Supplementary material
Risperidone-Induced Topological Alterations of Anatomical Brain Network in
First-episode Drug-Naive Schizophrenia Patients: a Longitudinal DTI Study
Maolin Hu, M.D.1,2, Xiaofen Zong, M.D.1, Junjie Zheng,Ph.D.3, J. John Mann,M.D.2, Zongchang Li,Ph.D.1, Spiro P.
Pantazatos,Ph.D.4, Yibo Li,Ph.D.3, Yanhui Liao,M.D.,Ph.D.1,5, Ying He,M.D.1, Jun Zhou,Ph.D.1, Deen Sang,M.S.6,
Hongzeng Zhao, M.D.6, Jinsong Tang, M.D.,Ph.D.*,1,5, Huafu Chen, Ph.D.*,3, Luxian Lv, M.D., Ph.D.*,7,8, Xiaogang
Chen, M.D., Ph.D.*,1,9,10,11
1 Mental Health Institute of the Second Xiangya Hospital, Central South University, 139 Middle Renmin Road,
Changsha, Hunan 410011, China.
2 Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute and Departments of
Psychiatry and Radiology, Columbia University, 1051 Riverside Drive, New York, NY 10032, USA.
3 Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology,
University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China.
4 Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute and Departments of
Psychiatry, Columbia University, New York, NY 10032, USA.
5 Department of Psychiatry and Biobehavioral Sciences, UCLA Semel Institute for Neuroscience, David Geffen
School of Medicine, Los Angeles, CA 90024, USA.
6 Department of Radiology, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical
University, Xinxiang, Henan 453002, China.
7 Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical
University, Xinxiang, Henan 453002, China.
8 Henan Key Lab of Biological Psychiatry, Henan Mental Hospital, Xinxiang Medical University, Xinxiang,
Henan 453002, China;
9 The China National Clinical Research Center for Mental Health Disorders, 139 Middle Renmin Road, Changsha,
Hunan 410011, China
10 National Technology Institute of Psychiatry, 139 Middle Renmin Road, Changsha, Hunan 410011, China.
11 Key Laboratory of Psychiatry and Mental Health of Hunan Province, 139 Middle Renmin Road, Changsha,
Hunan 410011, China.
*Correspondence: Xiaogang Chen ([email protected]), Jinsong Tang ([email protected]), Huafu
Chen ([email protected]) and Luxian Lv ([email protected])
Supplementary Introduction
Topology is a mathematical approach of qualitative properties of certain objects (called topological
spaces) which are preserved under a certain kind of transformation (deformations, twistings or
stretchings). The topological analysis provides a mathematical framework for characterizing and
quantifying the specific features (topological properties) of brain networks (Minati et al. 2013) .
According to this method, we can describe the brain as a network composed of nodes (brain areas)
and edges (connectivities among various regions), and explore the organization and structure of brain
by testing relationships among nodes and edges (He and Evans 2010)
The detailed description of topological characteristics
We used the Brain Connectivity Toolbox (http://www.brainconnectivtiy-toolbox.net) to calculate
the following network properties (Rubinov and Sporns 2010):
w
Nodal characteristics explored in the current study consist of: 1. nodal degree ( ki ), 2.
w
clustering coefficiency (CCnodal), 3. path length (PLnodal), 4. betweenness centrality ( bi ), 5. nodal
efficiency (Enodal).
w
The nodal degree ki is equal to the sum of the weighted streamlines terminating in a node i,
and it provides information on the total level of weighted connectivity of a node. This measurement
reflects the importance of a node in the network. A node with a high degree is more heavily
connected with other nodes in the network. Formally:
kiw 
j
N
w ij
,
with Wij the weighed connection between node i and node j (van den Heuvel et al. 2010).
CCnodal is defined as the fraction of how close the node's neighbors are to being a clique
(complete graph), and it provides information on how strong node i and its direct neighbors are
clustered. A node with high CCnodal is thus associated with efficient communication. Formally:
CC nodal 
with W
ij
j h
1
(w ijw ihw jh )3
, N
kiw (kiw  1)
,
w
the weighted link between node i and node j, ki the nodal degree (Rubinov and Sporns
2010).
PLnodal refers to the average of the shortest path from the node to all the other nodes in the
network, and it expresses how close a node is connected globally to other nodes, with shorter path
lengths reflecting higher levels of efficient access to information. Formally:
PLnodal 
j
N .j i
d ij
n 1
,
with dij the shortest distance between node i and node j (Rubinov and Sporns 2010).
w
Betweenness centrality, bi is equivalent to the number of all shortest paths in the network that
pass through a node and it expresses the importance of a node i in facilitating communication
between the different areas of the network:
biw 
sp hj(i )
1

h ,j N ,h i ,j ,i  j
(n  1)(n  2)
sp hj
,
with sphj(i) the number of shortest path between h and j that passes through node i (van den Heuvel et
al. 2010).
Efficiency of a node Enodal is defined as the reciprocal or inverse of path length (Rubinov and
Sporns 2010), and this measure reflects the efficiency of information transfer or communication:
E noal 
j
N ,j  i
d ij1
n 1
,
with wij the weighted link between node i and node j, djh the shortest path between node j and h.
w
Overall network characteristics include: 1. net strength ( S net ), 2. global-efficiency (Eglob), 3.
local-efficiency (Eloc), 4. normalized clustering coefficients (γ), 5. normalized path lengths (λ), 6.
small-worldness (σ).
w
The S net is defined as the gross of all neighboring link weights, and it refers to the total
connection strength of a network. This measurement reflects the development and resilience of the
network. Formally:
w
S net

1
i N kiw

N
with k iw the weighted nodal degree, N the number of nodes (van den Heuvel et al. 2010).
Global efficiency of the network Eglob is the average inverse shortest path length in the network:
1
E glob 

n
j
N ,j i
d ij1
n 1
i N
,
with dij the shortest distance between node i and node j (Rubinov and Sporns 2010).
Local efficiency of the network Eloc is the Eglob computed on node neighborhoods:
E loc 
1
n

j h
, N ,j i
w ijw ih[d jh(N i )]1
kiw (kiw  1)
i N
,
with wij the weighted link between node i and node j; djh (Ni) the length of the shortest path between
node j and h, that contains only neighbors of node i (Bullmore and Sporns 2012) .
The overall clustering-coefficient CCreal of network is computed as the average of CCnodal (van
den Heuvel et al. 2010):
CC real 
1
n
i N CC nodal .

The overall path length PLreal of the entire network is computed as the mean overall PLnodal, and
it expresses how well the overall network is connected (van den Heuvel et al. 2010):
PLreal 
1
N
i N PLnodal .

Then, a set of random networks were created by randomizing the connections of the network,
keeping the degree distribution and sequence of the matrix intact (Rubinov and Sporns 2010). The
CCrandom and PLrandom of the random network were evaluated following methods above.
Normalized clustering coefficients γ was computed:
γ
CC real
CC random .
Normalized path lengths λ was evaluated:
 
PLreal
PLrandom .
Finally, small-worldness (σ=γ/λ) was used to describe complex networks that have high
efficiency and high clustering (Watts and Strogatz 1998).
The Eglob, PLnodal and λ properties reflect the information integration efficiency of the network,
w
w
while the Eloc, CCnodal and γ reflect the functional segregation. The architectural features ki , S net
w
and σ represent the network architecture, and the bi represent the measure of centrality.
Supplementary Discussion
There were no group differences in global topological parameters in the current study. Consistently,
Fornito (Fornito et al. 2011) also identified relatively intact overall topological organizations of
functional network in FESP. In comparison, studies of CSP have reported deficits in a variety of
global topological attributes, in both structural and functional network (Collin et al. 2014; Fornito et
al. 2012). We postulate that patients’ overall topology is preserved in early phrase of their psychosis
but deteriorates progressively with ongoing disease. Future studies with longer follow-up are
required to test the hypothesis.
Supplementary Table S1. Demographic and clinical characteristics for first-episode schizophrenia
patients both at baseline and follow-up and their matched healthy comparison subjects
Patients at Baseline
Patients at Follow-up
Controls
Variable
(n=42)
(n=42)
M
SD
Age(years)
24.86
Education
M
Analysisa
Analysisb
t
P
(n=38)
SD
M
SD
t
P
4.80
24.76
4.56
0.09
0.929
10.48
2.84
11.05
2.91
0.90
0.373
Duration of illness(month)
8.38
2.61
PANSS-T
91.90
11.23
67.24
10.10
13.19
<0.001
PANSS-P
25.60
3.75
15.83
3.28
14.58
<0.001
PANSS-N
18.17
5.21
17.07
4.86
1.42
0.163
PANSS-G
48.14
6.46
34.33
4.71
13.37
<0.001
Yes
No
Yes
No
χ2
P
χ2
P
Gender(male)
27
15
25
13
0.02
0.888
Alcohol use
6
36
9
29
1.16
0.282
Tobacco use
9
33
8
30
0.002
0.967
15
27
0
38
42
0
38
0
Family history of
psychiatric illness
Handedness (right)
apatients
at baseline vs. controls, independent-samples T-test.
bpatients
at baseline vs. follow-up, paired-samples T-test.
PANSS = Positive and Negative Syndrome Scale; PANSS-T = PANSS total scores; PANSS-P = PANSS positive symptom scores;
PANSS-N = PANSS negative symptom scores; PANSS-G = PANSS general psychopathological symptom scores.
Supplementary Table S2. Comparison results of nodal topological characteristics (FDR corrected)
Nodal
property
Patientsb
Controlsa
(n=37)
Baseline(n=41)
Week 8(n=38)
Analysisc
Analysisd
Analysise
Left ACG/AAL31
kiW
1022.0±836.0
671.8±514.4
868.2±824.1
0.035
0.043
0.066
CCnodal
31.3±23.7
23.0±16.0
25.4±18.5
0.044
0.074
0.060
Enodal
53.2±24.9
43.2±18.6
47.6±23.1
0.047
0.051
0.073
Right ACG/AAL32
kiW
702.0±740.0
423.6±453.7
555.7±596.7
0.032
0.023
0.062
CCnodal
32.9±22.2
22.1±16.5
28.0±25.3
0.033
0.050
0.056
Enodal
45.5±24.8
33.8±20.7
38.3±23.1
0.040
0.048
0.076
Left PCG/AAL35
kiW
3758±1571
3526±1267
3770±1366
0.067
0.043
0.079
CCnodal
54.9±24.5
43.6±18.4
48.6±21.7
0.035
0.042
0.056
Enodal
87.5±29.2
82.1±22.8
87.0±23.1
0.072
0.044
0.094
Right PCG/AAL36
kiW
3867±1590
3396±1244
3787±1373
0.050
0.014
0.079
CCnodal
54.3±21.4
45.1±17.3
49.6±22.8
0.048
0.049
0.060
Enodal
89.5±28.9
80.7±22.7
87.5±23.3
0.062
0.015
0.086
Left SFG_med_orb/AAL25
kiW
459.9±332.4
245.6±179.7
292.7±213.7
0.019
0.052
0.019
CCnodal
20.5±10.2
16.0±7.1
16.0±8.4
0.034
0.124
0.037
Enodal
32.3±15.1
22.1±10.3
24.7±11.2
0.013
0.079
0.041
Right pallidum/AAL76
kiW
937.9±555.6
721.0±342.4
815.8±416.4
0.020
0.060
0.049
CCnodal
16.17±4.58
15.85±5.66
16.12±6.22
0.062
0.130
0.068
Enodal
58.03±19.03
50.38±15.45
54.33±17.72
0.044
0.063
0.070
left amygdala/AAL41
kiW
150.7±138.2
115.7±128.1
172.7±128.6
0.056
0.003
0.071
CCnodal
9.35±5.03
7.71±4.33
9.64±4.51
0.049
0.033
0.075
Enodal
18.1±10.8
15.7±8.7
20.4±8.5
0.071
0.005
0.055
left PHG/AAL39
kiW
138.1±100.6
175.4±153.6
182.8±111.8
0.053
0.044
0.035
CCnodal
9.01±4.25
9.29±5.26
9.81±3.82
0.078
0.055
0.062
Enodal
16.7±8.8
17.7±8.6
20.2±9.7
0.078
0.030
0.053
left CAU/AAL71
kiW
1065.6±489.3
892.6±427.1
769.4±387.6
0.052
0.013
0.015
CCnodal
15.29±5.38
13.88±5.45
12.98±5.70
0.049
0.059
0.049
Enodal
49.24±14.61
44.56±15.43
41.65±16.10
0.063
0.053
0.043
aOne
of the reconstructed brain network was removed from the 38 healthy volunteers for its lowest outlier network
density.
bOne of the 42 patients discontinued during the DTI scan at baseline, and also withdrew from the follow-up MRI
scan. Three other patients withdrew from the follow-up MRI scans.
cpatients at baseline vs. controls, permutation test with FDR correction.
dpatients at baseline vs. follow-up, paired-samples T-test with FDR correction.
epatients at follow-up vs. controls, permutation test with FDR correction.
FDR = False discovery rate; AAL = Automated Anatomical Labeling atlas; ACG = anterior cingulate and
paracingulate gyri; PCG=Posterior cingulate gyrus; SFG_med_orb = superior frontal gyrus medial orbital; PHG =
W
parahippocampal gyrus; CAU = caudate nucleus; ki = nodal degree; CCnodal =clustering coefficiency; Enodal =
nodal efficiency.
Supplementary Table S3. Comparisons of nodal topological characteristics comparing to null
model with similar density and strength
Comparison results (FDR corrected)
Nodal Property
Patients_0w vs. Controls
Patients_8w vs. Patients_0w
Patients_8w vs. Controls
CCnodal
0.1763
0.3459
0.1823
PLnodal
0.0649
0.0791
0.0593
biW
0.2760
0.3890
0.2637
Enodal
0.0506
0.0640
0.0575
CCnodal
0.0824
0.1738
0.1791
PLnodal
0.0493
0.1321
0.0511
biW
0.0127
0.2378
0.0059
Enodal
0.0269
0.0427
0.0485
CCnodal
0.1834
0.1891
0.1920
PLnodal
0.0615
0.0526
0.0484
biW
0.2794
0.3787
0.2700
Enodal
0.0616
0.0149
0.0503
CCnodal
0.1848
0.2037
0.2040
PLnodal
0.0622
0.0549
0.0486
biW
0.2716
0.2741
0.2574
Left ACG/AAL31
Right ACG/AAL32
Left PCG/AAL35
Right PCG/AAL36
0.0590
0.0049
0.0577
CCnodal
0.1778
0.2045
0.1153
PLnodal
0.0542
0.1214
0.0569
biW
0.2570
0.3862
0.2598
Enodal
0.0181
0.0575
0.0372
CCnodal
0.2036
0.1799
0.2049
PLnodal
0.0715
0.0781
0.0568
biW
0.2712
0.2518
0.2758
Enodal
0.0570
0.1030
0.0613
CCnodal
0.1975
0.0767
0.2030
PLnodal
0.0748
0.0496
0.0528
biW
0.2641
0.3992
0.2527
Enodal
0.0620
0.0028
0.0367
CCnodal
0.1928
0.2055
0.1850
PLnodal
0.0644
0.0500
0.0513
biW
0.2630
0.2568
0.2609
Enodal
0.0488
0.0325
0.0155
Enodal
Left SFG_med_orb/AAL25
Right pallidum/AAL76
left amygdala/AAL41
left PHG/AAL39
left CAU/AAL71
CCnodal
0.2035
0.1176
0.1134
PLnodal
0.0725
0.0894
0.0768
biW
0.2672
0.3732
0.2507
Enodal
0.0565
0.0103
0.0301
The nodal degree which was described as strength of a node would have a significant impact on other metrics. Thus,
we performed normalization for these characters. We formed a null model for individual network by randomized
w
100 networks by reserving the degree and weight distribution. Then, we normalized CCnodal, PLnodal, bi , Enodal in
each node of individual network by dividing the mean values of each characters of random networks to control the
potential bias by differences in connectivity strength.
Patients_0w = patients at baseline; Patients_8w = patients after 8-weeks treatment; FDR = False Discovery Rate;
AAL = Automated Anatomical Labeling atlas; ACG = anterior cingulate and paracingulate gyri; PCG=Posterior
cingulate gyrus; SFG_med_orb = superior frontal gyrus medial orbital; PHG = parahippocampal gyrus; CAU =
W
caudate nucleus; CCnodal =clustering coefficiency; PLnodal = path length; bi = betweenness centrality; Enodal = nodal
efficiency.
Supplementary Table S4. Overall network characteristics of healthy controls and first-episode
schizophrenia patients for baseline and follow-up data
Overall
Patientsb
Controlsa
Analysisc
Analysisd
Analysise
Characteristics
(n=37)
Baseline(n=41)
Week 8(n=38)
γ
2.79±0.24
2.80±0.20
2.75±0.22
0.999
0.071
0.534
λ
1.39±0.09
1.36±0.06
1.37±0.08
0.219
0.999
0.497
W
S net
1238.06±290.35
1118.17±251.32
1189.02±287.12
0.088
0.061
0.724
Eglob
51.95±11.59
47.86±10.25
50.24±11.19
0.157
0.123
0.680
Eloc
82.08±15.78
75.93±15.00
78.70±15.95
0.101
0.390
0.461
σ
2.02±0.21
2.06±0.17
2.01±0.18
0.632
0.072
0.999
aOne
of the reconstructed brain networks was removed from the 38 healthy volunteers for its lowest outlier network
density.
bOne patient discontinued during the DTI scan at baseline, and also withdrew from the follow-up MRI scan. Three
other patients withdrew from the follow-up MRI scans.
cpatients at baseline vs. controls, permutation test with Bonferroni correction.
dpatients at baseline vs. follow-up, paired-samples T-test with Bonferroni correction.
epatients at follow-up vs. controls, permutation test with Bonferroni correction.
W
γ = normalized clustering coefficients; λ = normalized path lengths; S net = net strength; Eglob = global efficiency;
Eloc = local-efficiency; σ = small-worldness.
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