Supplemental Data s8

Supplemental Data

Appendix e-1. Methods

Appendix e-2. Results

Table e-1

Table e-2

Table e-3

Table e-4

Figure e-1

Figure e-2

E-References

1 Appendix e-1. Methods

Graph-theoretic measures

We measured the brain functional network topologies using the Brain

Connectivity Toolbox (http://www.brain-connectivity-toolbox.net) 1. We evaluated the following global network measures: 1) overall clustering coefficient, 3) shortest path length, and 5) small-worldness. Nodal strength were also calculated for each node. The definition and brief interpretation of these metrics is described below

Small-world properties

Small-world properties were originally proposed by Watts and Strogatz 2.

Here, we investigated small-world properties of structural connectivity

w network. The weighted clustering coefficient of node i , Ci , which expresses the likelihood that the neighbourhoods of node i are connected 3, is defined

1/ 3 (w ijw ihw jh ) as follows: w j,hN , where wij is the weight between nodes i Ci  ki (ki 1)

and j in the network, and ki is the degree of node i . The clustering

2 w coefficient is zero, Ci  0, if the nodes are isolated or with just one

w connection. The overall clustering coefficient, Cnet , was computed as the

1 w C w  C w average of Ci across all nodes in the network: net  i , extent N iN measure of the local interconnectivity or cliquishness of the network 2.

The path length between nodes i and j was defined as the sum of the edge lengths along the path, where each edge’s length was obtained by computing

the reciprocal of the edge weight, 1/ wij . The shortest path length Lij between nodes i and j was defined as the length of the path with the shortest length

w between the two nodes. The weight characteristic shortest path length Lnet of a network was measured by a “harmonic mean” length between pairs 4, to overcome the problem of possibly disconnected network components.

w Formally, Lnet is the reciprocal of the average of the reciprocals:

3 w 1 Lnet  N N 1  1 , where N is the number of nodes. The weight N(N 1) i1 ji Lij characteristic shortest path length quantifies the ability for information propagation in parallel.

w w To examine small-world properties related to Cnet and Lnet , brain networks were compared to random networks. A small-world network has similar path

w w length but higher clustering than a random network, that is   Cnet / Crandom 1,

w w 2   Lnet / Lrandom  1 . These two conditions can also be summarized into a scalar quantitative measurement, the small-worldness,    /  , that is typically larger than one in case of small-world organization . For each individual brain network a set of 100 comparable random networks with similar degree sequence and symmetric adjacency matrix were formed, and

w w Crandom and Lrandom were defined as the average weighted clustering coefficient and weighted path length.

Nodal characteristics analysis

w Three nodal topological characteristics, including nodal strength ( Si ). The

4 w strength ( Si ) was computed as the sum of the weights of all the connections

w w Si  wij of node i , that is  . The Si quantifies the extent to which a node is jN

1 w relevant to the graph . The total connection strength Snet of the network was

1 w S w  S w computed as the sum of Si for all nodes N in the network: net  i . N iN

Multivariate pattern analysis

A multivariate pattern analysis (MVPA) was adopted to predict the surgical outcomes. This approach employs a set of machine learning-based algorithms allowing multivariate individual-level prediction of group membership on high dimensional imaging data. Specifically, linear support vector machine (SVM), from the LIBSVM machine learning library, was used here (www.csie.ntu.edu.tw/~cjlin/libsvm). Graph measures showing significant between-group difference preoperatively were used as measure-of-interest for the prediction. A linear decision boundary in this high dimensional space was defined by a hyperplane that separated the individual outcome according to a class label (i.e., SF vs. NSF).

5 6 Appendix e-2. Results

Main effect in ANOVA

Small-worldness did not show significant group and treatment main effect.

Connectivity feature (edge) with significant treatment main effect were predominantly in left hemisphere connecting: ITG (temporooccipital part) and

LOC (inferior division) (T = -4.07, P = 0.1 × 10-3), CO cortex and SFG (T =

-4.12, P = 0.1 × 10-3)/IFG (pars triangularis [T = 3.65, P = 0.4 × 10-3], pars opercularis [T = 3.62, P = 0.4 × 10-3]), Frontal Pole and thalamus (T = 4.10, P

= 0.1 × 10-3)/caudate (T = 3.75, P = 0.3 × 10-3)/pallidum (T = 4.04, P = 0.1 ×

10-3), SFG and insular (T = -4.93, P = 0.9 × 10-5)/putamen (T = -3.98, P = 0.2 ×

10-3)/pallidum (T = -3.89, P = 0.2 × 10-3)/ precentral gyrus (T = 3.57, P = 0.5 ×

10-3), IFG (pars triangularis) and insular (T = 3.62, P = 0.4 × 10-3)/FO cortex (T

= -4.26, P = 0.7 × 10-4), insular and FO cortex (T = 5.40, P = 0.2 × 10-

5)/thalamus (T = 4.96, P = 0.8 × 10-5)/pallidum (T = 3.62, P = 0.4 × 10-3), caudate and putamen (T = -4.42, P = 0.4 × 10-4). Right cuneal cortex and right lingual gyrus also showed significant treatment main effect (T = -3.70, P = 0.3

× 10-3) (Figure e-1, table e-2).

Nodal strength (Si) showed significant group main effect in bilateral hemispheres: left CO cortex (T = 2.90, P = 0.3 × 10-2), left subcallosal cortex

(T = 2.64, P = 0.6 × 10-2), right postcentral gyrus (T = 2.93, P = 0.3 × 10-2), right MTG (anterior part) (T = 2.97, P = 0.3 × 10 -2), right MTG (posterior part)

7 (T = 2.70, P = 0.5 × 10-2), right TP (T = 2.57, P = 0.7 × 10-2) (Figure e-2), while the treatment main effect exclusively in left hemisphere similar to connectivity feature: SPL (T = 2.82, P = 0.4 × 10-2), lateral occipital cortex (superior division) (T = 2.70, P = 0.5 × 10-2), FP (T = 2.83, P = 0.4 × 10-2), and IFG (pars triangularis) (T = 4.01, P = 0.1 × 10-3) (Figure e-2, table e-3, and e-4).

Multivariate pattern analysis

All the six nodes with significant group main effect showed between-group difference preoperatively, while sigma and edges did not. Thus, nodal strength in these nodes were adopted to distinguish SF and NSF patients based on preoperative data. The MVPA indicated a good discrimination for the NSF patients: area under receiver operating characteristic curve = 0.80, accuracy =

0.79, sensitivity = 0.75, specificity = 0.82.

8 Figure legend:

Figure e-1 Edges show significant treatment main effect. SFG.L = left superior frontal gyrus, FP.L = left frontal pole, IFG3T.L = left inferior frontal gyrus (pars triangularis), IFG3o.L = left inferior frontal gyrus (pars opercularis), PRG.L = left precentral gyrus, INS.L = left insular, OLi.L = left lateral occipital cortex

(inferior division), IGTto.L = left inferior temporal gyrus (temporooccipital part),

FOC.L = left frontal orbital cortex, Tha.L = left thalamus, Pall.L = left pallidum,

Put.L = left putamen, Caud.L = left caudate, CN.R = right cuneal cortex, LG.R

= right lingual gyrus, CO = central opercular.

Figure e-2. Nodes show significant group (A) or treatment (B) main effect.

TGant.R = right temporal gyrus (anterior part), CO.L = left central opercular cortex, POG.R = right postcentral gyrus, MTGant.R = right middle temporal gyrus (anterior part), MTGpost.R = right middle temporal gyrus (anterior part),

SC.L = left subcallosal cortex, TP.R = right temporal pole, SPL.L = left superior parietal lobule, OLs.L = left lateral occipital cortex (superior division), FP.L = left frontal pole, IFG3t.L = left inferior frontal gyrus (pars triangularis).

Figure e-3 The area under the receiver operating characteristic cure (= 0.80) indicating good discriminating ability

9 e-References:

1. Rubinov M, Sporns O. Complex network measures of brain connectivity:

Uses and interpretations. Neuroimage 2010;52:1059-1069.

2. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks.

Nature 1998;393:440-442.

3. Onnela JP, Saramaki J, Kertesz J, Kaski K. Intensity and coherence of motifs in weighted complex networks. Phys Rev E Stat Nonlin Soft Matter

Phys 2005;71:065103.

4. Newman MEJ. The structure and function of complex networks. SIAM review 2003;45:167-256.

5. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low- frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006;26:63-72.

6. Humphries MD, Gurney K, Prescott TJ. The brainstem reticular formation is a small-world, not scale-free, network. Proc Biol Sci 2006;273:503-511.

10 Table e-1Regions of interest in the Harvard-Oxford Atlas

Region name Abbreviatio n Precentral Gyrus PRG.L Postcentral Gyrus POG.L Intracalcarine Cortex CALC.L Heschls Gyrus (includes H1 and H2) HG.L Occipital Pole OP.L Superior Temporal Gyrus, anterior division STGant.L Superior Temporal Gyrus, posterior division STGpost.L Inferior Temporal Gyrus, anterior division ITGant.L Inferior Temporal Gyrus, posterior division ITGpost.L Inferior Temporal Gyrus, temporooccipital ITGto.L part Superior Parietal Lobule SPL.L Supramarginal Gyrus, anterior division SGant.L Lateral Occipital Cortex, superior division OLs.L Lateral Occipital Cortex, inferior division OLi.L Supplementary Motor Cortex SMC.L Cuneal Cortex CN.L Lingual Gyrus LG.L Temporal Fusiform Cortex, anterior division TFant.L Temporal Fusiform Cortex, posterior division TFpost.L Temporal Occipital Fusiform Cortex TOF.L Occipital Fusiform Gyrus OF.L Frontal Operculum Cortex FO.L Central Opercular Cortex CO.L Parietal Operculum Cortex PO.L Planum Polare PP.L Planum Temporale PT.L Supracalcarine Cortex SCLC.L Frontal Pole FP.L Superior Frontal Gyrus SFG.L Middle Frontal Gyrus MFG.L Inferior Frontal Gyrus, pars triangularis IFG3t.L Inferior Frontal Gyrus, pars opercularis IFG3o.L Middle Temporal Gyrus, anterior division MTGant.L Middle Temporal Gyrus, posterior division MTGpost.L Middle Temporal Gyrus, temporooccipital part MTGto.L Supramarginal Gyrus, posterior division SGpost.L Angular Gyrus AG.L Paracingulate Gyrus PAC.L

11 Precuneus Cortex PCN.L Insular Cortex INS.L Temporal Pole TP.L Frontal Medial Cortex FMC.L Subcallosal Cortex SC.L Cingulate Gyrus, anterior division CGant.L Cingulate Gyrus, posterior division CGpost.L Frontal Orbital Cortex FOC.L Parahippocampal Gyrus, anterior division PHant.L Parahippocampal Gyrus, posterior division PHpost.L Hippocampus Hip.L Amygdala Amy.L Thalamus Tha.L Caudate Caud.L Putamen Put.L Pallidum Pall.L Accumbens Accbns.L Precentral Gyrus PRG.R

12 Table e-2 Connections showing significant treatment main effect T P Left hemisphere ITG ― LOC -4.07, 0.1 × 10-3 CO ― SFG -4.12 0.1 × 10-3 CO ― IFG (pars triangularis) 3.65 0.4 × 10-3 CO ― IFG (pars opercularis) 3.62 0.4 × 10-3 Frontal Pole ― thalamus 4.10 0.1 × 10-3 Frontal Pole ― caudate 3.75 0.3 × 10-3 Frontal Pole ― pallidum -3.89 0.2 × 10-3 SFG ― insular -4.93 0.9 × 10-5 SFG ― putamen -3.98 0.2 × 10-3 SFG ―pallidum -3.89 0.2 × 10-3 SFG ― precentral gyrus 3.57 0.5 × 10-3 IFG (pars triangularis) ― insular 3.62 0.4 × 10-3 IFG (pars triangularis) ― FOC -4.26 0.7 × 10-4 Insular ― FOC 5.4 0.2 × 10-5 Insular ― thalamus 4.96 0.8 × 10-5 Insular ― pallidum 3.62 0.4 × 10-3 Caudate ― putamen -4.42 0.4 × 10-4 Right hemisphere Cuneal cortex ― lingual gyrus -3.70 0.3 × 10-3 ITG = inferior temporal gyrus, LOC = lateral occipital cortex, CO = central opercular, SFG = superior frontal gyrus, IFG = inferior frontal gyrus, FOC = frontal orbital cortex.

13 Table e-3 Regions showing significant group main effect T value P Left hemisphere Central Opercular Cortex 2.90 0.3 × 10-2 Subcallosal cortex 2.64 0.6 × 10-2 Right hemisphere Postcentral gyrus 2.93 0.3 × 10-2 MTG (anterior part) 2.97 0.3 × 10-2 MTG (posterior part) 2.70 0.5 × 10-2 Temporal pole 2.57 0.7 × 10-2 MTG = middle temporal gyrus.

14 Table e-4 Nodes showing significant treatment main effect T P Left hemisphere Superior parietal lobule 2.82 0.4 × 10-2 LOC (superior division) 2.70 0.5 × 10-2 Frontal ploe 2.83 0.4 × 10-2 IFG (pars triangularis) 4.01 0.1 × 10-3 LOC = Lateral occipital cortex, IFG = inferior frontal gyrus