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Correlation of microstructure di erences in di usion MRI scans with Fugl-Meyer assessment
scores in stroke subjects
Kyler Hodgson , Ganesh Adluru , Lorie Richards , Jennifer Majersik , and Edward DiBella
Biomedical Engineering, University of Utah, Salt Lake City, UT, United States, Radiology and Imaging Sciences, University of Utah, Occupational Therapy,
University of Utah, Salt Lake City, UT, United States, Neurology, University of Utah, Radiology and Imaging Sciences, Biomedical Engineering, University of
Utah, Salt Lake City, UT, United States
Synopsis
Improved characterization of brain microstructure is important for image-based methods for diagnosing stroke. We explored the extent to which
microstructural maps including Fractional Anisotropy (FA), Generalized Fractional Anisotropy (GFA), and Neurite Orientation Dispersion Density Index
(NODDI) detect ipsilateral and contralateral di erences in stroke patients as a measure of stroke severity. The di erence between hemispheres was
correlated with Fugl-Meyer Assessment motor function scores and the results of 16 patient scans reported. Results suggest that the Orientation
Dispersion Index (ODI) contains information that could be clinically useful in understanding stroke recovery.
Purpose
Understanding microstructure di erences could be important in the diagnosis and prognosis of stroke. We seek to understand whether certain methods
of computing microstructure maps yield information that can be used to predict stroke outcomes. In particular, we wanted to understand whether
di usion spectrum imaging (DSI) based NODDI models could potentially have more sensitivity to microstructure damage following stroke than standard
di usion tensor imaging (DTI) methods such as FA and GFA. Determining the correlation between an image-based measure of severity and clinical motor
assessments is a positive step towards image-based stroke prognosis for motor ischemic stroke.
Methods
Acquisition and Clinical Assessment of Stroke Severity
16 data sets of motor ischemic stroke subjects were acquired by DSI with a b-max of 4000 and 203 directions on a Siemens 3T Verio scanner using a 32
channel head coil. A simultaneous multi-slice blipped CAIPI sequence was used with a slice acceleration factor of three. The 16 scans included 9 baseline
scans, 5 scans at 1 month time point, and 2 scans at 3 month time point. Following each scan, a Fugl-Meyer assessment (FM) of motor function was
performed with higher scores representing better motor control. The assessment includes scores for Upper Extremity (UE) and Lower Extremity (LE) and
a composite which is the sum of the two .
Processing
Segmentation of stroke regions was performed manually using ITK-Snap software. FA, GFA , and two versions of NODDI maps were computed: the
slower original NODDI method , and the faster Accelerated Microstructure Imaging via Convex Optimization (AMICO) NODDI method (Fig 1).
Analysis
For each stroke segmentation, a re ection onto the contralateral hemisphere was calculated. The symmetric Kullback-Leibler Divergence (sKLD) was
calculated between the ipsilateral stroke region and its contralateral re ection (Fig 2). The correlation coe cient was calculated for FM with the
ipsilesional and contralesional means. Correlation was also found for the inverse of each FM component, 1/FM, 1/UE, and 1/LE, with the sKLD and stroke
volume. Inverse FM scores provide a better relation because sKLD is non-linear measure of the distance between distributions. Lastly, the sKLD values
were scaled by multiplying each sKLD by the volume of the stroke region and again correlated with 1/FM, 1/UE, and 1/LE .
Results
The highest correlation was found for Slow NODDI ODI sKLDs with 1/FM and 1/UE (Table 1). Consistent with the correlation results, paired t-tests for the
mean value in the ipsilateral and contralateral stroke regions showed that only for the NODDI ODI maps were the mean values signi cantly di erent
between hemispheres (original p=0.00089, AMICO p=0.0016). Correlation with FM for ipsilesional mean values were below 0.3, but correlation of
contralesional mean values in FA and RDI was above 0.6 (Table 1). Volume-scaling sKLD values raised the correlation of GFA and CSF microstructure
maps 30-60% (Fig 3).
Discussion
Unexpectedly, the highest correlation for non-volume scaled sKLDs with 1/FM and signi cantly di erent means between ipsilateral and contralateral
regions, were found in the Slow ODI map. ODI is focused on the regions outside of neurons and seeks to characterize ber directions by examining the
extra-cellular space and capturing the anisotropy of these regions which is not considered in strictly white matter FA and GFA maps . This suggests,
and agrees with other studies, that white matter analysis alone yields an incomplete picture . The RDI and FA maps, had similar values for correlation
with composite FM scores and UE scores. The fact that the correlation of GFA was so much lower than all the other maps, including the NODDI CSF maps,
was surprising because it is supposedly an improved measure of white matter. Our ndings thus far also align with the general nding by Granziera et
al , that contralesional means correlate with clinical assessments.
Conclusion
1 2 3 4 5
1 2 3
4 5
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The increase in correlation in the GFA and CSF maps after scaling by volume could be a useful tool for stroke analysis. Moreover, the relatively
unchanged, and high correlation of ODI and RDI maps following volume scaling, suggest that they may be sensitive to microstructure changes following
stroke not present in the other models. While volume itself is not a predictor of stroke severity , volume scaling the sKLD had a notable e ect. The
approach may lead to a more sophisticated measure that takes into account di erences between brain hemispheres and is sensitive to volume
depending on lesion location.
This study will continue to enroll subjects and validate the results obtained thus far. Time point
analysis will be done to determine whether DSI models such as NODDI better capture
ipsilesional and contralesional di erences, and whether these di erences show trends that
could be used in clinical prognosis for ischemic stroke subjects.
Acknowledgements
This work is supported by R01NS083761.
References
[1] Kuo et al., Neuroimage, 41:7-18, 2008. [2] https://www.cmrr.umn.edu/multiband/ [3] Sullivan et al., Stroke
42:427-432, 2011 [4] http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ [5] Zhang et al., Neuroimage, 61:1000-
16, 2012 [6] http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab [7] Daducci et al.,
Neuroimage, 105:32-44, 2015 [8] https://github.com/daducci/AMICO [9] Adluru et al., IEEE
EMBC, 36:742-745, 2014 [10] https://cran.r-project.org/web/packages/entropy/index.html [11]
Gauthier et al., Stroke. 39(5): 1520–1525, 2008. [12] Granziera et al., Int Soc Magn Reson Med, p.
4199, 2011. [13] Granziera et al., Neurology, vol. 79, pp. 39-46, 2012. [14] Puig, American Journal
of Neuroradiology, vol. 32, no. 5, pp. 857–863, Jul. 2011.
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Figures
Figure 1- Example scan slices from one stroke patient for each DTI and DSI map with stroke region indicated by yellow arrow. The slower original NODDI
is on the top row and the faster AMICO NODDI is on the second row. Both NODDI methods result in 3 microstructure maps: 1) Restricted Di usion Index
(RDI) 2) Cerebrospinal Fluid (CSF) and Orientation Dispersion Index (ODI). The scan parameters were TR=3.7 sec, TE=114.2 msec, number of slices=51,
slice thickness=2.1mm, total data acquisition time = 13 minutes.

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Trace images from Siemens 3T Verio scanner were used to draw the ipsilateral stroke region (seen as bright region on left side of image). A bisection line
was then drawn down the center of the brain as the axis of re ection. A di erence between ipsilesional and contralesional areas is visually apparent.
Below each brain image is the corresponding probability distribution of intensity values (di erence scales for Trace and ODI) in the ipsilateral and
contralateral stroke areas. These distributions used to calculate the sKLD for each model.
Figure 3 - sKLD plotted as function of 1/FM and 1/UE. The left two plots show that after scaling, correlation in the CSF map exceeds that of the original
unscaled original NODDI ODI map (as well as the scaled ODI map). The right two plots show the dramatic di erence before and after scaling by volume
for GFA. While the r values are not exceptionally high, considering the sources of possible error, such as, scan day vs FM day, manual segmentation,
existing di erences between brain hemispheres, multiple time points, etc., they are high enough to warrant optimism for future work.
Table 1- Correlation coe cients for 1/FM and 1/UE with sKLD for each microstructure map and with volume of stroke region. One expects that as stroke
severity increases, the sKLD between ipsilateral and contralateral sides of the brain will increase. The lower the FM score, the more severe the stroke.
Correlation coe cient values for LE were 0±0.12 and therefore not considered noteworthy. The correlation with FM and contralesional mean is relatively
strong in FA and RDI. No correlation is evident with ipsilesional means.
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