DEIM Forum 2016 G7-1

Predicting between-subject variability of brain activities
based on brain structure
Xiao XIANG† , Yusuke YAMAMOTO†† , Makoto P. KATO† , and Katsumi TANAKA†† Graduate School of Informatics, Kyoto University†† Graduate School of Management, Kyoto University
Yoshida Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan
E-mail: †{kou,kato,tanaka}@dl.kuis.kyoto-u.ac.jp, ††yamamoto@gsm.kyoto-u.ac.jp
Abstract We present an approach to estimate task-evoked fMRI data based on structural MRI in order to reveal
in which level individual diﬀerences of brain activities can be explained by brain structure. We extract regions ofinterest and conducted voxel-based morphometry analysis on both fMRI and structural MRI data, and apply asupervised machine learning approach to capture the correspondence between them. Experiments were conductedwith fMRI data for gambling and emotion processing tasks, which tend to show remarkable diﬀerence between
individuals.
Key words neuroimage, data mining, brain

1. Introduction

subject variability has not been analyzed in depth. However,the between-subject variability may imply the individual dif-

With the development of neuroimaging techniques [1], es-

ferences of mental processes for the same stimulus. In a study

pecially magnetic resonance imaging(MRI) and functional

using fMRI, Mariko Osaka et al. selected two labeled groups,

magnetic resonance imaging(fMRI), researchers have been

one has high score in working memory capacity while anotherable to observe both brain structure and brain neurobiolog-

is lower, as experiment subjects to record their brain activ-

ical activity in higher temporal and spatial resolution. MRI

ities [5]. The results indicate that the attention controllingscanners play strong magnetic fields on brain in order tosystem is more eﬀective in higher scored group compared todetect diﬀerent intensity of resonant electromagnetic waves

that of lower group. Prediction the between-subject variabil-radiated by hydrogen atom so that tissues can be distin-

ity for a certain stimulus is useful to realize highly person-

guished by their various water content and whole brain im-

alized services such as advertisements and medical consulta-

age is generated [2]. The fMRI concept builds on the earliertion.

MRI scanning technology and the discovery of properties ofIn this paper, we tackle the problem of predicting individ-

oxygen-rich blood, which is property shows brain activities

ual brain activities based on brain structure data. To spec-are closely linked to blood flow and blood oxygenation in theify the scope of the problem, we employ task-evoked fMRI

brain. Similarly, fMRI scanners detect diﬀerent intensity ofdata as record of brain activities during certain tasks andresonant electromagnetic waves radiated by hemoglobins in

structural MRI data as brain structure data of subjects. In

various reduction level to find active regions of brain, whichview of greater granularity and more explainable, we extract

are consuming more oxygencan [3].

dozens of region of interest(ROI) from both MRI data andOrdinary analysis in fMRI usually lay emphasis on com-

fMRI data. By applying Voxel-based morphometry analy-

mon correspondences between brain functions/behaviors andsis, we compress MRI data into first-order vector. Finally,

neurobiological brain activities of subjects. For instance,

based on MRI vector, we predict possible values of each ROI

Fossati et al. carried out an experiment [4], in which tenof the same subject to one stimulus, using support-vector

healthy subjects were presented with words describing posi-

machine(SVM).

tive and negative personality traits during fMRI scanning.

We present our experiment on WU-Minn HCP [6] dataset,

The results provide evidence that self-reference is mainly

which includes 500 subjects’ MRI and task-evoked fMRI

contributed by right dorsomedial prefrontal cortex. How-

data. We focus on gamble and emotion processing task andever, most of the existing fMRI experiments explored brain

give prediction of every stimulus to each subject and estimate

activities common for all participates, while their between-mean accuracy.

In this paper, Section 2 introduces several representativeby utilizing the grey matter density in specific brain regions.

studies on task-evoked fMRI and studies combining neu-

In a VBM study on personality, Hu et al. [18] investigated theroimaging and informatics techniques. Section 3 describes

correlation between the five-factor model (FFM) personality

the general model in which we predict fMRI data based ontraits and brain structure. Zhou et al. [19] showed neurobi-

MRI data. We explains procedure and result of our exper-

ological evidences on brain matter abnormalities in an inter-

iment in Section 4. Finally, conclusions was drawn in last

net addiction group. A study focusing on gender diﬀerence

section.

in online trust shows that male and female subjects activate

2. Related Workgender-independent brain regions when exposed to eBay offers, which indicates that the brain regions that encode trust-Classic studies in the field of task-evoked fMRI are usu-

worthiness diﬀer between women and men [20]. These papers

ally in a form of testing hypotheses through observing brain

tried to demonstrate that between-subject diﬀerences can be

responses to a certain stimulus. In an reward-related fMRI

used as a source of information to link human behaviours andexperiment [7] conducted by Itzhak et al., researchers showedcognition to the brain anatomy. Kanai and Rees [21] pro-discrete categories of beautiful faces as stimuli to human sub-vided a meta-analysis on this topic. However, most of thejects and scanned active brain regions using fMRI scanner.

studies in this field have ignored the intermediate section in

The results suggest that passive viewing of beautiful female

the ternary relation of the brain structure, brain activities,

faces activates reward circuitry(a bunch of brain regions re-personality. In other words, the correlation between brain

lated to certain brain function) and the circuitry does not

structure and brain activities have not extensively analyzed

include aesthetic assessment in all subjects. In a study con-in the existing work.

ducted by Naomi et al. [8], researchers tried to find if brain

An attempt to explore the relationship between brain

bases of social pains are similar to those of physical pain bystructure and activities has been initiated by Yi et al. [22],checking if the same regions in subjects’ brains are activated

who observed both structural and functional variability using

to social and physical pain stimuli. In addition, Joshua et

VBM analysis and analysis on default mode network fMRI

al. [9] applied two fMRI experiments using moral dilemmas

data. Whereas, the relationship between the brain structure

as probes and argued that in moral judgment, emotional

and task-evoked fMRI data has not been explored and is theprocessing also plays an important role. Another stream ofmain interest of this paper.

research lays emphasis on the connectivity between brain
regions. For instance, Qin et al. [10]demonstrated a brain

3. Methodology

network implicated in both pain sensation and pain modula-

3. 1 Problem Definition

tion, by performing statistical covariance analysis. Jonas et

Structural MRI and functional MRI data is usually com-

al. [11] presented an approach based on polythetic decision

posed of million of voxels, each of which indicates the in-trees to find and classify frequency subbands in the brain.

tensity of the electromagnetic wave at a certain location ofSmith gives a review on area of fMRI connectivity. [12] Af-

the brain (and at a certain time point in the case of func-

ter voxel-based morphometry(VBM) [13] analysis was pro-tional MRI data).

posed, a number of studies on the correlation between brain

particular subject are represented by a third-order tensor

structure and human personality or human behavior patterns

X ∈ RW ×H×D where W , H, and D are the width, height,

have been published in the literature. Good et al. [14] uti-

and depth of a MRI scan. In a similar way, functional MRI

lized VBM to examine the eﬀects of age on brain matters indata for a particular subject are represented by a forth-order

465 normal adults. Baron et al. [15] conducted an analysis

tensor Y ∈ RW ×H×D×T where T is the total number of timeon the gray matter density by means of VBM on both pa-

points. The problem of predicting function MRI data based

tients with Alzheimer’s disease and healthy subjects, finding

on structural MRI data X can be formalized as follows:that significant clusters of gray matter were lost in the patient group. Cadenhead et al. [16] showed the magnitude of

Therefore, structural MRI data for aŶ = f (X)(1)

placebo analgesia is related to the gray matter density in sev-

where Ŷ (∈ RW ×H×D×T ) is predicted functional MRI data,

eral brain regions. Studies centered on remarkable between-

and f is a function such that f : RW ×H×D → RW ×H×D×T .

subject diﬀerences, such as anatomical variability between

If we can find a function that achieves reasonably small loss

patients and healthy subjects. Recently, some researchers

between the predicted and observed functional MRI data,

have tackled prediction of subjects’ personality or behavior

we can argue that there is strong correlation between brain

patterns based on the brain structure. Kanai et al. [17] pre-structure and activities.

dicted the number of friends on a social networking service

Although our conceptual problem definition was presented

above, it is infeasible to solve such a regression problem withand its neighbors, therefore, comparison can be done acrosshigh dimensional input and output. Thus, we introduce asame ROI from diﬀerent subjects at every voxel. VBM anal-

simplified version of the regression problem below. Suppose

ysis can be summarized succinctly as the following 4 steps:that functions Φs and Φf can extract some features from•

Segment MRI image, to identify grey matter (GM),

structural MRI data X and functional MRI data Y, andwhite matter (WM) and cerebrospinal fluid (CSF) using

output Ks - and Kf -dimensional vectors, respectively. Then,

Gaussian mixture model prior distribution.

the problem described in Equation (1) can be simplified as

types of brain matter, three segmented images for each raw

follows:

image are generated.
•ŷ = f (Φs (X))(2)

where f is a function defined as f : R

Ks

→ R

Kf

, and ŷis prediction for Φf (Y), a vector for function MRI data Y.

For diﬀerent

Estimate the segmentation by align the segmented im-ages together and iteratively registering the raw image with
their average•

Generate spatially normalized and smoothed JacobianThus, our purpose is to find a function f that minimizes thescaled segmented images, using the segmentation estimateddiﬀerence between ŷ and Φf (Y), which can be measured byin the previous stepa loss function l : RKf × RKf → R. Here, Φf and Φs can
be ROI extraction. Each ROI represents a structural related•

Compute GM, WM, CSF volume of each ROI by cal-culating the voxels in segmented images.

tissues having similar function, which can be helpful to in-

Finally, we aﬃne all these volumes into first-order vector

terpret the experiment results later. For example, when we

as a structural feature vector to one subject. Let xi be thefind subjects have much grey matters in ROI A and theircalculated volume from Xi data then the VBM and aﬃning

ROI B are likely to be activated by a certain stimulus, we

procedures can be expressed as follows:

can provide more convincing explanation based on anatom-xi =ically or functionally relationship, which have been revealed
by previous neurobiology works, between ROI A and ROI B.
3. 2 Extracting ROI
We have two types of data, structural data from MRI scanning and functional data from fMRI scanning. Extracting
ROIs and performing volume calculating in case of structural

∑

volV

(5)

V∈Xi

where volV is the volume of a voxel in the ith ROI. Naturally,
the whole procedure Φs can be expressed in one formulation:
xi =

∑

volV

(6)

V∈X·Midata will be discussed in Section 3.2.1. Extracting ROI fromand with k disjoint ROIs, we obtain k-dimenstions vector Xj

functional data, which involves two extracting methods, will

for jth subject:

be discussed in Section 3.2.2.
We apply previously prepared masks on MRI image of eachsubjects basing on prior probability maps, in order to distinguish each brain structure, such as hippocampus, subcortical
nuclei or inferior frontal gyrus. This preprocessing can be expressed as:

Xj = (x1 , x2 , ...xk )(7)

3. 2. 1 Extracting ROI from functional data
In case of functional data, we extract ROIs firstly by clustering the relatively more active regions in order to drop
other regions with low information value. This clustering

Xi = X · Mi

(3)

where X is structural data. Mi is the ith ROI mask which
is a third-order binary tensor, the value of voxels in the region we want to extract is 1 while the left is 0. · is a tensor
product defined as following:

Yj = Y · Mf j

(8)where Y is functional data and Mf j is the jth functional
ROI mask computed by clustering. The disadvantages arethat we have had to use a whole run of data to define the ROI

A · B = (aijk · bijk )1<i<m,1<j <n,1<k<o= =procedure can be simply represented as follows:

= =

= =

(4)

and that functional ROIs can be noisy, when the activation
signal is not strong. An alternative is to use the anatomy ofwhere A, B ∈ R{m×n×o} . After ROI extraction, volume of

the brain to estimate the location of functional areas, which

all ROIs in subjects’ brain can be obtained by doing VBM

has been introduced in section 3.2.1. Let Y′ be the bothanalysis on preprocessed MRI image. VBM analysis excelled

functional and structural extracted ROI tensor:

traditional method by registering brain to a template, whichgets rid of remarkable diﬀerence in brain anatomy among
subjects. Hence, each voxel represents the average of itself

Y ′ = Y · Mf · Ms

(9)where Mf is functional ROI mask and Ms is structural ROI

mask. Further, data is remain fourth-order tensor due totime-series information after extraction. Thus, we perform
dimension reduction on the data by taking the mean of theelectromagnetic intensity of voxels in ROI Y ′ . Let yt′ be theTable 1: Structural session

average intensity of a ROI to a certain stimulus:
y = avg(∑

Type

intV )

TR(ms) TE(ms) TI(ms) Voxel Size Acquisition(10)

DescriptionV∈Y ′Time (min:sec)′

where intV is the intensity of the a voxel in ROI Y and

T1w

2400

2.14

1000

0.7 mm 7:40
isotropic

avg takes the average of intensities of all voxel in this ROI.
Finally, we obtain one value for one ROI we extracted to

T2w

3200

565

0.7 mm 8:24
isotropic

a certain stimulus, and with k disjoint ROIs, we obtain kdimensions vector Yj for jth subject:
Yj = (y1 , y2 , ...yk )(11)

We combine Xj and Yj as a data point of collection D, which
can be expressed as D = {(X1 , Y1 )...(Xj , Yj )...(Xn , Yn )}for n is the size of samples.
3. 3 Prediction

Table 2: Task-evoked fMRI

Selection for classifier depends on what we actually wantto predict. In section 3.2.2, we reduct raw data to scalars

Task

Runs Frames per run Run Duration

without category, therefore, it becomes a regression problem.

Gambling

2

253

3:12

Support Vector Machine (SVM), as an adaptable supervised

Language

2

316

3:57

learning model in machine learning, is widely used in regres-Social Cognition

2

274

3:27

sion. In a study on testing performance of 179 classifiers [23],Emotion Processing

2

176

2:16

results also show that SVM and random forest (RF) are most
likely to be the best. We choose SVM as our estimator to
make prediction and examine the accuracy. We map thecollection D into k subsets for k ROIs and for ith ROI, wehave Di = {(X1 , y1,i ), (X2 , y2,i )...(Xn , yn,i )}, where yj,i is yof ith ROI of jth subject. For each ROI we train a estimatorfi :Rk → R. Finally we can combine all the estimators into
one as a global estimator for all k ROIs, in other word, wholebrain.

4. Experiment
4. 1 Experiment Introdcution
In WU-Minn HCP dataset, there are massive data including personality, family background, behavior data, MR scanning data of 500 subjects. We mainly deal with the MRI data
and task-evoked fMRI data. Table 1 provides basic parameters for the structural MRI session and Table 2 provides alist of the fMRI scans we use in our experiment.
The gambling task is adapted from one developed by Delgado et al. [24]. Subjects play a number guessing game while
being scanned. In this game, subjects will win or lose money
according to their answer by button-click for indicating if
they think the number on the mystery card is bigger orsmaller than 5 (ranging from 1-9). After each guess, they
will get positive/negative/neutral feedbacks on screen. The

Figure 1: Pred score of all 48 areas in Language task: areas
with red bars are relatively predicableexperiment is block-designed in order to distinguish each response of subjects’ brain to stimulus. In another experiment
adapted from one developed by hariri et al. [25], subjects
are presented with blocks of trials that either ask them todecide which of two faces presented on the bottom of thescreen match the face at the top of the screen, or which of
two shapes presented at the bottom of the screen match theshape at the top of the screen. The faces have an angry or
fearful expression. There are 7 fMRI scans for 7 diﬀerent
types of tasks in HCP dataset. We pick up 4 tasks and theirfMRI data from all 7 tasks for the reason that we consider
that in these two tasks probably reflect more inter-individual
variety and show diﬀerences in thinking patterns not knowledge or experience.
MRI and 4 types of fMRI dataset of 100 unrelated subjects
were used. We selected Harvard-Oxford Atlas (Figure 1) as

Figure 3: Pred score of all 48 areas in Emotion task: areas with
red bars are relatively predicablethe mask to extract 48 ROIs.

Figure 2: Harvard-Oxford Atlas: divide whole brain into 48 disjoint ROIs

To evaluate the predictability of each ROIs, we give a defFigure 4: Pred score of all 48 areas in Social cognition task:

inition of predicable score as following:∑n−p
P red scorei =∑p2
1
j=1 (yj,i − p
k=1 yk,i )
−1
∑n−p
2
j=1 (yj,i − fi (Xj ))areas with red bars are relatively predicable(12)

where n is the number of subjects, p is the size of training set, n − p is the size of test size. The numerator ofthis formulation shows the diﬀerences between average predictions and observed values while the denominator is thediﬀerences between our values predicted by our model andthe observed values. Hence, one ROI is more predictable
when its pred score is larger. Especially, the ROI whose
pred score is lower than zero, is hardly able to be predicted.
4. 2 Predicable Areas
Figure 1, 3, 4, 5 show pred score of all 48 regions of all 4tasks.
We find not all ROIs are easy to predict, which is explicable since only a few ROIs take part in one certain task. In
Table 3, we list 3 ROIs, whose pred score is highest, for eachof the 4 tasks.

Figure 5: Pred score of all 48 areas in Gambling task: areas
with red bars are relatively predicableTable 3: Top3 predicable ROIs: areas with BOLD font are unique areas that are not so predicable in other tasks
Task

Top1

Top2

Top3

Planum Temporale

Postcentral Gyrus

Superior Parietal Lobule

Lateral Occipital Cortex, inferior division Planum Temporale

Central Opercular Cortex

Gambling
LanguageSocial Cognition

Planum Temporale

Frontal Pole

Temporal Occipital Fusiform Cortex

Emotion Processing

Planum Temporale

Frontal Pole

Postcentral Gyrus

According to the results, we find there is a correlation be-

SVM were better than average predictions in some ROIs,

tween the predictable ROIs and the type of task. In all fourwhich indicates there are correspondence between structuretasks, we found there are some common areas that are more

and activity in some certain areas.

predictable, such as Frontal Pole, which is considered be involved with working memory [26], Planum Temporale, one of

5. Conclusion

the most important functional areas for language [27], Post-

In this paper, we discussed an approach to predict

central gyrus, etc. All these common areas are involved with

between-subject variability of brain activities and conducted

some behaviors taken in these kinds of task, because subjects

the experiment on a open dataset to observe the prediction

certainly watch the monitor, use their working memory, or

accuracy. We find that there is, assuredly, correspondence

read some texts or pictures when being scanned by func-

between brain structure and brain activities. The volume

tional MRI machine. However, what is more telling is thatof some areas of subject brain interferes with the activation

those diﬀerent predicable areas with highest scores in eachlevel of some areas during certain task. There are some lim-task. For instance, only in language task, inferior divisionitations in this paper. Firstly, we were not able to discussof Lateral Occipital Cortex are easily to be predicted, which

why most contributive features from input data are fixed.

may contributed to the large number of text volume in the

Besides, we didn’t deal with the correlations between ROIs,

task. Another evidence is that Temporal Occipital Fusiform

anatomically or functionally, as our input data, which is also

Cortex, which plays a important role in face and body recog-

important as tissues are usually activated simultaneously.

nition [28], is highly predicable only in social cognition task,

We may discuss these topics in the future.

in which subjects were showed with shapes that may suggest
human faces to them. To conclude, there are some diﬀerent

6. Acknowledgement

predicable areas according to various stimuli included in var-

This work was supported in part by the following projects:

ious task, which may infer that functions of those predicable

Grants-in-Aid for Scientific Research (Nos. 15H01718, andareas are related with the given stimuli.

26700009) from MEXT of Japan, and ImPACT program (Ac-

4. 3 Contributive Areastualize Energetic Life by Creating Brain Information Indus-

Another viewpoint of the results is to inspect these features

tries) from CAO of Japan.

we used to regress. Features are selected based on univariate
linear regression tests and the weights of selected featuresrefer to F values. The most contributive features, which
are actually the subjects’ brain matter volumes of ROIs, are

References

found varied from ROI to ROI they contribute to. For ex-[1] Aaron G Filler. The history, development and impact
of computed imaging in neurological diagnosis and neurosurgery: Ct, mri, and dti. Nature Precedings, 7(1):1–69,
2009.
[2] Donald W McRobbie, Elizabeth A Moore, Martin J Graves,and Martin R Prince. MRI from Picture to Proton. Cambridge university press, 2006.
[3] Scott A Huettel, Allen W Song, and Gregory McCarthy.
Functional magnetic resonance imaging, volume 1. Sinauer
Associates Sunderland, 2004.
[4] Philippe Fossati, Stephanie J Hevenor, Simon J Graham,
Cheryl Grady, Michelle L Keightley, Fergus Craik, and Helen Mayberg. In search of the emotional self: an fmri study
using positive and negative emotional words. American
Journal of Psychiatry, 2014.
[5] Mariko Osaka, Naoyuki Osaka, Hirohito Kondo, Masanao
Morishita, Hidenao Fukuyama, Toshihiko Aso, and Hiroshi

ample, in gambling task, we list top 3 contributive areas tothe top 3 predicable areas, in figure 6.
We may say their is structurally correlation between themost contributive areas and predicable areas they contribute
to. We find all contributive areas are concentrated in a relatively narrow part of brain and the predicable area they
contribute is surrounded by them. However, we have no evidence to confirm if predicable areas and their contributive
areas are functionally interrelated due to lack of white matter fiber tracts information, even though some of them are
structurally adjacent.
The experiments results shows that our predictions with

Figure 6: Predictable Areas and their top 3 contributive areas

[6]

[7]

[8]

[9]

[10]

[11]

[12]
[13]

[14]

Shibasaki. The neural basis of individual diﬀerences inworking memory capacity: an fmri study. NeuroImage,18(3):789–797, 2003.
David C Van Essen, Stephen M Smith, Deanna M Barch,
Timothy EJ Behrens, Essa Yacoub, Kamil Ugurbil, WUMinn HCP Consortium, et al. The wu-minn human connectome project: an overview. Neuroimage, 80:62–79, 2013.
Itzhak Aharon, Nancy Etcoﬀ, Dan Ariely, Christopher F
Chabris, Ethan O’Connor, and Hans C Breiter. Beautiful
faces have variable reward value: fmri and behavioral evidence. Neuron, 32(3):537–551, 2001.
Naomi I Eisenberger, Matthew D Lieberman, and Kipling D
Williams. Does rejection hurt? an fmri study of social exclusion. Science, 302(5643):290–292, 2003.
Joshua D Greene, R Brian Sommerville, Leigh E Nystrom,John M Darley, and Jonathan D Cohen. An fmri investigation of emotional engagement in moral judgment. Science,
293(5537):2105–2108, 2001.
Wei Qin, Jie Tian, Lijun Bai, Xiaohong Pan, Lin Yang,
Peng Chen, Jianping Dai, Lin Ai, Baixiao Zhao, Qiyong
Gong, et al. Fmri connectivity analysis of acupuncture effects on an amygdala-associated brain network. Mol Pain,
4(55):1–17, 2008.
Jonas Richiardi, Hamdi Eryilmaz, Sophie Schwartz, Patrik Vuilleumier, and Dimitri Van De Ville. Decoding
brain states from fmri connectivity graphs. Neuroimage,56(2):616–626, 2011.
Stephen M Smith. The future of fmri connectivity. Neuroimage, 62(2):1257–1266, 2012.
John Ashburner and Karl J Friston.
Voxel-based
morphometry-the methods. Neuroimage, 11(6):805–821,
2000.
Catriona D Good, Ingrid S Johnsrude, John Ashburner,
Richard NA Henson, KJ Fristen, and Richard SJ Frackowiak. A voxel-based morphometric study of ageing in 465
normal adult human brains. In Biomedical Imaging, 2002.

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

5th IEEE EMBS International Summer School on, pages
16–pp. IEEE, 2002.
JC Baron, G Chetelat, B Desgranges, G Perchey, B Landeau, V De La Sayette, and F Eustache. In vivo mappingof gray matter loss with voxel-based morphometry in mild
alzheimer’s disease. Neuroimage, 14(2):298–309, 2001.
Petra Schweinhardt, David A Seminowicz, Erik Jaeger,
Gary H Duncan, and M Catherine Bushnell. The anatomyof the mesolimbic reward system: a link between personality and the placebo analgesic response. The Journal ofneuroscience, 29(15):4882–4887, 2009.
R Kanai, B Bahrami, R Roylance, and G Rees. Online social
network size is reflected in human brain structure. Proceedings of the Royal Society of London B: Biological Sciences,279(1732):1327–1334, 2012.
Xiaochen Hu, Michael Erb, Hermann Ackermann, Jason A
Martin, Wolfgang Grodd, and Susanne M Reiterer. Voxelbased morphometry studies of personality: issue of statistical model specification-eﬀect of nuisance covariates. Neuroimage, 54(3):1994–2005, 2011.
Yan Zhou, Fu-chun Lin, Ya-song Du, Zhi-min Zhao, JianRong Xu, Hao Lei, et al. Gray matter abnormalities ininternet addiction: a voxel-based morphometry study. European journal of radiology, 79(1):92–95, 2011.
René Riedl, Marco Hubert, and Peter Kenning. Are there
neural gender diﬀerences in online trust? an fmri study on
the perceived trustworthiness of ebay oﬀers. Mis Quarterly,34(2):397–428, 2010.
Ryota Kanai and Geraint Rees. The structural basis ofinter-individual diﬀerences in human behaviour and cognition. Nature Reviews Neuroscience, 12(4):231–242, 2011.
Liye Yi, Jinhui Wang, Longfei Jia, Zhilian Zhao, Jie Lu,
Kuncheng Li, Jianping Jia, Yong He, Chuanlu Jiang, andYing Han. Structural and functional changes in subcortical vascular mild cognitive impairment: a combined voxelbased morphometry and resting-state fmri study. 2012.

[23] Manuel Fernández-Delgado, Eva Cernadas, Senén Barro,and Dinani Amorim. Do we need hundreds of classifiers
to solve real world classification problems? The Journal of
Machine Learning Research, 15(1):3133–3181, 2014.
[24] Mauricio R Delgado, Leigh E Nystrom, C Fissell, DC Noll,and Julie A Fiez. Tracking the hemodynamic responses toreward and punishment in the striatum. Journal of neurophysiology, 84(6):3072–3077, 2000.
[25] Ahmad R Hariri, Venkata S Mattay, Alessandro Tessitore, Bhaskar Kolachana, Francesco Fera, David Goldman,
Michael F Egan, and Daniel R Weinberger. Serotonin transporter genetic variation and the response of the human
amygdala. Science, 297(5580):400–403, 2002.
[26] Sam J Gilbert, Stephanie Spengler, Jon S Simons, J Douglas Steele, Stephen M Lawrie, Christopher D Frith, andPaul W Burgess. Functional specialization within rostral
prefrontal cortex (area 10): a meta-analysis. Journal of
cognitive neuroscience, 18(6):932–948, 2006.
[27] Bruno Dubuc. The brain from top to bottom. 2002.
[28] Isabel Gauthier, Pawel Skudlarski, John C Gore, andAdam W Anderson. Expertise for cars and birds recruits
brain areas involved in face recognition. Nature neuroscience, 3(2):191–197, 2000.