Pattern Recognition in Neuroimage 2012UCL, London, UK, July 3, 2012

Saturday, June 14, 14

June 16, 2014 EPH / DTSC / UC3M

Outline

• Introduction

• Dataset

• Feature Selection Algorithm

• Linear Classifiers Interpretation

• Bagging and Conformal Analysis

• Complete System

• Experiments

• Conclusions

2


Introduction



MRI and OCD detection

• Obsessive Compulsive Disorder (OCD) related to malformations and dystrophy in brain tissues (Soriano-Mas, 2007)

• Applications of sMRI to OCD

• diagnosis

• stratification of patients

• prediction of most suitable treatment

• characterization of the disease

4



Machine learning and sMRI

• Small sample problem: sMRI datasets comprise over 2M voxels in only tens to hundreds of images

• OCD is a very localized disease

• Approaches:

• (univariate) Voxel Based Morphometry: and use voxels as proxy for brain areas

• plug complete brain scans to an SVM/GP/etc and use kernels

• Fix a priori brain regions (atlas, clustering of voxels) and apply Lasso or bagging

5



Neuromarkers road map✓ Automatic and interpretable voxel selection [PRNI2012]

✓ Discover brain regions relevant to OCD [MEDIA2014]

✓ Characterization with neuromarkers [ECML2014]

★ Multicenter analysis (massive simulations, big data)

★ Capture interactions among brain regions

★ Application to other diseases with MRI

★ Application to other problems [NIPS2014??]

★ Integration of other modalities (genes, psychological tests)

6


Data Set



OCD Dataset• 172 MRI scans from 86 outpatitents with OCD and 86

control subjects (same age and gender distribution)

• 1.5-T Signa Excite, high-resolution T1, 3-D fast spoiled gradient inversion-recovery prepared sequence with 130 contiguous slices (TR, 11.8 ms; TE,4.2 ms; flip angle, 15º; field of view, 30 cm; 256x256 pixel matrix; slice thickness, 1.2mm)

• Matlab-SPM8 preproc. with grey matter segmentation and DARTEL normalization to SPM-T1 template

• Smoothed with a 4 mm FWHM Gaussian kernel

• 172x482000 data matrix with values in [0.2, 1.4]8


Voxel Selection Algorithm



Classifier interpretation

10

Starplots (Bi et al. 2003): Linear Classifiers enable interpretation of features role in the decision

w1-w2

0w4

-w5

wd-4-wd-3

0wd-1

-wd

...

...

d voxels

OCD

><

Healthy

b



Bagging SVMs

• Small sample problem implies a high risk of overfitting: sign of weights not aligned with the discrimination of OCD but with separation of individual instances.

• Bagging: Learn 20K linear classifiers (SVMs) with 86 training examples each

• Look for weights that are consistent in sign through all the 20K simulations

• Norm 2 SVM: less aggressive than norm 1 (this only points out M nonzero weights per simulation)

11



Conformal analysis

• Bagging does not cope with all the overfitting risk.

• If we introduce the test vector only in the voxel selection, we arrive at 0% test error rate in a Leave One Out classification.

• Conformal analysis: measure the strangeness of the test sample being assigned to every output class

• Do the bagging twice, 1st consider test sample as positive, 2nd consider test sample as negative

• Intersect both voxel sets12



Complete System

13

Training data {x(i), yi}li=1

Test data xtest

{x(1), · · · , x(l), xtest}{y(1), · · · , y(l), 1}

y = 1test

y = !1test

{x(1), · · · , x(l), xtest}{y(1), · · · , y(l),!1}

V+ V!

V = V+ " V!

SVM Bagging


Discovery of brain areas



Clustering of voxels• Leave-One-Out experiments yield many

discrimination patterns

• Cluster selected voxels using connectivity: detect groups of connected voxels (718 groups, 60 voxels average population)

• Each cluster groups voxels of the same sign

• Crossvalidate a minimum group size (55 voxels, 170 groups)

• Align clusters to plot them in a single figure

15


Experimental Results



Classification Accuracy

17

1 2 3 4 5 6 7 8 9x 104

0.2

0.3

0.4

0.5

CE

# Voxels

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 21

1.5

2

2.5

3

3.5

4

VBMRFEBS

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 21

1.5

2

2.5

3

3.5

4

TïVBMTïRFETïBS

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 21

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

Validated T−BS

t-Test!RFE!BS!

T-t-Test!T-RFE!T-BS!

Validated T-BS!

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6x 104

0.2

0.3

0.4

0.5

CE

# Voxels

M=86M=90M=110M=130



Brain regions (I)

18

Table 2: Analysis of the regions discovered by clustering connected

voxels in the selected set.

Region name Consistency Group size (+/�) p

Left Superior Frontal Gyrus 100.00 % 0.00 / 333.97 0.000

Right Superior Occipital Gyrus 98.26 % 0.00 / 188.45 0.001

Left Lateral Temporal Pole 100.00 % 0.00 / 382.96 0.001

Left Anterior Insula, Frontal Operculum 98.26 % 0.00 / 82.32 0.001

Right Anterior Insula, Frontal Operculum 100.00 % 6.16 / 1207.35 0.001

Left Fusiform Gyrus 96.51 % 0.00 / 81.52 0.001

Right Lateral Temporal Pole 100.00 % 0.00 / 234.92 0.001

Left Superior Parietal Cortex 93.02 % 0.00 / 155.66 0.002

Dorso-Medial Prefrontal Cortex 100.00 % 0.00 / 231.70 0.002


Left Middle Frontal Gyrus 100.00 % 0.00 / 114.98 0.002

Left Medial Temporal Pole 100.00 % 0.00 / 1077.95 0.002


Supplementary Motor Area 99.42 % 0.00 / 187.44 0.003

Right Superior Temporal Gyrus 100.00 % 394.28 / 0.00 0.003

Right Medial Temporal Pole 99.42 % 0.00 / 415.89 0.003

Left Angular Gyrus 100.00 % 0.31 / 1500.99 0.003

Left Superior Occipital Gyrus 100.00 % 0.00 / 176.97 0.004

Right Precentral Gyrus 97.67 % 211.93 / 0.00 0.006

Gyrus Rectus 100.00 % 0.00 / 900.17 0.006

Right Parahippocampal Cortex 100.00 % 197.04 / 0.00 0.007

Right Putamen, Insular Cortex 100.00 % 1985.25 / 0.00 0.007

Right Caudal Hippocampus 100.00 % 0.00 / 442.27 0.008


Right Middle Temporal Gyrus 100.00 % 0.00 / 622.59 0.008

Left Inferior Temporal Gyrus 100.00 % 462.21 / 0.00 0.008

27



Brain regions (II)

19

Table 2: Analysis of the regions discovered by clustering connected

voxels in the selected set.

Region name Consistency Group size (+/�) p

Left Superior Frontal Gyrus 100.00 % 0.00 / 333.97 0.000

Right Superior Occipital Gyrus 98.26 % 0.00 / 188.45 0.001

Left Lateral Temporal Pole 100.00 % 0.00 / 382.96 0.001

Left Anterior Insula, Frontal Operculum 98.26 % 0.00 / 82.32 0.001

Right Anterior Insula, Frontal Operculum 100.00 % 6.16 / 1207.35 0.001

Left Fusiform Gyrus 96.51 % 0.00 / 81.52 0.001

Right Lateral Temporal Pole 100.00 % 0.00 / 234.92 0.001

Left Superior Parietal Cortex 93.02 % 0.00 / 155.66 0.002




Left Medial Temporal Pole 100.00 % 0.00 / 1077.95 0.002



Right Superior Temporal Gyrus 100.00 % 394.28 / 0.00 0.003

Right Medial Temporal Pole 99.42 % 0.00 / 415.89 0.003

Left Angular Gyrus 100.00 % 0.31 / 1500.99 0.003

Left Superior Occipital Gyrus 100.00 % 0.00 / 176.97 0.004


Gyrus Rectus 100.00 % 0.00 / 900.17 0.006

Right Parahippocampal Cortex 100.00 % 197.04 / 0.00 0.007

Right Putamen, Insular Cortex 100.00 % 1985.25 / 0.00 0.007

Right Caudal Hippocampus 100.00 % 0.00 / 442.27 0.008


Right Middle Temporal Gyrus 100.00 % 0.00 / 622.59 0.008

Left Inferior Temporal Gyrus 100.00 % 462.21 / 0.00 0.008

27Saturday, June 14, 14


Brain regions (III)

20

Left Precentral Gyrus 99.42 % 0.00 / 130.40 0.010

Anterior Cerebellum, Lingual Gyrus, 100.00 % 12627.95 / 1167.98 0.011

Right Caudate, Bilateral Fusiform Gyrus,

Medial Thalamus, Left Putamen, Midbrain,

Rostral Anterior Cingulate Cortex

Right Cerebellar Hemisphere 100.00 % 397.91 / 0.00 0.012

Right Parieto-Occipital Sulcus 100.00 % 0.00 / 126.76 0.012

Left Caudal Inferior Temporal Gyrus 91.28 % 0.00 / 145.14 0.012

Left Cerebellar Hemisphere 95.93 % 103.89 / 0.00 0.015


Right Angular Gyrus 90.12 % 0.00 / 136.99 0.021

Right Middle Frontal Gyrus 100.00 % 0.00 / 126.07 0.023

Right Caudate 92.44 % 124.85 / 0.00 0.024


Left Postcentral Gyrus 94.77 % 86.50 / 0.00 0.034

Left Parieto-Occipital Sulcus 100.00 % 213.54 / 0.00 0.047


Left Middle Temporal Gyrus 98.84 % 94.59 / 0.00 0.051

Mid-Cingulate Cortex 100.00 % 0.00 / 230.92 0.055

Left Ventrolateral Prefrontal Cortex 100.00 % 740.83 / 0.00 0.061

Left Supramarginal Gyrus 100.00 % 476.01 / 0.00 0.061

Precuneus 100.00 % 105.60 / 0.00 0.076

Left Sensorimotor Cortex 98.84 % 131.02 / 0.00 0.085



Left Inferior Frontal Gyrus (Orbitalis) 100.00 % 195.51 / 303.29 0.165


Left Orbital Cortex 91.86 % 119.09 / 0.00 0.175

Dorsal Anterior Cingulate Cortex 100.00 % 126.12 / 0.00 0.202

28



Brain regions (IV)

21


Anterior Cerebellum, Lingual Gyrus, 100.00 % 12627.95 / 1167.98 0.011

Right Caudate, Bilateral Fusiform Gyrus,

Medial Thalamus, Left Putamen, Midbrain,

Rostral Anterior Cingulate Cortex


Right Parieto-Occipital Sulcus 100.00 % 0.00 / 126.76 0.012

Left Caudal Inferior Temporal Gyrus 91.28 % 0.00 / 145.14 0.012

Left Cerebellar Hemisphere 95.93 % 103.89 / 0.00 0.015


Right Angular Gyrus 90.12 % 0.00 / 136.99 0.021

Right Middle Frontal Gyrus 100.00 % 0.00 / 126.07 0.023

Right Caudate 92.44 % 124.85 / 0.00 0.024


Left Postcentral Gyrus 94.77 % 86.50 / 0.00 0.034

Left Parieto-Occipital Sulcus 100.00 % 213.54 / 0.00 0.047


Left Middle Temporal Gyrus 98.84 % 94.59 / 0.00 0.051

Mid-Cingulate Cortex 100.00 % 0.00 / 230.92 0.055

Left Ventrolateral Prefrontal Cortex 100.00 % 740.83 / 0.00 0.061

Left Supramarginal Gyrus 100.00 % 476.01 / 0.00 0.061

Precuneus 100.00 % 105.60 / 0.00 0.076

Left Sensorimotor Cortex 98.84 % 131.02 / 0.00 0.085



Left Inferior Frontal Gyrus (Orbitalis) 100.00 % 195.51 / 303.29 0.165


Left Orbital Cortex 91.86 % 119.09 / 0.00 0.175

Dorsal Anterior Cingulate Cortex 100.00 % 126.12 / 0.00 0.202

28Saturday, June 14, 14


Discrimination pattern

22Figure 7: Discrimination pattern recovered by the T-BS method after the post-processing that

removes clusters whose size is smaller than a threshold fixed with cross validation. Blue voxels

are associated to control subjects (their corresponding wd in the ensemble of SVMs was mostly

negative), while yellow voxels are associated to OCD patients (their corresponding wd in the

ensemble of SVMs was mostly positive). Each voxel’s color intensity indicates its frequency in

the 172 LOO experiments; the whitest voxels showed up as positive weights in the 172 LOO

iterations while the bluest ones showed up as negative weights in the 172 LOO iterations.

Notice how relevant voxels appear in clusters where all the elements have the same sign. On

average, the post-processing reduced the voxel set size down to 35,195, reaching a LOO CE

of 28.5%.

25

Figure 4: Discrimination pattern recovered by the T-BS method. Blue voxels are associated

to control subjects (their corresponding wd in the ensemble of SVMs was mostly negative),

while yellow voxels are associated to OCD patients (their corresponding wd in the ensemble

of SVMs was mostly positive). Each voxel’s color intensity indicates its frequency in the 172

LOO experiments; the whitest voxels showed up as positive weights in the 172 LOO iterations

while the bluest ones showed up as negative weights in the 172 LOO iterations. Notice how

relevant voxels appear in clusters where all the elements have the same sign. On average, the

T-BS selected 42, 816 voxels in each LOO iteration, reaching a CE of 26.2%.

22


Concluding remarks



Conclusions

• Multivariate region discovery based on bagging plus conformal analysis plus clustering

• Identify voxels that are relevant for the discrimination of OCD patients (26% LOO test error rate)

• The discovered clusters appear in brain regions that are known to be related to OCD

• The regions are consistent across a LOO study

24


Thanks!


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