Text S1. White Matter Network Model and Analysis

Appendix

Text S1. White matter network model and analysis

Data Preprocessing

The preprocessing of the DTI data involved the DTI pipeline software PANDA (http://www.nitrc. org/projects/panda/). The process included eddy current and motion artifact correction, estimation of the diffusion tensor and calculation of the fractional anisotropy (FA). Briefly, the eddy current distortions and motion artifacts in the DTI data were corrected by applying an affine alignment of each diffusion-weighted image to the b=0 image. After this process, the diffusion tensor elements were estimated by solving the Stejskal and Tanner equation, and then the reconstructed tensor matrix was diagonalized to obtain three eigen values (λ1, λ2, λ3) and three eigenvectors, and the corresponding FA value of each voxel was calculated.

Network construction

Nodes and edges are the two basic elements of a network. In this study, we defined all of the network nodes and edges using the following procedures.

Network node definition.

Specifically, the individual FA image in native space was coregistered to a T1-weighted structural image using an affine transformation. The individual structural image was then non- linearly registered to the ICBM152 template. Based on the resultant transformations of these two steps, an inverse warping transformation from the standard space to the native dMRI space was obtained. Prior atlases in the standard space were then inversely warped back to the individual native space by applying this inverse transformation. The inverse transformations were used to warp the automated anatomical labeling (AAL) atlasfrom the MNI space to the DTI native space. Notably, the discrete labeling values were preserved via the nearest- neighbor interpolation method. Using this procedure, we obtained 90 cortical and subcortical regions and each represented a node of the network. In these steps, the functions of FSL(http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) were used.

WM tractography. Diffusion tensor tractography was implemented via the "fiber assignment by continuous tracking" method. All of the tracts in the dataset were computed by seeding each voxel with an FA greater than 0.2. The tractography was terminated if it turned at an angle greater than 45 degrees or reached a voxel with an FA of less than 0.2. For each subject, tens of thousands of streamlines were generated to reveal the major WM tracts.

Network edge definition.

For the network edges, two regions were considered structurally connected if at least three fiber streamlines with two end-points were located in these two regions. As a result, we constructed the unweighted binary WM network for each participant that was represented by a symmetric 90×90 matrix.

Network Properties.

The path length between any pair of nodes (e.g., node i and node j) is defined as the sum of the edge lengths along this path. For weighted networks, the length of each edge is assigned by computing the reciprocal of the edge weight, 1/wij. The shortest path length, Lij, is defined as the length of the shortest path for node i and node j. The shortest path length of a network is computed as follows:

1 Lp( G ) = L ij N( N - 1) i刮 j G Eq(1) where N is the number of nodes in the network. The Lp of a network quantifies its ability for the parallel propagation of information..

To examine the small-world properties, the clustering coefficient, Cp, and shortest path length, Lp, of the brain networks were compared with those of random networks. In this study, we generated 100 matched random networks, which had the same number of nodes, edges, and degree distributions as the real networks (Maslov and Sneppen, 2002). Notably, we retained the weight of each edge during the randomization procedure such that the weight distribution of the network was preserved. Furthermore, we computed the normalized shortest

real rand l = Lp/ L p path length (lambda), , and the normalized clustering coefficient (gamma),

real rand rand rand g = Cp/ C p Lp Cp , where and are the mean shortest path length and the mean clustering coefficient of the 100 matched random networks, respectively. Notably,the two parameters correct the differences in the edge numbers and degree distributions of the

g >1 networks across individuals. A real network would be considered small-world if and l 1 (Watts and Strogatz, 1998). In other words, a small-world network not only has higher local interconnectivity but also had shortest path lengthsthat are approximately equivalent to those of the random networks. These two measurements can be summarized in a simple quantitative metric termed small-worldness,

s= g/ l Eq(2) which is typically greater than 1 for small-world networks.

Network efficiency: The global efficiency of G measures the global efficiency of the parallel information transfer in the network [56], which can be computed as:

1 1 Eglob ( G ) = N( N- 1) i刮 j G Lij Eq(3) where Lij is the shortest path length between node i and node j in G.

The local efficiency of G reveals how fault tolerant the network is and reveals how efficient the communication among the first neighbors of the node i is when node i is removed. The local efficiency of a graph is defined as:

1 Eloc( G )= E glob ( G i ) N i G Eq(4) where Gi denotes the subgraph composed of the nearest neighbors of node i.

Regional nodal characteristics: To determine the nodal (regional) characteristics of the WM networks, we computed the regional efficiency, Enodal(i) Eq(5) where Lij is the clustering coefficient between node i and node j in G. Enodal(i) measures the average clustering coefficient between a given node i and all of the other directly connected nodes in the network. APOE allele n Gender (M/F) APOE ε2/ε2 4(0.5%) 2/2 APOE ε2/ε3 116(13.1%) 39/77 APOE ε2/ε4 10(1.1%) 2/8 APOE ε3/ε3 616(69.6%) 230/386 APOE ε3/ε4 131(14.8%) 49/82 APOE ε4/ε4 8(0.9%) 3/5 APOE ε2 7.57% APOE ε3 83.56% APOE ε4 8.87% Table S1. APOE genotype distribution in the behavioral cohort (n=885) Table S2. Demographic and cognitive performance of APOE ε4 carrier and non-carrier

APOE 4- APOE 4+ t/X2value P value

Demographic

Number(%) 736(84.1%) 139(15.9%)

Age(years) 65.24(7.38) 65.31(7.41) -0.116 0.907 Education(years) 10.82(3.68) 11.10(3.56) -0.842 0.400 Gender(M/F) 267/460 53/94 0.018 0.894 Cognition Performance

General cognition

MMSE 27.61(2.01) 27.12(2.43) 2.614 0.009* Episodic Memory AVLT delayed recall 5.66(2.59) 5.00(2.77) 2.723 0.007* AVLT total 29.94(9.59) 28.02(9.97) 2.162 0.031 RO-figure delayed recall 13.01(6.43) 12.586.65 0.078 0.479 Visuo-Spatial Ability RO-figure copy 32.96(4.05) 32.60(4.54) 0.962 0.336 Clock Drawing Test 24.36(4.11) 24.49(3.65) -0.332 0.740 Language Category Fluency 44.38(9.05) 43.64(9.73) 0.878 0.380 Boston Naming Test 22.90(3.78) 23.01(4.17) -0.316 0.752 Attention Symbol Digit Modifying Test 34.35(11.72) 31.86(11.74) 2.287 0.022* Trail Making Test A 61.80(28.65) 66.33(43.47) -1.562 0.119 Executive Function Trail Making Test B 183.50(78.06) 191.66(83.26) -0.554 0.580 Stroop Test C (time) 78.15(23.02) 79.39(28.77) -1.12 0.263 Unless otherwise indicated, data are mean (standard deviation). Table S3. Group Differences in global measures of WM structural network

WM network APOE 4- APOE 4 Main Effect APOE 4×cognition interaction properties

NC(n=34) MCI(n=32) NC(n=26) MCI(n=18) p value p value p value γ 3.25(0.25) 3.42(0.33) 3.29(0.27) 3.31(0.31) 0.433 0.087 0.075 λ 1.09(0.014) 1.10(0.015) 1.09(0.013) 1.09(0.016) 0.686 0.028* 0.086 Cp 0.45(0.015) 0.46(0.019) 0.46(0.015) 0.45(0.021) 0.226 0.372 0.004* Lp 2.10(0.061) 2.14(0.087) 2.11(0.061) 2.12(0.075) 0.543 0.063 0.234 LocE 0.68(0.016) 0.69(0.019) 0.69(0.019) 0.68(0.022) 0.643 0.844 0.011* GloE 0.47(0.014) 0.47(0.018) 0.48(0.014) 0.47(0.16) 0.560 0.069 0.236

Abbreviations: λ=normalized shortest path, γ=normalized clustering coefficient, Lp =shortest path, Cp=clustering coefficient, LocE= local efficiency, GloE=global efficiency. For details see

Methods and Supplementary Materials S2. Figure S1: Flow diagram of study participants

MMSE=Mini-Mental State Examination, MCI=mild cognitive impairment, NC=normal cognition, ε2=APOE ε2, ε4=APOE ε4, MRI=Magnetic Resonance Imaging.

Figure S2: Flowchart of the WM network construction by diffusion MRI

The reconstruction of WM fibers of the whole brain was performed using DTI deterministic tractography in PANDA (http://www.nitrc.org/ projects/panda/). The network nodes were defined by parcellation of the brain into the 90 regions of the AAL template based on T1- weighted images. Two regions were considered to be structurally connected if at least three fibers streamlined with two endpoints were located those two regions. Next, the binary networks of each subject were created by the fiber pathways that connected each pair of brain regions. The matrix and 3D representation (axial view) of the WM network of a normal subject are shown in the right panel. The nodes and connections were mapped onto the cortical surfaces using the in-house BrainNet viewer software (www.nitrc.org/projects/bnv/). Reference

1. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 2002; 15(1): 273-289. 2. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 2010; 52(3): 1059-1069. 3. Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS computational biology 2007; 3(2): e17. 4. Shu N, Liang Y, Li H, Zhang J, Li X, Wang L et al. Disrupted topological organization in white matter structural networks in amnestic mild cognitive impairment: relationship to subtype. Radiology 2012; 265(2): 518-527.