Disrupted functional and structural networks in cognitively normal

elderly subjects with the APOE ε4 allele

SUPPLEMENTARY MATERIALS

Neuropsychological testing

The comprehensive neuropsychological battery was comprised of the following 5

cognition domains with the tests included in parentheses: 1,memory (Auditory Verbal

Learning Test (AVLT) , Rey-Osterrieth Complex Figure test (ROCF)(recall) and

backward Digit Span ); 2,attention (Trail Making Test (TMT) A, Symbol Digit

Modalities Test (SDMT) and Stroop Color and Word Test (SCWT)-B); 3,visuo-spatial

ability (ROCF (copy), Clock-Drawing Test (CDT)); 4, language (Category Verbal

Fluency Test (CVFT), Boston Naming Test (BNT) ); and 5,executive function (TMT-

B and SCWT-C).

MRI data acquisition

All participants were scanned with a SIEMENS TRIO 3T scanner in the Imaging

Center for Brain Research at Beijing Normal University, including high-resolution

T1-weighted structural MRI, diffusion tensor imaging (DTI) and resting state

functional MRI (rsfMRI) scans. Participants laid supine with their head fixed snugly

by straps and foam pads to minimize head movement. T1-weighted, sagittal 3D

magnetization prepared rapid gradient echo (MP-RAGE) sequences were acquired

and covered the entire brain [176 sagittal slices, repetition time (TR)=1900 ms, echo

time (TE)=3.44 ms, slice thickness=1 mm, flip angle=9°, inversion time=900 ms,

field of view (FOV)=256×256 mm2, acquisition matrix=256×256]. For each DTI

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scan, images covering the whole brain were acquired by an echo-planar imaging

sequence with the following scan parameters: TR=9500 ms, TE=92 ms, 30 diffusion-

weighted directions with a b-value of 1000 s/mm2, and a single image with a b-value

of 0 s/mm2, slice thickness=2 mm, no inter-slice gap, 70 axial slices, acquisition

matrix =128×128, FOV=256×256 mm2, averages=3. Resting state data were collected

using an echo-planar imaging sequence that consisted of a TE=30 ms, TR=2000 ms,

flip angle=90°, 33 axial slices, slice thickness=3.5 mm, acquisition matrix=64×64,

FOV =200×200mm2. During the single-run resting acquisition, subjects were

instructed to keep awake, relax with their eyes closed, and remain as motionless as

possible. The resting acquisition lasted for 8 minutes, and 240 image volumes were

obtained.

Preprocessing

The preprocessing of DTI data 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 , 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. All of the preprocessing of DTI data were

performed with the FDT toolbox in FSL (http://www.fmrib.ox.ac.uk/fsl). The

preprocessing of rsfMRI data included slice timing, within-subject inter scan

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realignment to correct possible movement, spatial normalization to a standard brain

template in the Montreal Neurological Institute coordinate space, resampling to

3×3×3 mm3, and smoothing with an 8-mm full-width half-maximum Gaussian kernel.

In addition, rsfMRI data were processed with linear detrending and 0.01-0.08 Hz

band-pass filtering.

Brain network construction

The brain network constructions each for DTI and rsfMRI data are based on the

approach previously reportedand detailed below. Nodes and edges are the two basic

elements of a network. In this study, we defined all network nodes and edges using

the following procedures.

White matter structural network construction

The nodes were defined in native space for each individual using the procedure

proposed by Gong and colleagues . Briefly, the skull-stripped T1-weighted image was

non-linearly and spatially normalized to Montreal Neurological Institute (MNI) space

using FMRIB's Linear Image Registration Tool. The individual FA images were

coregistered to the individual skull-stripped T1-weighted images. To transform the

AAL atlas from MNI space to DTI native space, the inverse transformations achieved

in the above two steps were successively applied to the AAL atlas. Using this

procedure, we obtained 90 nodes for the WM network. Diffusion tensor tractography

was implemented with DTI-studio software (H. Jiang, S. Mori, Johns Hopkins

University) by using the "fiber assignment by continuous tracking" method . All of the

tracts in the dataset were computed by seeding each voxel with an FA that was greater

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than 0.2. The tractography was terminated if it turned 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 etch out all of the major WM tracts. For the regional

pair-wise connections (referred to as edge) in the network, two regions were

considered structurally connected (with an edge) if at least three fiber streamlined

with two end-points was located in these two regions. Tractography results were

visually inspected by the experts in neuroimaging ((N.S., and K.W.C.) and no

apparent errors in fiber tracking were found. At the same time, we did not find any

significant differences in the tractography quality in carriers and non-carriers.

Specifically, we defined the average FA along the pathways of the interconnecting

streamlines between two regions as the weight of the network edges. As a result, we

constructed the FA-weighted WM network for each participant that was represented

by a symmetric 90×90 matrix.

Graph theory formulas

Global Efficiency is a global measure of the parallel information transfer ability in the

whole network. It is computed as the average of the inverse of the “harmonic mean”

of the characteristic path length:

where N is the number of nodes in the graph G, and is the characteristic path

length between nodes i and j in graph.

Local Efficiency quantifies the ability of a network to tolerate faults that correspond

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to the efficiency of the information flow between the nearest neighbors of any given

node . The local efficiency of a network is computed as follows:

where is the sub-graph composed of the nearest neighbors of node i and the

connections among them.

Nodal efficiency is a measure of its capacity to communicate with other nodes of the

network. The nodal efficiency for a given node (Enodal) was defined as the inverse of

the harmonic mean of the shortest path length between this node and all other nodes

in the network. The nodes with high nodal efficiency values can be categorized as

hubs in a network. Nodal efficiency (Enodal) was computed by the equation below:

where is the shortest path length between node i and node j. Here, node iis

considered to be a brain hub if Enodal(i) is at least 1 standard deviation (SD) greater

than the average nodal efficiency of the network.

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Supplementary Figures:

Figure S1. (A) White matter brain network matrices weighted by averaged FA. The

elements of this matrix indicate the average FA connecting node pairs. (B) Functional

network matrices weighted by averaged functional connectivity (FC). The elements of

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this matrix indicate the average FC connecting node pairs. Note that these matrices

are symmetrical.

Figure S2. Group differences in small-worldness of white matter structural and

functional networks were quantified between groups. Bars and error bars represent

mean values and standard deviations, respectively, of network properties ineach

group. ∗=Significant group difference at p<0.05; ∗∗= Significant group difference at

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p<0.001.

Figure S3. Receiver operating characteristic curves for MMSE, Global

efficiency_FUN, Global efficiency_WM, Decreasing_region_FUN, and

Decreasing_region_WM.

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Figure S4. Mediation analysis in lower ROCF-delayed recall scoring and higher

ROCF-delayed recall scoring subgroups (A). The mediation effect of PHG.R

efficiency in white matter network or functional network on AVLT-delayed recall (B)

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and Backward digit span (C) performances. *=Significant group difference at p<0.05;

**= Significant group difference at p<0.001.

Figure S5. Parahippocampal gyrus efficiency of ε4 carriers or non-carriers in both

networks

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Differences between the left and right parahippocampal gyrus were assessed in APOE

ε4 carriers and non-carriers using an analysis of covariance adjusted for age, sex and

education. PHG.L=left parahippocampal gyrus; PHG.R=right parahippocampal gyrus.

Supplementary Tables

Table S1. Parcellation of 90 AAL cortical and subcortical regions

Index Abbr. Regions Index Abbr. Regions

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1,2 PreCG Precentalgyrus 47,48 LING Lingual gyrus

3,4 SFGdor Superior frontal gyrus, dorsolateral 49,50 SOG Superior occipital gyrus

5,6 ORBsup Superior frontal gyrus, orbital part 51,52 MOG Middle occipital gyrus

7,8 MFG Middle frontal gyrus 53,54 IOG Inferior occipital gyrus

9,10 ORBmid Middle frontal gyrus, orbital part 55,56 FFG Fusiform gyrus

11,12 IFGoperc Inferior frontal gyrus, opercular part 57,58 PoCG Postcentralgyrus

13,14 IFGtriang Inferior frontal gyrus, triangular part 59,60 SPG Superior parietal gyrus

15,16 ORBinf Inferior frontal gyrus, orbital part 61,62 IPLInferior parietal, but supramarginal and angular

gyri

17,18 ROL Rolandic operculum 63,64 SMG Supramarginalgyrus

19,20 SMA Supplementary motor area 65,66 ANG Angular gyrus

21,22 OLF Olfactory cortex 67,68 PCUN Precuneus

23,24 SFGmed Superior frontal gyrus, medial 69,70 PCL Paracentral lobule

25,26ORBsupme

dSuperior frontal gyrus, medial orbital 71,72 CAU Caudate nucleus

27,28 REC Gyrus rectus 73,74 PUT Lenticular nucleus, putamen

29,30 INS Insula 75,76 PAL Lenticular nucleus, pallidum

31,32 ACGAnterior cingulate and

paracingulategyri77,78 THA Thalamus

33,34 DCGMedian cingulate and

paracingulategyri79,80 HES Heschlgyrus

35,36 PCG Posterior cingulate gyrus 81,82 STG Superior temporal gyrus

37,38 HIP Hippocampus 83,84 TPOsup Temporal pole: superior temporal gyrus

39,40 PHG Parahippocampal gyrus 85,86 MTG Middle temporal gyrus

41,42 AMYG Amygdala 87,88TPOmi

dTemporal pole: middle temporal gyrus

43,44 CALCalcarine fissure and surrounding

cortex89,90 ITG Inferior temporal gyrus

45,46 CUN Cuneus

odd number: left hemisphere, even number: right hemisphere.

Table S2. Brain regions showing significant group differences in the nodal efficiency

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between carriers and non-carriers

Brain regions

APOE ε4 Carriers

(Mean±SD)

APOE ε4 Non-

carriers

(Mean±SD)

F-value p-value q-value

Functional network

HIP.L 0.47±0.06 0.54±0.08 12.60 0.00069 0.0155

HIP.R 0.47±0.06 0.55±0.09 10.82 0.00157 0.0283

AMYG.L 0.49±0.06 0.56±0.08 13.74 0.00041 0.0126

AMYG.R 0.50±0.05 0.57±0.08 14.72 0.00027 0.0122

PHG.R 0.51±0.07 0.59±0.08 16.83 0.00010 0.0099

HES.R 0.51±0.05 0.57±0.09 9.92 0.00241 0.0361

White matter network

ACG.L 0.63±0.09 0.71±0.09 12.29 0.00080 0.072

SFGdor.R 0.94±0.15 1.09±0.16 11.34 0.00123 0.0369

PHG.R 0.70±0.07 0.77±0.08 11.81 0.00099 0.0450

IOG.L 0.63±0.07 0.71±0.08 11.20 0.00131 0.0290

The age, gender, and brain size effects were removed in group comparison analyses.

Q-values for the comparison of FDR-corrected.

Table S3. Receiver-Operating-Characteristic analysis of MMSE, functional and

structural networks

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Marker AUC (S.E.) Sensitivity Specificity p-value

MMSE 0.51(0.07) 66% 20% 0.90

Functional network

Global efficiency 0.70(0.06) 80% 58% 0.001

Decreasing region 0.79(0.05) 80% 70% <0.0001

Structural network

Global efficiency 0.74(0.06) 74% 70% <0.0001

Decreasing region 0.81(0.05) 89% 65% <0.0001

The decreasing region represents the mean nodal efficiency of significant decreasing regions in

functional or structural networks at a threshold of p<0.05 (FDR-corrected). AUC= area under the

curve; MMSE= Mini-Mental Status Examination.

Table S4. Pairwise comparison of Receiver-Operating-Characteristic curves

Pairs Difference between areas

(S.E.)

z statistic p-

value

MMSE ~ Global efficiency_FUN 0.19(0.08) 2.15 0.031

MMSE ~ Global efficiency_WM 0.23(0.08) 2.65 0.008

MMSE ~ Decreasing region _FUN 0.28(0.09) 3.24 0.001

MMSE ~ Decreasing region _WM 0.30(0.08) 3.65 <0.001

Global efficiency_FUN~ Global efficiency_WM 0.04(0.09) 0.41 0.68

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Global efficiency_FUN~Decreasing region _FUN 0.09(0.04) 2.13 0.03

Global efficiency_FUN~Decreasing region _WM 0.11(0.08) 1.29 0.20

Global efficiency_WM~ Decreasing region _FUN 0.05(0.08) 0.63 0.53

Global efficiency_WM~ Decreasing region _WM 0.07(0.02) 2.99 0.002

Decreasing_region_FUN~ Decreasing_region_WM 0.02(0.07) 0.23 0.82

S.E.=Standard Error; MMSE=Mini-Mental Status Examination, WM=White matter network,

FUN=Functional network.

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