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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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