Detecting Tourette’s Syndrome in Anatomical Regions of the Brain

through MRI Analysis and Naive Bayes Classiﬁer

Murilo Costa De Barros

1 a

, Kaue Tartarotti Nepomuceno Duarte

2 b

, Wang-Tso Lee

3 c

Chia-Jui Hsu

and Marco Antonio Garcia De Carvalho

1 d

Computing Visual Laboratory, School of Technology UNICAMP, R. Paschoal Marmo, 1888,

Jd. Nova It

alia, 13484-332, Limeira, S

ao Paulo, Brazil

Vascular Imaging Laboratory, Calgary University, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada

Department of Pediatrics, National Taiwan University Children’s Hospital, Taipei, Taiwan

Department of Pediatrics, National Taiwan University Hospital Hsinchu Branch, Taipei, Taiwan

Keywords:

Classiﬁcation, Tourette Syndrome, GLCM, Naive Bayes, Image Processing, Segmentation.

Abstract:

Tourette Syndrome (TS) is an inherited condition represented by involuntary vocal and motor movements

(tics). Nowadays, there is no available cure, only psychological treatments to inhibit it, requesting the use of

medication in rare cases. The importance of diagnosing Tourette’s in childhood enables a range of possible

treatments that would decrease the intensity of TS, and in some cases, even stop it. In most cases, the TS

diagnosis considers only clinical assessment. Analyzing the brain and its anatomical regions via imaging

data can provide relevant information in order to assist doctors. This work aims to propose an approach in

order to identify the most affected anatomical region of the brain by TS. The approach consists of three major

steps: (i) the brain is segmented in its anatomical regions; (ii) texture patterns are extracted via Gray-level

Co-occurrence Matrix for each region; ﬁnally, (iii) each brain region is evaluated using Naive Bayes classiﬁer,

determining the presence or absence of TS. We use MRI images from 68 subjects around nine years old

equally divided whether has TS or not. The regions from the limbic system were relevant in the diagnosis:

right-side accumbens reached 68% of accuracy; posterior and central parts of corpus callosum ranked in the

top four positions. Combining the top ﬁve most predictive regions led our approach to reach 78% of accuracy.

The results were signiﬁcant in detecting the most affected regions in TS and providing a reliable approach to

classify the brain regions accordingly.

1 INTRODUCTION

Tourette’s Syndrome (TS) is a genetic-pathologic dis-

order that comprises neurophysiological and neu-

roanatomical aspects in subjects, commonly devel-

oped in between two and eighteen years old, the ma-

jority in male subjects (Hounie, 2006).

TS is characterized by uncontrolled movement

disorders (motor or vocals tics); the most common

tics are frequently blinking, shaking shoulders, and

other involuntary movements. Repeated vocalization

urges may be developed, such as coughing, grunting,

whistling, and easing the throat These tics can often

https://orcid.org/0000-0003-2452-8128

https://orcid.org/0000-0002-4074-3672

https://orcid.org/0000-0003-3231-9764

https://orcid.org/0000-0002-6303-5564

be temporarily suppressed by patients who have TS,

becoming overwhelming after some time. Figure 1 il-

lustrates the most common uncontrolled facial move-

ments caused by TS. The most complex and rare tics

are related to subjects who have TS; three of them of-

ten serves as guidelines to diagnose TS:(1) coprolalia:

Vocal tics with obscene and offensive words; (2) co-

propraxia: Motor tics with an obscene or offensive

gesture; (3) echolalia: Vocal tics using words repeti-

tion or word sounds (Teixeira et al., 2011).

Kobierska (Kobierska et al., 2014) stated that co-

prophenomena, i.e., actions of coprolalia and copro-

praxia, is developed in the peak period of TS where

the tics start becoming more aggressive between 8 and

12-year-old phase, often in people with mental disor-

ders and who have behavioral problems.

TS patients may present obsessive thoughts when

they see a picture, a person, or any uncomfortable

Costa De Barros, M., Duarte, K., Lee, W., Hsu, C. and Garcia De Carvalho, M.

Detecting Tourette’s Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classiﬁer.

DOI: 10.5220/0011056800003209

In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering (IMPROVE 2022), pages 26-33

ISBN: 978-989-758-563-0; ISSN: 2795-4943

Figure 1: Common uncontrolled facial movements in a sub-

ject affected by TS.

moment that triggers high levels of anxiety and com-

pulsive behaviors, i.e., repetitive actions aiming to re-

lieve the stress caused by those obsessions. Besides,

subjects who have TS may present other disorders

(Hounie, 2006) such as Obsessive-Compulsive Disor-

der (OCD) and Attention Deﬁcit Hyperactivity Disor-

der (ADHD), posing a challenge to diagnose TS inde-

pendently (Figure 2).

Figure 2: Disorders Intersection and TS diagnosis. Source:

Adapted from (Hounie, 2006).

Medical imaging, most speciﬁcally Magnetic Res-

onance Imaging (MRI), plays a vital role in identi-

fying TS and has commonly been used in the medi-

cal ﬁeld to support doctors in their diagnosis (Muller,

2009). However, subtle and imperceptive changes

may occur in distinct anatomical brain regions, pre-

cluding TS identiﬁcation without clinical assessment.

The yearning for computational approaches to auto-

matically diagnose anatomical variation in subjects

with TS using medical imaging drives the main chal-

lenge of our work.

Nowadays, Artiﬁcial intelligence (AI) has been

adopted in different ﬁelds to improve their methods

to achieve better outcomes. Not differently, the gap

between AI and the medical ﬁeld has been narrow-

ing across the years, and it has been vastly used in

diseases such as Alzheimer’s and Parkinson’s. How-

ever, to the best of our knowledge, combining AI for

anatomical brain analysis using medical imaging in

TS is a novelty topic. The main contributions and

features of this work are: (1) computational approach

focused on TS; (2) feature-based information about

brain regions for TS; (3) application of machine learn-

ing to identify the most predictive regions; (4) ensem-

ble of the most predictive brain regions for TS. The

main goal of this work is to propose an approach to

automatically identify the most affected regions of TS

via texture feature extraction and Naive Bayes, em-

ploying only structural MRI.

The remainder of this paper is organized as fol-

lows: Section 2 shows the related work and the gaps

in the literature; the proposed approach is described

in Section 3; Section 4 details the results and the list

of regions affected by TS; ﬁnally, Section 5 presents

the summary and conclusions.

2 RELATED WORK

Medical imaging is crucial for TS diagnosis, in ad-

dition to clinical assessments, image processing, and

machine learning, and plays a vital role in interpret-

ing brain information, thus allowing the prediction of

possible outcomes for the subjects.

Muellner (Muellner et al., 2015) proposed an ap-

proach aiming to analyze the depth, opening, length

and thickness of the gray matter groove in exams Dif-

fusion Tensor Imaging (DTI). The author labeled the

areas using the BrainVisa Software, improving the

most affected regions, like the frontal, parietal and

temporal lobes. They considered 104 subjects, half

for TS and half for normal controls. In addition, adult

subjects were considered instead of children, which

differs from the most common approaches in the lit-

erature.

Peterson (Peterson et al., 2007) proposed an ap-

proach to analyzing the morphology of the amygdala

and the hippocampus in MR scans via ANALYZE 7.5

software. The authors focused on identifying the sim-

ilarity of those regions among TS subjects via Eu-

clidean distance. For that, the images were registered

to belong to the exact spatial location. They identiﬁed

a smaller amygdala in children with TS.

In Tinaz (Tinaz et al., 2015), an approach was pro-

posed to measure brain changes when patients pause

their medication and treatments. Their dataset com-

prises 26 functional MRIs, where 13 TS subjects are

aged between 18 to 46 years, and 13 normal control

Detecting Tourette’s Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classiﬁer

subjects aged between 22 to 56 years. They reported

that once the treatment had stopped, alterations in the

frontal regions were shown, also reducing the right

stratothalamic nodules.

The eagerness for works that employ machine

learning or feature extraction for TS remains in

the literature since only a few bring a computa-

tional analysis for this syndrome. However, this

type of analysis has been broadly applied in diseases

such as Alzheimer’s and Parkinson’s. Long (Long

et al., 2017) analyzed the morphological changes

for Alzheimer’s disease and its mild cognitive im-

pairment stages using MRI in healthy older peo-

ple. The 3D T1-weighted images were segmented

via FreeSurfer. The images were registered using

FSL ﬂirt to align the brain across the subjects. Next,

the Support Vector Machine (SVM) was adopted for

classiﬁcation. The ﬁndings showed 96.5% accuracy

in the gray matter detection, 91.74% in progressive

Mild Cognitive Impairment (MCI), and 88.99% in the

amygdala and hippocampus detection for stable MCI.

Jafarpour (Jafarpour et al., 2012) proposes a ro-

bust method to analyze MRI via feature extraction and

classiﬁcation. The authors address 120 MRI scans, 41

for normal control subjects and 79 for comorbidities.

The authors employed a Gray-Level Co-occurrence

Matrix (GLCM) to extract texture features, consider-

ing only a single direction θ = 0 and distance d = 1.

Finally, the descriptor outcome was clustered. They

reported an accuracy higher than 92% using classi-

ﬁers.

Solana-Lavalle (Solana-Lavalle and Rosas-

Romero, 2021) developed an approach to predict

Parkinson’s disease in MRI scans. Firstly, the brain

regions are segmented, and they are registered and

aligned across distinct subjects. A t map correspond-

ing to the difference between the labeled voxels

and the region of interest is created. They reported

signiﬁcant results, with 99.01% of accuracy in men

and 96.97% accuracy in women.

In essence, our work differs from the literature

by the following topics: (1) We provide a brain seg-

mentation via Freesurfer and feature extraction for TS

subjects; (2) We propose feature-based classiﬁcation

using Naive Bayes for TS subjects; (3) We propose

computational TS region analysis according to texture

information; (4) We analyze deﬁning the most critical

regions to detect TS automatically in childhood; (5)

We separated an ensemble of the best predictive re-

gions for TS.

3 PROPOSED APPROACH

This section presents our proposed approach to ad-

dress the problem of detecting TS in brain anatomi-

cal regions. Figure 3 illustrates the main steps of the

proposed method: (1) image acquisition; (2) volume

segmentation; (3) feature extraction; and (4) classiﬁ-

cation and majority voting. These steps will be ex-

plained in the following subsections.

1. Image Acquisition. including the deﬁnition of the

acquisition protocol;

2. Volume Segmentation. using Freesurfer segmen-

tation, dividing into distinct region groups;

3. Feature Extraction. applying GLCM per region

volume and extracting texture information;

4. Classiﬁcation and Majority Voting. detecting the

most affected regions by TS.

3.1 Image Acquisition

The image dataset was obtained by National Taiwan

University and comprises sixty-eight subjects, where

thirty-four subjects have TS, and thirty-four are nor-

mal control subjects. The age is between 6 to 14

years. Table 1 summarizes the demographic informa-

tion for the selected subjects.

The image dataset was acquired using a TrioTim

series of the SIEMENS scanner. The MRI acqui-

sition protocol deﬁned 3T T1-weighted sMRI with

3mm voxel size and 192x256 matrix size. Repetition

time (T R) equals 2000.0 and echo time (T E) equals

26.0. Each subject’s brain volume is composed of 208

slices, enabling the construction of the entire volume

to the next step.

3.2 Volume Segmentation

FreeSurfer tool was employed to segment the brain

into deep-inner gray-matter regions (Fischl et al.,

2002). This process mainly consists of three ma-

jor steps: (1) image normalization (Figure 4 (a)) fol-

lowed by skull stripping (Figure 4 (b)); (2) smooth-

ing followed by an inﬂation of the surface; and (3)

Table 1: Demographic information of the selected subjects.

Attributes

(N=34)

Age (years)

median (range)

6 to 14

8, 94

6 to 13

8, 58

Gender

% male

71% 68%

IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering

3.1. Image

Acquisition

1.2 Patient Volume Selection

3.2. Volume

Segmentation

Brain

Segmentation

Binary Matrix

Generation

region #1

region #2

region #3

region #N

Co-occurrence Matrix

Calculation

3.3. Feature

Extraction

3.4. Classification

And Majority Voting

Naïve Bayes

Classification

1.1 CSV filtering

Best Region

Majority Voting

region #1

Data

Acquisition

Demographic

Analysis

name

mri

001

001.d

002

002.d

Texture Descriptor

Extraction

region #2

Results

region #3

region #1 region #2

region #3

region #N

Figure 3: Proposed workﬂow.

registration and cortical parcellation. Among the ex-

isting atlases in Freesurfer, we adopted the Desikan-

Killiany-Tourville (DKT) atlas (Figura 4.c). We re-

stricted the Freesurfer outcome to 30 internal gray-

matter regions.

After the segmentation process, each anatomical

region is represented by an unique region ID, thus al-

lowing further comparisons across distinct subjects.

Each region is separated into a three-dimensional bi-

nary volume, where the voxels are assigned to “True”

when they belong to the assigned region and “False”

otherwise. Thus, each subject has a set of thirty binary

volumes. Figure 4 illustrates the DKT atlas segmen-

tation applied to a particular brain volume.

(a) (b) (c)

Figure 4: Pre-processing using FreeSurfer: (a) Original im-

age normalized; (b) Skull stripping; (c) DKT-atlas.

3.3 Feature Extraction

This step is concerned to extract reliable texture fea-

tures using GLCM studied by (Haralick et al., 1973)

and also using radiomics according to (van Griethuy-

sen et al., 2017). The GLCM stores the pixel/voxel

afﬁnity between a voxel value v

to v

(Figure 5). The

resultant matrix is N ×N, where N corresponds to the

maximum voxel value among the values in an image

(Figure 6).

The grayscale value represents the indexes in the

rows and columns, and the interaction f (v

, v

) is the

number of occurrences from v

towards v

Figure 5: 3D volume where each cube corresponds to a

gray-tone intensity voxel.

1 2 0 1

2 1 3 1

2 1 0 2

0 1 0 1

Figure 6: Matrix Co-occurrence.

The transitions are calculated majorly according

to two parameters: (1) θ angle (0º, 45º, 90º,145º); and

2) distance d = 1. Angle corresponds to the orienta-

tion from v

and v

stored in the GLCM. Distance is

deﬁned as the interval in pixels or voxels between v

and v

Two adaptations were adopted to GLCM: (1) since

there are 3D structures (26-connectivity) instead of

2D (8-connectivity), we add more directions to calcu-

late the displacement vector (dx, dy, dz); (2) we refor-

mulated to rely on anatomical regions instead of the

brain, disregarding occurrences of voxels that do not

belong to a given region. In general, each anatomical

region has a distinct GLCM.

In the end, to compose the ﬁnal vector con-

taining the information of each region per subject,

twenty-three metrics to GLCM-based texture descrip-

tors were extracted:

Detecting Tourette’s Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classiﬁer

Autocorrelation; Joint Average; Cluster Promi-

nence; Cluster Shade; Cluster Tendency; Contrast;

Correlation; Difference Average; Difference Entropy;

Difference Variance; Joint Energy; Joint Entropy;

Informational Measure of Correlation 1 and 2; In-

verse Difference Moment; Maximal Correlation Co-

efﬁcient; Inverse Difference Moment Normalized; In-

verse Difference; Inverse Difference Normalized; In-

verse Variance; Maximum Probability; Sum Average;

Sum Entropy; Sum of Squares (pyradiomics commu-

nity, 2016)

3.4 Classiﬁcation and Majority Voting

Classiﬁcation. Naive Bayes (NB) is a probabilis-

tic classiﬁer based on the Bayes theorem (Zhang

and Gao, 2011), part of the supervised learning

branch of machine learning algorithms. This clas-

siﬁer independently analyzes attributes to identify

its afﬁnity to a given class. Considering a data

vector x = (x

, x

...x

) and a set of class w =

, w

...w

), thus the probability of each class

belong to a vector is represented by the expression

of p(w

|x). Equation 1 states the calculation of NB

(Pedrini and Schwartz, 2008).

P(w

|X) =

P(X|w

) · P(w

)

P(X)

. (1)

where P(w

|X) is the posterior probability; P(w

) rep-

resents a given original class probability, P(X|w

) cor-

responds to how likely a vector x is with the class w

Therefore, this classiﬁcation has as its decision factor

two probabilities, being represented below Eq. 2.

P(w

|X) > P(w

|X). (2)

Where j = (1, 2, 3...n) with i 6= j

The version we applied uses the Gaussian param-

eter (Equation 3).

P(x

|y) =

2πσ

exp

−

− µ

)

2σ

(3)

where µ is the average value of a given class, and

σ represents the covariance matrix of the class. We

decided to use this classiﬁer, as it has a good multi-

class probabilistic performance, in addition to recom-

mended when the amount of data is not large.

Majority Voting. individual region outcomes are com-

bined into a single prediction using an election of

those values. The prediction is chosen according to

the most occurrent class identiﬁed in the predictions

for a given subject.

3.5 Computational Aspects

Our implementation was entirely developed in Python

3.8 using the Spyder IDE, in macOS High Sierra ver-

sion 10.13.6. Our method was implemented using the

following tools and techniques:

1. Data Acquisition. the data were stored into fold-

ers labeled id class, id is the subjects id and varies

between 1 and 34, and class is labeled M for TS

subjects and H otherwise;

2. Volume Segmentation. bash ﬁles were produced

to apply the Freesurfer tool using the command

recon − all automatically. Then, a code was used

to group the intersection between regions across

the subjects, remaining only the subjects’ regions;

3. Feature Extraction. a code was developed to ex-

tract the GLCM features using the library pyRa-

diomics, NumPy and Pandas;

4. Classiﬁcation and Majority Voting. the classiﬁ-

cation was performed using a code developed in

python using the Naive Bayes contained in the li-

brary scikit-learn. The majority voting was en-

tirely developed using NumPy and Pandas.

All source codes used in the implementation, in addi-

tion to the design of the experiment and the graphic

are available publicly in our Github directory

4 RESULTS AND DISCUSSION

In this section, two aspects were taken into account:

(1) effectiveness of our approach; (2) region analysis

compared with the state-of-the-art.

Four metrics were employed to evaluate our re-

sults: (1) recall - R; (2) precision - P; (3) f-measure -

F; (4) accuracy - Acc. They are calculated by combin-

ing the true-positive (TP), true-negative (TN), false-

positive (FP), false-negative (FN) values. The equa-

tions are deﬁned as follows:

R =

T P

T P + FP

(4)

P =

T P

T P + FN

(5)

F =

2 ∗ R ∗ P

R + P

(6)

Acc =

T P + T N

T P + T N + FP + FN

(7)

https://github.com/muribarros/TS Feature Extractor.

git

IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering

The NB classiﬁes the outcome for each one of the

thirty chosen brain anatomical regions, returning the

values of the metrics related how predictive the region

was to infer whether a subject has TS or not. Figure

7 shows the accuracy value of thirty anatomical re-

gions, where some nomenclature should be deﬁned

as: Corpus Callosum (CC) and Cerebrospinal Fluid

(CSF). Among them, sixteen regions ranked greater

than 60% of accuracy.

The most predictive region, according to the re-

sults, was the right-side accumbens (Acc:68%); this

region is considered as the neural interface between

action and motivation located in the basal ganglia,

and it is the main component of the ventral striatum

and plays an essential role in the understanding of

neuroanatomical aspects of TS (Brito, 1997; Volman

et al., 2013).

There is a signiﬁcant inﬂuence on the right hemi-

sphere of the brain, which is related to creative think-

ing when compared to the left hemisphere, related to

analytical thinking. Studies in the literature address

the importance of the amygdala, ranked in 9th posi-

tion in TS due to belonging to the limbic system and

how it is a reliable region to identify a subject who

has TS (Neuner et al., 2010).

According to Caminiti (Caminiti et al., 2009), the

Corpus Callosum (CC) is the primary means of com-

munication between the brain’s hemispheres, con-

necting cortical regions. The region has the follow-

ing connections, depending on the analyzed area: (1)

CC anterior, connecting with Parietal areas; (2) CC

central, connecting with the motor, sensory and vi-

sual areas; (3) CC posterior, connecting with the pre-

frontal cortex. CC Central, CC mid-posterior, and

CC posterior have achieved the second, third, and

fourth positions in the top 5 most predictive brain re-

gions for TS. This outcome goes hand in hand with

the importancestate-of-the-art, which declares the im-

portance of CC related to motor tics (Muellner et al.,

2015; Steeves et al., 2010; Greene et al., 2016a).

Although analyzing single region is crucial to in-

fer its variation in subject with TS, it is vital to assume

a better prediction by combining the most predictive

regions outcome. Table 2 and 3 deﬁnes the effective-

ness of the top 5 most predictive regions. In addi-

tion, it is also shown that combining the anatomical

regions’ outcomes is more predictive to identify TS

than the outcomes analyzed separately.

In the state of the art, no works were found that

use the Naive Bayes classiﬁcation. However, there

are studies such as the one by (Greene et al., 2016b)

that uses the Support Vector Machine (SVM) classiﬁ-

cation in MR images. In addition to being compet-

itive, both works are complementary, demonstrating

Table 2: Effectiveness of the top ﬁve brain regions to detect

TS.

Anatomical Region Acc P R F

I) Right Accumbens area 68% 61% 97% 75%

II) CC Central 66% 64% 70% 67%

III) CC Mid Posterior 66% 66% 68% 67%

IV) CC Posterior 66% 74% 50% 60%

V) Right choroid plexus 64% 62% 73% 67%

Table 3: Ensembles of the top regions, were top 3 ﬁrst re-

gion and top 5 ﬁrst region.

Ensembles Acc P R F

Top 3 regions73% 67% 91%77%

Top 5 regions78%72%91%80%

views of different modalities and classiﬁcation char-

acteristics.

The table 4 below illustrates the summary of the

comparison of both works.

Table 4: Paper comparison.

(Greene et al., 2016b)

Proposed

method

Modality RMf RMs

Nº Image 84 68

Nº Anatomical

Region

264 86

Atlas anatomical

regions

Average fMR

scans

Freesurfer

Feature

Extraction

n/a GLCM

Classiﬁcation SVM NB

Acc fMR

70% n/a

Acc sMR

n/a 68%

Acc Ensemble

Top 3 regions

sMR

n/a 73%

Acc Ensemble

Top 5 regions

sMR

n/a 78%

5 SUMMARY AND CONCLUSION

In this work, we have presented an approach to iden-

tify TS based on the combination of brain segmen-

tation, feature extraction, classiﬁcation, and ensem-

ble. Our proposed workﬂow has four major steps: (1)

we develop an approach image acquisition, which is

consisted in acquiring the sMRI and study the demo-

graphic information of the subjects; (2) we develop an

approach volume segmentation, which is composed

of the segmenting the brain in anatomical regions; (3)

Detecting Tourette’s Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classiﬁer

Figure 7: Best accuracies among the regions to detect TS ranked from the lowest (left) to highest (right).

we develop an approach feature extraction, responsi-

ble for identifying texture patterns in the regions via

GLCM; (4) we develop an approach classiﬁcation and

majority voting, which classify the patients according

to their regional texture patterns, combine the most

signiﬁcant regions into a single detection.

Tourette’s syndrome is a disorder that affects mo-

tor and vocal capabilities, commonly developed in

childhood. Although there is no cure, treatments

are often prescribed through behavioral analysis us-

ing clinical assessment. However, this analysis may

not consider imaging, which gathers reliable informa-

tion about the subject’s brain. Moreover, there are few

works that use computational approaches thus turning

into a gap to be addressed.

Our contributions in this work were: (1) proposing

an approach focused on computational analysis using

imaging; (2) extracting feature focusing on anatom-

ical regions; (3) presenting a rank of the most pre-

dictive regions using Naive Bayes; (4) analyzing an

ensemble to detect TS using a group of regions. Fea-

ture extraction and classiﬁcation are being extensively

studied in Parkinson’s and Alzheimer’s disease; how-

ever, those topics are not widespread in TS.

The limbic system has shown an essential part for

analyzing TS since the top regions selected using our

approach that reached higher accuracy belong to this

system. The importance of the limbic system is also

addressed in the literature but not via a computational

approach. Nevertheless, the ensemble (i.e., combi-

nation) of the most predictive regions’ outcome has

increased the detection, thus highlighting the need for

detecting TS based on multiple regions outcomes. For

future work, we suggest the adoption of possible fea-

tures, described as follows:

1. The use of new modalities, such as functional

MRI and Positron Emission Tomography scans;

2. The use of additional anatomical regions, consid-

ering the white matter and cortical areas;

3. The use of 3D shape and texture descriptors,

which may lead to a new interpretation of the re-

gions;

4. Classiﬁcation using other techniques, such as

SVM, Neural Networks, and Random Forest;

ACKNOWLEDGEMENTS

The authors thank the group Department of Pedi-

atric Neurology from National Taiwan University, lo-

cated in Taipei-Taiwan, for providing T1-weighted

MR scans used in this work. This study was ﬁnanced

in part by the Coordenac¸

ao de Aperfeic¸oamento de

Pessoal de N

ıvel Superior – Brasil (CAPES) – Fi-

nance Code 001.

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