Disrupted Functional and Structural Networks in Cognitively Normal Elderly Subjects With

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

1 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

2 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

3 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:

1 1 Eglob = N( N- 1) i刮 j G Lij

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

4 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:

1 Eloc= E glob( G i ) N i G

whereGi 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:

1 1 Enodal ( i ) = N-1 i刮 j G Lij

where Lij 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

7 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

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

p<0.001.

9 Figure S3. Receiver operating characteristic curves for MMSE, Global efficiency_FUN, Global efficiency_WM, Decreasing_region_FUN, and Decreasing_region_WM.

10 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) and Backward digit span (C) performances. *=Significant group difference at p<0.05; **= Significant group difference at p<0.001.

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

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.

12 Supplementary Tables Table S1. Parcellation of 90 AAL cortical and subcortical regions

Index Abbr. Regions Index Abbr. Regions

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 IPL Inferior 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

25,26 ORBsupmed Superior 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 ACG Anterior cingulate and paracingulategyri 77,78 THA Thalamus

33,34 DCG Median cingulate and paracingulategyri 79,80 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 87,88 TPOmid Temporal pole: middle temporal gyrus

43,44 CAL Calcarine fissure and surrounding cortex 89,90 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 between carriers and non-carriers

13 Brain regions APOE ε4 Carriers APOE ε4 Non-carriers F-value p-value q-value (Mean±SD) (Mean±SD) 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

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

MMSE 0.51(0.07) 66% 20% 0.90

Functional network

14 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 z statistic p-value

(S.E.) 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

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

15 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.