1

Multimodal Neuroimaging of Corticobasal Syndrome McMillan et al

e-Appendix

Cortical Thickness Imaging Analysis

T1 image preprocessing was performed using a previously reported pipeline implemented in Advanced Normalization Tools (ANTs)1. We first perform N4 bias correction of all images to minimize image inhomogeneity effects that could otherwise yield artificial intensity variation and bias segmentation priors 1,2. We then perform brain extraction using a combination of template-based and segmentation strategies. This involves registering a dilated template brain to each individual subject brain that can then be used to guide brain segmentation from the full MRI volume. We then perform Atropos six-tissue class (cortex, deep grey, brainstem, cerebellum, white matter, and CSF/other) segmentation using an optimized combination of prior knowledge from N4 bias-correction and template-based priors to guide the segmentation process 2,3. We then calculate voxelwise measurements of cortical thickness4. Finally, to make group-level inferences we use a diffeomorphic and symmetric registration algorithm to warp each cortical thickness map to a custom template. The template used in this study, The Penn Neurodegenerative Disease Template, was comprised of 115 controls and 93 neurodegenerative disease patients (PD, AD, amyotrophic lateral sclerosis, and frontotemporal degeneration) who are demographically-comparable to the imaging series in the current study. The resulting cortical thickness images were then downsampled to 2mm isotropic voxels and smoothed using a 4mm FWHM Gaussian kernel.

Diffusion Tensor Imaging Acquisition and Analysis

Diffusion-weighted images were preprocessed using ANTS software. Briefly, the unweighted (b=0) images are first extracted and averaged. All DW images (including the individual b=0 volumes) are then aligned to the average b=0 using ANTs1,3. An affine transform is applied to capture eddy distortion in the DW images as well as motion. Diffusion tensors are computed using a weighted linear least squares algorithm in Camino1,5. The corrected average b=0 image is aligned to the subject's T1 image from the same scanning session, first rigidly to correct for motion, then using a deformable diffeomorphic transformation with mutual information to correct for inter-modality distortion. The diffusion to T1 warp is composed with the T1 to template warp (from the cortical thickness pipeline), producing a mapping from DWI space to the population T1 template in a single interpolation. Tensors are resampled into the template space using log-Euclidean interpolation5,6 and reoriented to preserve the anatomical alignment of white matter tracts6,7. We report fractional anisotropy (FA) that was computed on the tensor image in the group analysis template space. The resulting FA images were smoothed using a 4mm FWHM isotropic Gaussian kernel.

1.Tustison NJ, Cook PA, Klein A, et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. NeuroImage. 2014 Oct 1;99:166–179.

2.Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010 Jun;29(6):1310–1320.

3.Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC. An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics. 2011 Dec;9(4):381–400.

4.Das SR, Avants BB, Grossman M, Gee JC. Registration based cortical thickness measurement. NeuroImage. 2009 Apr 15;45(3):867–879.

5.Cook P, Bai Y, Nedjati-Gilani S. Camino: Open-source diffusion-MRI reconstruction and processing. International Society for Magnetic Resonance Imaging in Medicine. 2006;:2759.

6.Arsigny V, Fillard P, Pennec X, Ayache N. Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magn Reson Med. 2006 Aug;56(2):411–421.

7.Alexander DC, Pierpaoli C, Basser PJ, Gee JC. Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans Med Imaging. 2001 Nov;20(11):1131–1139.

Table e-1. Regions of reduced grey matter density (GM) and reduced fractional anisotropy (FA) in white matter (WM) for CBS-nonAD and CBS-AD relative to healthy controls.

Neuroanatomic Region (Brodmann Area)

L/R

Peak Voxel

MNI Coordinates

X Y Z

Peak Voxel

P-Value

Cluster Size (voxels)

Grey Matter Cortical Thickness: CBS-nonAD < Eld

Inferior parietal (40)

R

60

-36

46

0.001

477

Inferior parietal (40)

L

-50

-44

54

0.001

121

Primary motor (4)

L

-44

-2

50

0.002

140

Supplementary motor (6)

R

40

0

50

0.001

150

Grey Matter Cortical Thickness: CBS-AD < Eld

Inferior parietal (40)

R

56

-34

52

0.001

753

Primary motor (4)

L

-52

-2

50

0.001

535

Ventral striatum

L

-22

-6

-14

0.001

474

Ventral striatum

R

28

-2

-24

0.001

152

Dorsomedial prefrontal (9)

L

-38

44

26

0.001

130

Fusiform (37), middle temporal (21)

L

-46

-48

-14

0.001

7366

Fusiform (37)

L

-32

-60

-16

0.001

654

Fusiform gyrus (37)

R

28

-54

-16

0.001

187

Middle temporal gyrus (21)

R

62

-56

-6

0.001

382

Posterior cingulate (31)

--

-8

-54

28

0.001

683

Extrastriate (19)

R

30

-72

28

0.001

152

White Matter Fractional Anisotropy: CBS-nonAD < Eld

Superior longitudinal fasciculus

L

-53

1

24

0.001

525

Superior longitudinal fasciculus

L

-45

-20

38

0.001

3201

Superior longitudinal fasciculus

R

32

-26

23

0.001

150

Corpus callosum

--

-8

-8

28

0.001

39101

Corpus callosum

--

-7

14

59

0.001

352

Corpus callosum

--

2

24

-3

0.001

507

Corpus callosum

L

-27

10

41

0.001

251

Corpus callosum

L

-12

58

1

0.001

277

Corpus callosum

R

27

-64

26

0.001

1318

Corpus callosum

R

9

29

-17

0.001

442

Corpus callosum

R

16

37

19

0.001

126

Corticospinal tract

L

-8

-27

-43

0.002

111

Cingulum

L

-21

-40

-4

0.001

1014

Middle cerebellar peduncle

R

26

-37

-33

0.001

830

Inferior frontal-occipital fasciculus

L

-37

-55

22

0.001

124

Inferior frontal-occipital fasciculus

L

-45

-32

2

0.001

440

Inferior frontal-occipital fasciculus

L

-33

-14

5

0.001

715

Inferior frontal-occipital fasciculus

L

-40

33

5

0.001

424

Inferior frontal-occipital fasciculus

R

25

5

-13

0.001

203

Inferior frontal-occipital fasciculus

R

37

38

5

0.001

136

Inferior frontal-occipital fasciculus

R

27

42

-8

0.001

786

Inferior longitudinal fasciculus

L

-46

-12

-13

0.002

142

Inferior longitudinal fasciculus

L

-44

-7

-38

0.002

103

Inferior longitudinal fasciculus

R

28

-45

-15

0.001

273

Inferior longitudinal fasciculus

R

48

-9

-7

0.001

4007

Anterior thalamic radiation

L

-2

-7

13

0.001

338

Anterior thalamic radiation

L

-33

11

15

0.001

326

Anterior thalamic radiation

R

4

-7

9

0.001

11338

White Matter Fractional Anisotropy: CBS-AD < Eld

Cingulum

L

-25

-30

-7

0.006

104

Cingulum

R

28

-30

-6

0.003

148

Cingulum

R

28

-23

-21

0.001

349

Corpus callosum

L

-34

-75

22

0.001

145

Corpus callosum

L

-20

-64

30

0.001

632

Corpus callosum

L

-20

-54

16

0.002

113

Corpus callosum

L

-21

-45

35

0.001

1065

Corpus callosum

R

28

-63

28

0.001

128

Globus pallidus / ventral striatum

R

20

-3

-5

0.001

167

Inferior longitudinal fasciculus

L

-45

-8

-33

0.001

561

Inferior longitudinal fasciculus

R

49

-7

-7

0.001

151

Superior longitudinal fasciculus

L

-44

-40

29

0.001

130

Figure e-1. Multimodal neuroimaging of corticobasal syndrome (CBS) patients with evidence of Alzheimer’s disease (CBS-AD) or not (CBS-nonAD) relative to healthy seniors: left images illustrate reduced cortical thickness for CBS-AD relative to controls (red) and CBS-nonAD relative to controls (blue); right images illustrate reduced white matter fractional anisotropy for CBS-AD relative to controls and CBS-nonAD relative to controls (yellow regions) overlayed on red/green/blue (RGB) images illustrating average diffusion direction (red=left/right; green=anterior/posterior; blue=superior/inferior).