Comparing a diffusion tensor and non ten

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NeuroImage: Clinical

journalhomepage: www.elsevier.com/locate/ynicl

1 Comparing a diffusion tensor and non-tensor approach to white matter

2 fiber tractography in chronic stroke

3 a b a a a A.M. Auriat , , M.R. Borich , N.J. Snow , K.P. Wadden , L.A. Boyd *

4 a Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, Canada

5 b Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University School of Medicine, Atlanta, USA

OOF

6 article info

abstract

7 Article history: Diffusion tensor imaging (DTI)-based tractography has been used to demonstrate functionally relevant differ- 18

8 Received 5 November 2014 ences in white matter pathway status after stroke. However, it is now known that the tensor model is insensitive 19

9 Received in revised form 21 February 2015

10 Accepted 11 March 2015 20 to the complex fiber architectures found in the vast majority of voxels in the human brain. The inability to resolve

11 Available online xxxx intra-voxel fiber orientations may have important implications for the utility of standard DTI-based tract recon- 21 struction methods. Intra-voxel fiber orientations can now be identified using novel, tensor-free approaches. 22

12 Keywords: Constrained spherical deconvolution (CSD) is one approach to characterize intra-voxel diffusion behavior. In 23

13 Diffusion weighted imaging the current study, we performed DTI- and CSD-based tract reconstruction of the corticospinal tract (CST) and cor- 24

14 Constrained spherical deconvolution pus callosum (CC) to test the hypothesis that characterization of complex fiber orientations may improve the ro- 25

15 Diffusion tensor imaging bustness of fiber tract reconstruction and increase the sensitivity to identify functionally relevant white matter 26

16 Motor outcome abnormalities in individuals with chronic stroke. Diffusion weighted magnetic resonance imaging was performed 27

17 Stroke in 27 chronic post-stroke participants and 12 healthy controls. Transcallosal pathways and the CST bilaterally 28 were reconstructed using DTI- and CSD-based tractography. Mean fractional anisotropy (FA), apparent diffusion 29 coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD) were calculated across the tracts of interest. 30 The total number and volume of reconstructed tracts was also determined. Diffusion measures were compared 31 between groups (Stroke, Control) and methods (CSD, DTI). The relationship between post-stroke motor behavior 32 and diffusion measures was evaluated. Overall, CSD methods identified more tracts than the DTI-based approach 33 for both CC and CST pathways. Mean FA, ADC, and RD differed between DTI and CSD for CC-mediated tracts. In 34 these tracts, we discovered a difference in FA for the CC between stroke and healthy control groups using CSD 35 but not DTI. CSD identified ipsilesional CST pathways in 9 stroke participants who did not have tracts identified 36 with DTI. Additionally, CSD differentiated between stroke ipsilesional and healthy control non-dominant CST for 37 several measures (number of tracts, tract volume, FA, ADC, and RD) whereas DTI only detected group differences 38 for number of tracts. In the stroke group, motor behavior correlated with fewer diffusion metrics derived from the 39 DTI as compared to CSD-reconstructed ipsilesional CST and CC. CSD is superior to DTI-based tractography in de- 40 tecting differences in diffusion characteristics between the nondominant healthy control and ipsilesional CST. 41 CSD measures of microstructure tissue properties related to more motor outcomes than DTI measures did. Our 42 results suggest the potential utility and functional relevance of characterizing complex fiber organization using 43 tensor-free diffusion modeling approaches to investigate white matter pathways in the brain after stroke.

45 © 2015 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license

46 ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

52 Diffusion-weighted magnetic resonance imaging (DW-MRI) is UNCORRECTED PR MRI-based measures of corpus callosum (CC) ( Borich et al., 2012a ; 58

51 1. Introduction to relate changes in white matter microstructural properties 56 and motor function after stroke ( Jang, 2010 ). Differences in DW- 57

53 a non-invasive imaging technique commonly used to evaluate the mi- Lindenberg et al., 2012 ) and corticospinal tract (CST) ( Borich et al., 59

54 crostructural tissue properties of white matter fiber pathways in the 2014, 2012a ; Lindenberg et al., 2010 ; Stinear et al., 2007 ) microstructur- 60

55 human brain using tractography. DW-MRI has been extensively used al tissue properties are predictive of both motor function and motor 61 learning in individuals with chronic stroke ( Borich et al., 2014 ; 62

* Corresponding author at: University of British Columbia, 212-2177 Wesbrook Mall, Lindenberg et al., 2012 ; Stinear et al., 2007 ). Indeed, recent work has de- 63

Vancouver, British Columbia V6T 2B5, Canada. Tel.: +1 604 822 7392; fax: +1 604 822 scribed DW-MRI-derived measures of white matter microstructural 64 1860.

properties as a more valid predictor of motor function than the func- 65 E-mail address: lara.boyd@ubc.ca (L.A. Boyd).

tional MRI (fMRI)-derived blood oxygen level dependent (BOLD) signal 66

http://dx.doi.org/10.1016/j.nicl.2015.03.007 2213-1582/© 2015 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

2 A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

67 in chronic stroke ( Qiu et al., 2011 ). Moreover, DW-MRI has been touted 2012a ; Stinear et al., 2007 ). We also conducted correlation analyses be- 133

68 as a promising tool for rehabilitation planning and prognosis after tween diffusion measures and the level of physical impairment and 134

69 stroke ( Stinear et al., 2007 ), and these data may predict capacity for motor function in participants with chronic stroke. Our goals were: 135

70 motor learning ( Borich et al., 2014 ). Taken together, DW-MRI has

1) to consider whether CSD and DTI-based tractography differ in the 136

71 been established as both a useful and important non-invasive brain im- ability to detect post-stroke differences in microstructural tissue prop- 137

72 aging technique; therefore, it is critical to ensure that DW-MRI provides erties of white matter tracts, and 2) to determine if the microstructural 138

73 reproducible information that is both sensitive and specific in order to tissue properties of fiber tracts of interest are related to functional out- 139

74 meaningfully inform future clinical decision-making. comes after stroke using both analysis approaches. We hypothesize that 140

75 At present, no “gold standard” DW-MRI-based approach for in vivo CSD will detect a greater number of fibers because it is not restricted to 141

76 fiber tractography exists ( Farquharson et al., 2013 ; Jones, 2008 ;

a single tensor in heterogeneous regions, which includes areas with cross- 142

77 Tournier et al., 2011 ). The most widely-used approach to model white ing fibers; the increased detection of fibers may result in enhanced detec- 143

78 matter diffusion anisotropy is currently diffusion tensor imaging (DTI) tion of group differences and relationships with behavioral outcomes. 144

79 ( Basser et al., 1994 ). Briefly, DTI analysis provides voxelwise estimates

80 of fiber orientation, by generating a single tensor model that can only

2. Methods

81 estimate a single three-dimensional orientation per voxel ( Basser

82 et al., 1994 ; Basser, 1995 ; Basser and Pierpaoli, 1996 ). Therefore, DTI-

2.1. Participants

83 based fiber tract reconstruction relies on a single tensor with single

84 principal orientation representing intra-voxel diffusion behavior

85 Twenty-seven individuals with chronic stroke (for demographics 147

( Basser, 1995 ). As a result of the reliance on a single tensor, DTI is insen- see Table 1 ), and 12 right-handed controls (7 female, mean age 61.2 ± 86 148 sitive to the presence of multiple fibers within a single voxel (e.g., in the

SD 7.6 years) were recruited from the local community. We recruited a 87 149 case of crossing, kissing, merging, or branching fibers) ( Basser et al.,

heterogeneous stroke population, with no targeted site of lesion OOF

2000 ). The result is a fiber tract trajectory that may either not follow ( Fig. 1 ), and a diverse range of impairment (measured by the Fugl- 89 151 its “true” anatomical course or be a non-real, spurious pathway

Meyer Upper Extremity Motor Assessment ( Fugl-Meyer et al., 1975 )). 90 152 ( Tournier et al., 2011 ), which may substantially affect the interpre-

The participants were part of an ongoing study assessing the effects of in- 91 153 tation of results. It is suggested that greater than 90% of white

tervention on long-term recovery after stroke. All imaging and assess- 92 154 matter voxels in the brain contain more than one population of fibers

93 ments in the current study were collected prior to the intervention.

( Jeurissen et al., 2013 ). This issue becomes more complex in the case of 156

94 Informed consent was obtained from each participant in accordance

lesions and neural degeneration, where necrosis, edema, inflammation with the Declaration of Helsinki. The University of British Columbia 95 157 or changes in extracellular matrices may influence diffusion behavior 158

96 (UBC) research ethics board approved all aspects of this study. Partici-

( Pierpaoli et al., 2001 ; Tournier et al., 2011 ). Accordingly, tensor-free pants were excluded if they: 1) were outside the age range of 40–85; 97 159 DW-MRI modeling techniques have been proposed to account for com-

2) were within 0–6 months post-stroke; 3) had a history of seizure/ 160 plex intra-voxel fiber architectures, several of which are more sensitive

epilepsy, head trauma, a major psychiatric diagnosis, neurodegenerative 99 161 than DTI to detecting multiple fiber tract orientations in regions with 100

heterogeneous fiber populations ( Farquharson et al., 2013 ; Tournier 101

et al., 2011 ). 102

One novel method for tensor-free modeling of diffusion behavior is 103

constrained spherical deconvolution (CSD) ( Tournier et al., 2007 ). Brief-

Table 1

t1 : 1

104 Demographic and behavioral characteristics of stroke participants. t1 ly, the DW-MR signal is expressed as an estimate of the fiber orientation 2 :

Time since stroke UE-FM WMFT rate t1 : 3 106

105 distribution (FOD) response function within each voxel ( Tournier et al.,

ID Sex

Age

score (#/min) 107

2007 ) thus providing information regarding the orientations and con-

tributions of various fiber populations to observed diffusion behavior.

S01

F 71 83 56 52.01 t1 : 5 109

The FOD does not loose any information by averaging to obtain a single

S02

t1 tensor, as DTI does. The FOD contains all the orientation information for 6 S03 M 85 35 60 34.49 :

67 82 59 62.95 t1 : a single voxel allowing for multiple fiber orientations to be identified 7

F 66 27 58 38.08 t1 : 9 112

( Tournier et al., 2012 ). Unlike DTI, CSD is robust to the presence of

S06

F 50 37 63 58.16 t1 : 10 113

multiple fiber populations and does not make assumptions regarding

S07

uniform diffusion of water within a voxel ( Farquharson et al., 2013 ;

S08

F 56 27 35 38.43 t1 : 11

55 23.98 t1 : Tournier et al., 2007 12 ). The net result is a DW-MRI technique that is

: more sensitive to multiple intra-voxel fiber pathway trajectories 13

( Tournier et al., 2011 ).

S12

49 34.11 t1 : 16 118

While DTI and CSD have been directly compared in both healthy

S13

64 94 56 46.88 t1 human participants ( 17 Besseling et al., 2012 ; Farquharson et al., 2013 ) :

S14

82 12 59 46.41 and persons with Alzheimer t1 Reijmer et al., 2012 ), the two ap- : 18 3s disease (

S15

proaches have not been compared in persons with stroke. Although the UNCORRECTED PR S17 M 69 15 57 52.99 t1 : 20

61 91 16 11.26 t1 : 19 120

S16

121 reproducibility of DTI has been established in a stroke population

60 23 54 39.90 t1 : 21 122

S18

( Borich et al., 2012b ; Danielian et al., 2010 ), it is unknown how DTI-

S19

62 85 8 9.27 t1 : 22

123 based tractography compares to a CSD-based approach in the brain

after stroke. It is possible that the optimal tractography approach will : S22 M 51 22 16 1.29 t1

F 69 20 11 0.42 t1 : 26 126

differ depending on the specific fiber tracts studied and whether or

S23

58 25 16 3.48 t1 : 27 127

not neuropathology is present. Given these uncertainties, it is important

S24

to directly compare DW-MRI methods for tractography analysis

S25

F 65 21 15 12.33 t1 : 28

55 24 62 59.19 in patient populations, to investigate potential differences between t1 : 29

methods that may influence data interpretation. In the present study

Mean (SD)

we compared DTI- and CSD-based DW-MRI approaches for determinis-

tic streamline tractography to reconstruct white matter fiber tract path- yr, years; mo, months; WMFT, Wolf motor functional test; UE-FM, upper extremity t1 : 32 132

ways, CST and CC, which are important to stroke recovery ( Borich et al.,

portion of the Fugl-Meyer assessment.

t1 : 33

A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

2.2. Magnetic resonance imaging (MRI) acquisition 165

All MR imaging was completed at the UBC 3 T MRI Research Centre 166 with a Philips Achieva 3.0 T whole-body scanner (Philips Healthcare, 167 Andover, MD, USA), using an eight-channel sensitivity encoding head 168 coil (SENSE factor = 2.4) and parallel imaging. All participants received 169

a high-resolution three-dimensional T 1 -weighted anatomical scan 170 (T R = 7.47 ms, T E = 3.65 ms, flip angle θ = 6°, FOV = 256 × 256 mm, 171 160 slices, 1 mm 3 isotropic voxel). A high angular resolution diffusion 172

imaging (HARDI) scan was collected with a single shot echo-planar im- 173 aging (EPI) sequence (T R = 7096 ms, T E = 60 ms, FOV = 224 × 224 mm, 174

70 slices, voxel dimension = 2.2 × 2.2 × 2.2 mm). Diffusion weighting 175 was applied across 60 independent non-collinear orientations (b = 176 700 s/mm 2 ) along with five un-weighted images (b = 0 s/mm 2 ).

2.3. Image processing

Diffusion data were processed using the MATLAB-based (Mathworks, 179 Natick, MA, USA) ExploreDTI software package ( Leemans et al., 2009 ). 180 The DW images were corrected for subject motion and eddy current- 181 induced geometric distortions; signal intensity was modulated and OOF 182 b-matrix was rotated during motion correction ( Leemans and Jones, 183 2009 ). For DTI, the RESTORE approach was used for tensor estimation 184 ( Chang et al., 2012 ). Because the degree of atrophy and lesion size in 185 several stroke participants may have resulted in significant distortions 186 if scans were transformed to standard space, all data were analyzed 187 in each participant 3s native space. See Fig. 2 for an overview of image 188 processing and tractography methods.

2.4. Tractography

Both standard CSD and DTI deterministic streamline tractography 191 were performed for all DW images using the ExploreDTI software pack- 192 age. With DTI the threshold for FA was set at 0.2. For both CSD and DTI 193 maximum turning angle was set at 30°, and the fiber length range of 194 50–500 mm was selected ( Reijmer et al., 2012 ). CSD-based determinis- 195 tic whole-brain fiber tractography was initiated at each voxel using a 196

seedpoint resolution of 2 mm 3 , and 0.2 mm step size ( Reijmer et al., 197 Q4 2012 ). Tractography followed a fiber alignment by continuous tracking 198 (FACT) algorithm approach ( Mori et al., 1999 ).

200 We selected two primary tracts previously shown to be important to 201

2.4.1. Selection of ROIs

stroke recovery: 1) interhemispheric corpus callosum (CC) connections 202 ( Lindenberg et al., 2012 ; Mang et al., 2015 ), and 2) the corticospinal 203 tract (CST) ( Borich et al., 2012a ; Stinear et al., 2007 ). ROIs were 204 delineated manually for all participants by a single experienced rater 205 (K.P.W.). To avoid errors due to the presence of lesions in the stroke par- 206 ticipants DW images, ROIs were drawn for each individual subject in na- 207 tive space; for consistency, identical processing steps were used for the 208

control group. The identical ROI masks were used for the both DTI and UNCORRECTED PR 209

CSD-based tractography approaches. FA color maps for each individual 210 were compared to a FA/white matter atlas ( Oishi et al., 2011 ) to manu- 211 ally delineate corpus callosum and pontine ROIs on midsagittal and 212 axial planes, respectively. Transcallosal tracts were identified with a sin- 213 gle seed ROI placed in the midsagittal section of the CCs ( Fig. 2 , part 2). 214 The CST was independently assessed in each hemisphere with a SEED 215 ROI placed in the mid pons ( Kwon et al., 2011 ) and an AND ROI placed 216

Fig. 1. Lesion pro file of stroke participants. Axial sections have been selected from the site in the posterior limb of the internal capsule (PLIC) ( Borich et al., 2012b ). 217 of lesion core with the lesion identified in red.

These ROIs were selected based on previous work, in which we con- 218 ducted inter and intra rater reliability measurements, and found the 219

162 disorder, or substance abuse; 4) had aphasia (score

greatest specificity for the isolation of the CST ( Borich et al., 2012b ). 220 163

b 13 on the Frenchay

Aphasia Screen) ( Enderby et al., 1987 ); or 5) reported any contraindica- ROIs were manually delineated in the axial plane ( Fig. 2 , part 2). The 221 164

tions to MRI determined by screening by MR technologists. tracts were not confined with any additional restrictions. Fiber tract 222

4 A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

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Fig. 2. Schematic illustrating the diffusion imaging processing pipeline. Placement of ROIs in CC and CST are demonstrated in step 2.

223 reconstruction using the seed ROIs described was completed for both

2.6. Statistical analysis

the diffusion tensor and CSD with a deterministic streamline algorithm. 225

The diffusion-based measures of interest were fractional anisotropy Results are displayed as mean ± SD. Diffusion measures for CC tracts 257 226

(FA), apparent diffusion coefficient (ADC), axial diffusivity (AD), radial were compared using a two-way multivariate analysis of variance 258 227

diffusivity (RD), number of tracts, and tract volume. ADC, AD, and (MANOVA), using Bonferroni correction for multiple comparisons, 259 228

RD are all based on the eigenvalues of the apparent diffusion tensor with the independent factors Group (Control, Stroke) and Method 260 229

(λ 1 ,λ 2 ,λ 3 ( Basser, 1995 )). AD is an indicator of water diffusion along (CSD, DTI). For any significant interaction, additional analyses of vari- 261 230

the parallel, principal, direction of axonal water diffusion (AD = λ 1 ance (ANOVAs) and pairwise comparisons were conducted. For CST 262 231

( Basser, 1995 )). RD is an index of water diffusion perpendicular to the analysis diffusion measures were compared in a three-way MANOVA, 263 232

with Group (Control, Stroke), Method (CSD, DTI) and Hemisphere 264 233

principal direction of water (RD = λ 2 +λ 3 /2 ( Basser, 1995 )). ADC is

the mean value of eigenvalues of the apparent diffusion tensor (Ipsilesional/Nondominant, Contralesional/Dominant) as independent 265 234

factors. When significant interactions were observed, additional ANOVAs 266 235

(ADC = λ 1 +λ 2 +λ 3 /3( Basser, 1995 )). Mean values for FA, ADC,

AD and RD were calculated across all reconstructed fibers for each and pairwise comparisons were performed. An additional planned 267 236

tract of interest. comparison was made for each method (CSD and DTI) between the 268 ipsilesional stroke and non-dominant CST in healthy control for all the 269 diffusion measures (Bonferroni correction for multiple comparisons, 270

2.5. Measures of motor outcome p-value ≤ 0.008 considered significant). To examine the relationship 271 between CSD and DTI based diffusion measures, each measure was com- 272

238 Two licensed physical therapists conducted all functional assess- pared across method with Bivariate Person correlations. Bivariate Pearson 273 239

ments (M.R.B., C.P.). The upper extremity motor portion of the correlation coefficients (r) were calculated between each measure of 274 240

Fugl-Meyer assessment (FM) indexed physical impairment in the motor behavior (WMFT rate and FM score) and the diffusion-based 275 241

hemiparetic arm ( Fugl-Meyer et al., 1975 ). The FM scale contains measures of interest (FA, ADC, AD, RD, tract volume, tract number). For 276 242

33 items scored from 0 to 2, with higher scores indicating less im- all correlations (CSD vs. DTI and behavior vs. diffusion measures) the cor- 277 243

pairment (range of total scores 0–66). This test is clinically used to UNCORRECTED PR relations were considered significant by a p-value corrected for multiple 278

244 assess motor impairment in stroke rehabilitation ( van Wijck et al.,

comparisons of ≤0.008.

2001 ). Motor function in the hemiparetic upper extremity was 246

assessed using the Wolf Motor Function Test (WMFT; Wolf et al.,

3. Results

2001 ). Movement time to complete each of the 15 items in the WMFT 248

with the hemiparetic and non-hemiparetic arms was assessed. The

3.1. CC tractography

time was used to calculate a projected mean rate per minute of task per- 250

formance. For each item on the WMFT the projected task rate was calcu- The midsagittal CC ROI resulted in the identification of transcallosal 282 251

lated as: Task rate = 60 s / performance time (s). If an individual could fiber tracts in all participants for both CSD and DTI (39/39). Sample CC 283 252

not complete the task in 120 s, a mean rate of 0 was given for that task. tracts for a subset of participants are shown in Fig. 3 . MANOVA identi- 284 253

This method of calculating the WMFT is a valid and sensitive measure fied significant main effects of Group (Control, Stroke; p b 0.001) and 285 254

of hemiparetic upper extremity motor function in individuals with Method (DTI, CSD; p b 0.001) and a significant Group × Method interac- 286 255

stroke ( Hodics et al., 2012 ). tion effect (p b 0.001). The CSD method resulted in the identification of 287

A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

OOF

Fig. 3. Subset of stroke participants with axial, coronal and sagittal views of the tracts identified from the CC ROI with both CSD and DTI. The subset of participants was selected for their variety in lesion location, right corona radiata (S03), bilateral diffusely appearing white matter (S06), left posterior corona radiata and pre-central gyrus (S10), and a large left middle cerebral artery infarct (S26).

288 significantly more tracts in the midsagittal CC ROI (8817.74 ± 1580.72; groups. DTI-based fiber tractography resulted in the CST reconstruction 303 289

p b 0.001) than the DTI method (3346.82 ± 1143.64). Tract volume in 67/78 potential tracts. For both approaches unsuccessful fiber tract 304 290

was also significantly greater with the CSD method (306,577.57 ± reconstruction occurred in the ipsilesional hemisphere of participants 305 291

46,750.36 mm 3 ; p b 0.001) than the DTI method (102,202.30 ± in the stroke group. The CSTs identified in all stroke participants are 306 292

25,521.26 mm 3 ). FA, ADC, and RD (p ≤ 0.004) also differed significantly shown in Fig. 4 . There was a significant effect of Group (Control, Stroke; 307 293

between DTI and CSD ( Table 2 ). p b 0.0005), Method (DTI, CSD; p b 0.0005), Hemisphere (Ipsilesional/ 308 294

FA values from controls and stroke participants were significantly Non-dominant, Contralesional/Dominant; p b 0.0005), and significant 309 295

different with CSD (p b 0.001) but not with DTI (p = 0.124; Table 2 ). Group × Method (p = 0.017) and Group × Hemisphere (p = 0.002) 310 296

Number of tracts, ADC, AD, and RD differed significantly between the interaction effects. More tracts were identified with CSD (184.25 ± 311 297

stroke and control groups using both methods (p ≤ 0.0005). However, 160.22) than DTI (121.54 ± 80.45; p b 0.0005), and tract volume was 312 298

3 ; p b 0.0005) than DTI 299 313 (p ≤ 0.0005) but not CSD (p = 0.057). (7226.11 ± 3093.87 mm ). FA, ADC, AD and RD all differed between 314

tract volume differed between control and stroke groups with DTI greater with CSD (12,868.03 ± 7865.26 mm 3

the CSD and DTI (p b 0.0005; Table 3 ).

A smaller number of tracts were generated for the stroke group 316 for both CSD (p b 0.0005) and DTI (p b 0.0005) methods. Mean 317 301

3.2. CST tractography

Fiber tract reconstruction using the CSD approach resulted in suc- tract volume was significantly reduced in the stroke group as com- 318 302

cessful tract reconstruction in 76/78 possible CST tracts across both pared to controls when using CSD (p b 0.0005) and DTI (p = 319

t2 : 1 Table 2 t2 : 2 Diffusion-based measures for stroke and control participants in white matter tracts from the corpus callosum.

UNCORRECTED PR

t2 : 3 CSD-based tractography

DTI-based tractography

Control (SD) t2 : 5 Mean FA

t2 : 4 Stroke (SD)

Control (SD)

Stroke (SD)

0.54 (0.02) t2 : 6 Mean ADC (E 4 ) (mm 2 /s)

8.62# (0.16) t2 : 7 Mean AD (E − 4 ) (mm 2 /s)

14.52* (0.17) t2 : 8 Mean RD (E − 4 ) (mm 2 /s)

5.67@ (0.19) t2 : 9 Number of tracts

4585.17* (639.94) t2 : 10 Tract volume (mm 3 )

t2 : 11 AD, axial diffusivity; ADC, apparent diffusion coefficient; FA, fractional anisotropy; RD, radial diffusivity. For significant differences between stroke and control groups. All measures were t2 Q1 : 12 significantly different between CSD and DTI.

t2 : 13 # p = 0.005. t2 : 14 * p b 0.0005. t2 : 15 @ p = 0.002.

6 A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

OOF

UNCORRECTED PR Fig. 4. Results of CST reconstruction for each stroke participant utilizing CSD and DTI approaches.

320 0.044). Mean FA was significantly reduced in stroke as compared to difference between contralesional hemisphere of the stroke participants 328 321

HC with CSD (p b 0.0005) and DTI (p = 0.020). There are no group and the dominant hemisphere of the controls (p = 0.081). 329 322

by method interactions for ADC (p = 0.337), AD (p = 0.755), or The planned comparison between the ipsilesional stroke CST and the 330 323

RD (p = 0.240). non-dominant CST of the healthy controls with CSD found significant 331 324

Total tract numbers differed between hemispheres in the stroke differences between all diffusion measures (p ≤ 0.001) with the excep- 332 325

group (p b 0.0005) but not in the controls (p = 0.160). There were tion of AD (p = 0.016) after correcting for multiple comparisons. Using 333 326

fewer tracts in the ipsilesional hemisphere of stroke participants than DTI, significant group differences were observed for only tract number 334 327

in the non-dominant hemisphere of controls (p b 0.0005), but no

(p b 0.0005).

A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

t3 : 1 Table 3 t3 : 2 Diffusion-based measures of the CST for stroke and control participants.

t3 : 3 CSD-based tractography

DTI-based tractography

t3 : 4 Stroke

Non-dominant Dominant (SD) t3 : 6 (SD)

t3 : 5 Ipsilesional

Contralesional

Non-dominant

Dominant (SD)

t3 : 7 Mean FA 0.41* (0.06)

0.55 (0.02) t3 : 8 Mean ADC

7.88 (0.13) t3 : 9 (E 4 ) t3 : 10 (mm 2 /s) t3 : 11 Mean AD

13.39 (0.25) t3 : 12 (E − 4 )

t3 : 13 (mm 2 /s) t3 : 14 Mean RD

5.13 (0.16) t3 : 15 (E − 4 )

t3 : 16 (mm 2 /s) t3 : 17 Number of

160.92* (69.2) 156.42 (92.2) t3 : 18 tracts t3 : 19 Tract volume

8811.26* (6192.8) 13,093.75 (7998.2) 19,300.60* (6545.6) 16,046.07 (6379.0) 5450.7 (2955.0) 7474.78 (2951.0) 8626.20 (2760.8) 7984.53 (2764.1) t3 : 20 Significance for comparison between ipsilesional and non-dominant hemispheres with each method. All measures were significantly different between methods: p ≤ 0.015.

t3 : 21 * p

b 0.0005. t3 : 22 # p = 0.001.

OOF

3.3. Functional measures

4. Discussion

337 In the stroke group, there were no significant correlations between This study assessed DTI- and CSD-based tractography in persons 354 338

diffusion measures (i.e., FA, ADC, AD, RD, tract number) for the recon- with chronic stroke and related each of these measures to motor func- 355 339

structed CC tracts and FM score or WMFT rate using DTI ( Table 4 ). FA tion and impairment. CSD reconstructed ipsilesional CSTs for nine 356 340

measures for CC tracts reconstructed with CSD positively correlated stroke participants (33% of the total sample), who did not have identifi- 357 341

with motor function (WMFT rate; Fig. 5 ). The DTI-based measures of able tracts with DTI. Differences in microstructural tissue properties 358 342

FA and RD in the ipsilesional CST correlated with motor impairment (FA, ADC, RD, and tract volume) of CST white matter in chronic stroke 359 343

(FM). However, there were significant correlations between several dif- participants were identified with CSD but not DTI. Additionally, our re- 360 344

fusion measures (FA, ADC, and RD) of the ipsilesional CST fibers identi- sults indicate that post-stroke paretic arm function and impairment 361 345

fied with CSD and both FM and Mean WMFT rate ( Table 5 , Fig. 5 ). level are correlated with a greater number of CSD- than DTI-based 362 ipsilesional CST and CC diffusion measures.

363 Direct lesions to the CST and/or extreme cortical damage resulted in 364

3.4. Correlation between methods the failure of CST detection for several stroke participants. Importantly 365 CSD reconstructed tracts in more individuals with a severely damaged 366

347 For colossal tracts each diffusion measure were strongly correlated CST, which may offer new insights into the neuroanatomical substrates 367 348

between methods (p ≤ 0.003). Correlation plots, Pearson 3s correlation of severe motor impairments after stroke. Additionally, the pattern of 368 349

values, and p-values are shown in Supplementary Fig. 1. Likewise, for cortical fibers identified with CSD resembles known anatomy more 369 350

CST each diffusion measure was correlated between methods (p ≤ closely, where reconstructed fibers are present in both the medial and 370 351

0.001). Correlation plots, Pearson 3s correlation values, and p-values are lateral regions of the primary motor cortex and underlying white matter 371 352

shown in Supplementary Fig. 2. ( Ebeling and Reulen, 1992 ). DTI-based tractography failed to recon- 372 struct fibers projecting to the lateral aspect of the cortex (see Figs. 3 373 and 4 ), which is consistent with previous findings ( Farquharson et al., 374

t4 : 1 Table 4 2013 ; Jones, 2008 ). A previous study in young, healthy individuals 375 t4 : 2 Correlation of diffusion parameters of the CC and upper extremity motor behavior.

also showed more robust results when reconstructing the CST with 376 t4 : 3 DTI-based tractography

CSD compared to DTI ( Farquharson et al., 2013 ). Lateral projections of 377 t4 : 4 Pearson 3s r

CSD-based tractography

the CST play a significant role in motor recovery after stroke ( Hallett 378 Davidoff, t4 379

FM score ), specifically fine motor control of the hand (

p-Value

Pearson 3s r

p-Value

et al., 1998

Number of tracts 380 0.163 0.416 0.103 0.608 1990 ). The detection of these lateral projections with CSD likely contrib- t4 : 7 Tract volume

t4 : 6

t4 : 8 FA UNCORRECTED PR 0.032 0.874 0.476 0.120 motor function. If DW-MRI is to become a feasible tool for assessing 382

uted to the significant correlation between diffusion measures and 381

t4 : 9 ADC − 0.077

prognosis, functional potential, or rehabilitation strategies it is impor- 383 t4 : 10 Ad − 0.296

tant for this technique to be as sensitive and specific to actual white

t4 385 : 12

matter fiber architecture as possible. Inability to detect an intact CST t4 : 13 Mean WMFT Rate

or an under-estimation of the projection of fiber populations may under- 386 t4 : 14 Number of tracts

mine patients 3 expected potential for recovery resulting in minimized 387 t4 : 15 Tract volume

rehabilitation efforts. CSD may be a tool for optimizing tractography strat-

egies by identifying greater extent of fiber projections in important re- 389 t4 : 18 AD − 0.254

gions such as the CC and CST. However, it is difficult to know if and to 390 t4 : 19 RD

t4 : 17 ADC − 0.077

what extent identified tracts may have been contaminated by non-CST 391

tracts. Although, given the strong relationship between motor outcome : 392 20 AD, axial diffusivity; ADC, apparent diffusion coefficient; FA, fractional anisotropy; FM, t4 : 21 Fugl-Meyer; RD, radial diffusivity; WMFT, Wolf motor function test.

t4

and diffusion characteristics of the CST we are confident that the majority 393 t4 : 22 With correction for multiple comparisons p-values are considered significant at ≤0.008.

of the identified tracts were accurately identified. 394

8 A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

-values. p and r

s n3 ar so e

fi cP eci sp r

fo ) (CST 5

e bl Ta d

OOF

an

4 (CC) bl e

Ta

ee ,s n o

la ti corre

nt

ca fi signi *

anisotropy.

fractional and

e s tcom

o u l iora

b ehav

behavioural y

tremit ex

UNCORRECTED PR tw e

upper een

b n o ti la

Corre

5. . Fig

A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

t5 : 1 Table 5 of anatomically known transcallosal fibers (i.e., fibers that extend out to t5 440 : 2 Correlation of diffusion parameters of the CST and upper extremity motor behavior.

lateral cortex) likely contributed to enhanced ability to differentiate 441 t5 : 3 DTI-based tractography

stroke and control participants and to identify a relationship between 442 t5 : 4 Pearson 3s r

CSD-based tractography

p-Value

Pearson 3s r

p-Value

FA and motor function after stroke. To the best of our knowledge, our cur- 443 444

t5 5 FM score rent study is the first to assess post-stroke FA in the entire CC using either

t5 : : 6 Number of tracts

445 t5 : 7 Tract volume

CSD or DTI-based tractography in stroke.

The ability of CSD and DTI to detect differences between health con- 446

trol and post stroke microstructural tissue properties is particularly im- 447 t5 : 9 ADC

t5 Q3 : 8 FA 0.717

portant for stroke recovery research. Differences in mean FA of the 448 t5 : 10 AD − 0.397

ipsilesional CST of stroke participants and the non-dominant CST of

t5 450 : 12

the healthy controls were detected with CSD but not with DTI. It is pos- t5 : 13 Mean WMFT rate

sible that in participants with stroke, CSD was able to identify a greater 451 t5 : 14 Number of tracts

number of tracts with altered microstructural properties, which con- 452 t5 : 15 Tract volume

tributed to the reduced FA values observed. CSD was also able to detect

tracts in several individuals who had no ipsilesional CST detected with 454 t5 : 18 AD − 0.328

t5 : 17 ADC − 0.476

DTI; it is possible that these individuals had CSTs with lower FA relative 455 t5 : 19 RD

to the other participants, and these individuals could have driven the 456 t5 : 20 AD, axial diffusivity; ADC, apparent diffusion coefficient; FA, fractional anisotropy; FM,

detected differences between healthy control and stroke. The reduced 457 t5 : 21 Fugl-Meyer; RD, radial diffusivity; WMFT, Wolf motor function test.

number of participants with tracts detected with DTI likely contributed 458 t5 : 22 * With correction for multiple comparisons p-values are considered significant at ≤0.008.

to the inability to detect a stroke/healthy control group difference, 459 which emphasizes the importance of selecting a tractography method OOF 460

395 The differences identified between the diffusion measures for the capable of detecting tracts in participants with a severely affected CST. 461 396

tracts identified with DTI and CSD support previous findings ( Reijmer For transcallosal tracts, mean ADC, AD, and RD were all increased in 462 397

et al., 2012 ). However, direct comparison between the methods is com- chronic stroke. The magnitude of difference between groups, although 463 398

plicated by the fact that there is no lower FA bound for CSD but the min- still significant, was smaller with DTI. Specific to the comparison of 464 399

imum value for DTI is selected as part of the tractography protocol ipsilesional (stroke) and non-dominant (healthy control) CST, CSD 465 400

(FA N 0.2). CSD uses a tensor-free method, which relies on FOD, to iden- again detected greater ADC, AD, and RD values in chronic stroke partic- 466 401

tify tracts. It is only after tract identification that the tensor is used to cal- ipants; however, DTI failed to detect these differences. ADC represents 467 402

culate the eigenvalues required to determine FA and the other diffusion the overall magnitude of water diffusion ( Basser and Pierpaoli, 1996 ), 468 403

measures. While the differences in FA range impacts differences in dif- and has been extensively studied in individuals with stroke ( Schlaug 469 404

fusion measures between methods several other factors potentially et al., 1997 ; Schwamm et al., 1998 ; Wang et al., 2006 ; Yang et al., 470 405

contribute to the observed method differences. DTI can fail to identify 1999 ; Yoshioka, 2008 ). Consistent with the present study, ADC in the 471 406

tracts in regions with crossing fibers ( Basser et al., 2000 ), resulting in CST appears to be elevated above normal in the chronic stage of stroke 472 407

tract numbers and volumes that are significantly less than those identi- ( Schlaug et al., 1997 ; Schwamm et al., 1998 ), and has been related to 473 408

fied with CSD ( Reijmer et al., 2012 ). Voxels from regions with crossing functional outcomes ( Jang, 2010 ; Schwamm et al., 1998 ). AD and RD 474 409

fibers will have a lower FA values because the diffusion behavior in have been less frequently studied after stroke. Nonetheless, studies 475 410

these white matter regions is less uniform. Thus, CSD tractography pro- using DTI-based tractography of the CST found acute post stroke AD to 476 411

be related to motor outcomes ( Grässel et al., 2010 ; Groisser et al., 477 412

ducing tracts with lower mean FA values may be due to the presence of

a greater number of fiber populations with different architectural char- 2014 ). Recently, one study found increased RD in several regions, in- 478 413

acteristics, such as smaller axonal diameter ( Tournier et al., 2011 ) or cluding the posterior CC, in acute stroke patients compared to controls; 479 414

smaller crossing angles ( Tournier et al., 2008 ) as opposed to differences however, increased AD occurred only in the corona radiata ( Bozzali 480 415

in white matter microstructural tissue properties ( Basser and Pierpaoli, et al., 2012 ). These results are consistent with work by Lindenberg 481 416

1996 ) or myelination ( Song et al., 2002 ). Regardless of the methodolog- et al., who assessed persons with chronic stroke in comparison to con- 482 417

ical differences between the identification of tracts and quantification of trols ( Lindenberg et al., 2012 ). To the best of our knowledge, these mea- 483 418

the diffusion measures, CSD and DTI methods are strongly correlated sures (ADC, AD, RD) have not been assessed in stroke using CSD. Our 484 419

with each other. Which suggests that although the diffusion values results suggest that ADC, AD, and RD are elevated in chronic stroke, 485 420

differ, both methods are measuring similar aspects of microstructural and CSD may prove to be more sensitive to these changes in diffusivity 486 421

tissue properties. than DTI; this observation is consistent with our findings that CSD- 487 422

CSD was able to distinguish differences in callosal FA between con- based FA was better able to differentiate stroke/healthy control groups, 488 423

trol and stroke groups, whereas DTI failed to identify a significant

relative to DTI.

group difference. Previous work utilizing DTI-based tractography de- CSD-based anisotropy (FA) and diffusivity (ADC, RD) in the CST 490 425

tected lower callosal FA in persons with chronic stroke compared to correlated with both motor function (WMFT Rate) and level of motor 491 426

age-matched controls ( Gupta et al., 2006 ). However, this study utilized impairment (FM score) in the paretic arm. Whereas, DTI based anisotro- 492 427

callosal segmentation, and found region-specific reductions in callosal UNCORRECTED PR py (FA) and RD in the CST only correlated with the level of motor im- 493

428 FA (rostrum, genu, rostral body, anterior midbody, and splenium) oc- pairment (FM score) in the paretic arm. The primary observation that 494 429

curred in acute and sub-acute stroke with reductions increasing with CSD and DTI-based anisotropy and diffusion measures of the CST corre- 495 430

time since stroke ( Gupta et al., 2006 ). Borich et al. (2012a) used a late with motor function and impairment is in agreement with existing 496 431

cross-sectional ROI approach to examine FA differences in persons DTI literature ( Borich et al., 2012a ; Lindenberg et al., 2010 ; Schaechter 497 432

with chronic stroke and controls and found that stroke participants et al., 2009 ). Several studies found correlations between DTI-based 498 433

had reduced FA in the sensory sub-region of the CC compared to controls. tractography measures and post-stroke function. For instance Lindenberg 499 434

However, no work to date has examined diffusion behavior in recon- et al. found a correlation between fiber number asymmetry (ipsi − 500 435

structed transcallosal pathways across the entire callosum of individuals contralesional/ipsi + contralesional) and motor outcome in chronic 501 436

with chronic stroke. It is possible that by basing tractography on the entire stroke ( Lindenberg et al., 2010 ). Cho et al. used DTI tractography to 502 437

CC, as opposed to following a parcellation scheme, we were not able to classify CST integrity after corona radiata infarct ( Cho et al., 2007a ) and 503 438

detect a difference between stroke and control participants in callosal intra-cerebral hemorrhage ( Cho et al., 2007b ), and found a relationship 504 439

FA when DTI was used. The ability of CSD to detect a greater proportion between tract involvement and functional outcome. In the current 505

10 A.M. Auriat et al. / NeuroImage: Clinical xxx (2015) xxx–xxx

506 study both DTI and CSD-based tractography in chronic stroke partic- protocol utilized a b-value of 700 s/mm 2 which is lower than the standard 572 507

ipants showed a relationship between ipsilesional CST microstruc- 1200 typically utilized for DWI analysis ( Jeurissen et al., 2013 ). The lower 573 508

tural tissue properties and motor outcomes where more normal b-value reduced our scanning time but may have reduced the ability to 574 509

diffusion behavior was associated with less physical impairment. separate crossing-fibers ( Jeurissen et al., 2013 ); even with this limitation 575 510

However, CSD-based tractography also related to improved motor CSD was still able to detect significant differences between stroke and 576 511

function. control participants which DTI failed to identify. 577 512

In the CCs, only CSD-derived mean FA values were related to motor 513

function. All DTI-derived measures failed to correlate with motor func-

5. Conclusion

tion or impairment. Previous reports on the relationship between CC 515

DW-MRI-related measures and behavioral outcomes utilized segmenta- The current study compared CSD- and DTI-based fiber tractography 579 516

tion of the CC into regions of fiber populations with distinct projections techniques in persons with chronic stroke. Results showed that fiber 580 517

or utilized an ROI or voxel-based approach instead of tractography tractography with CSD can be used to identify functionally-relevant 581 518

( Bozzali et al., 2012 ; Lindenberg et al., 2012 ; Takenobu et al., 2014 ). Ad- white matter tracts in the post-stroke brain, and can be considered in 582 519

ditionally, literature assessing the relationship between CC diffusion mea- future work. CSD-based tractography was able to detect CST fibers in 583 520

sures and motor behavior post-stroke utilized DTI-based tractography. nine more individuals with stroke compared to the DTI-based approach. 584 521

Nevertheless, FA measurements from CC tracts identified using CSD cor- This may be critical when attempting to evaluate neuroanatomical sub- 585 522

related with motor function in chronic stroke participants. strates for recovery from severe CST damage. CSD was better able to 586 523

Several reasons may explain why DTI-derived measures of diffusion distinguish differences in diffusion and anisotropy between stroke- 587 524

metrics of the CST and CC tracts did not relate as well as CSD-derived affected participants and controls. It appears that CSD is useful for 588 525

measures did to motor function or impairment in the current work. studying white matter microstructural properties after stroke. If DW- 589 526

OOF

DTI failed to reconstruct many of the tracts to lateral cortex that were MRI is to continue to be a valuable tool for assessing prognosis or 527

590 identified with CSD, this likely contributed to the lack of significant cor-

predicting potential for motor recovery after stroke, state-of-the-art 591 528

relations between DTI measures and motor function. Additionally, pop- techniques for tract identification should be utilized. 592 529

ulations with stroke tend to have heterogeneous characteristics such as, Supplementary data to this article can be found online at http://dx. 593 530

varied time since stroke onset, wide-range of functional and cognitive

doi.org/10.1016/j.nicl.2015.03.007 .

impairments, and differences in lesion size and location. Many of the 532

previous studies, which have identified a relationship between DTI- 533

based diffusion measures of the CST and functional outcomes, relied 595 534

Uncited references

on homogeneous stroke populations ( Cho et al., 2007a,b ). However, in 535

No citations were found for the following references: Kristo et al. 596 the current study we utilized a heterogeneous stroke population with

variable lesion location, time post-stroke, and level of upper extremity 597 537

(2013) ; Nagesh et al. (1998)

impairment ( Fig. 1 , Table 1 ). In diverse groups of stroke patients, espe- 538

cially with greater levels of impairment represented, CSD may be neces-

Acknowledgements

sary to detect a sufficient number of tracts in order to demonstrate 540

relationships with behavior. These possibilities, should be evaluated The Canadian Institutes of Health Research (CIHR) supported this 599 541

and accounted for in future work to mitigate some of the inherent chal- work (MOP-1066551 to LAB). LAB is a Canada Research Chair and re- 600 542

lenges demonstrated in conducting and comparing research involving ceives support from the Michael Smith Foundation for Health Research 601 543

DW-MRI in stroke, regardless of the tractography technique employed. (MSFHR). AMA receives support from the CIHR and MSFHR. The Natural 602 Q5 544

Our study has some limitations. Primarily, several methods exist for Sciences and Engineering Research Council of Canada and MITACS 603 545

tractography and the comparison of DWI measures. Alternative methods provided support to KPW. Courtney Pollock (CP), a certified physical 604 546

such as probabilistic tractography, which samples many possible fiber therapist, completed a portion of the functional assessments included 605 547

paths, as compared to deterministic tractography, which samples one

in this study.

possible fiber path, may have yielded different results ( Jones, 2008 ). Fre- 549

quently, diffusion measures are extracted from regions of interest, and are

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