Supplementary Information
E1. Validation of Quantitative Imaging Methodology
Quantitative mapping of tissue T1 and T2 relaxation times was performed
using custom designed MRI sequences developed in-house under a research
agreement with Philips Medical Systems.
T1 mapping: Fast T1 mapping was based on the inversion recovery methods
originally published by Ordidge et al [1] and expanded by Clare et al [2]. The
sequence imaged 72 axial slices spanning the brain, which were grouped into
5 consecutive slabs. For each slab a slice selective adiabatic (sech) inversion
pulse was applied to invert magnetisation within a nominal 2.4 cm thick region
and followed by slice selective single shot EPI readout of 12 slices of 2 mm
thickness equally spaced across the region of the slab determined to
experience full inversion. The first slice was imaged 250ms post inversion and
subsequent slices every 205 ms thereafter. During the repetition time of
15000ms each slab was inverted and imaged with a slab order of 1,3,5,2,4 to
minimise interaction between slices. In this way the time between inverting
adjacent slabs was 6000ms, sufficient to allow full relaxation of brain tissue.
The sequence was repeated 12 times and on each repetition the order of
acquired slices within each slab was permuted by one position, for example
1,2,3,4,5,6,7,8,9,10,11,12
2,3,4,5,6,7,8,9,10,11,12,1
3,4,5,6,7,8,9,10,11,12, 1, 2
etc
such that the at the end of acquisitions every slice had been imaged at each
of the 12 inversion times. Total scan time was therefore 180s for the
complete IR series in 72 slices with isotropic 2mm resolution. This sequence
is shown schematically in Figure e1.
Insert Figure e1 here
Calculation of T1 maps used a purpose written algorithm (Matlab) which
reordered the data into incremental inversion order for each slice and then
performed voxel-wise non-linear least squares fitting to the standard 3
parameter model for the inversion recovery experiment:
𝑆(𝑇𝐼𝑅 ) = 𝑆𝑜 (1 − 𝛼𝑒
−𝑇𝐼𝑅
𝑇1 )
Where TIR is the inversion time, T1 is the pixel T1 to be determined from the
data, So is the proton density and  is the effective inversion efficiency (ideally
2 but a free variable in the fit). Images of proton density, T1, inversion
efficiency and the goodness of fit were saved for evaluation.
T2 mapping: Fast T2 mapping used a multi-spin echo sequence with
segmented EPI readout based around a Gradient and Spin Echo Imaging
sequence. The sequence collected 8 spin echoes with equal spacing of 20ms
and 5 gradient recalled echoes (EPI factor 5) to accelerate data collection.
Repetition time was set to 4700ms and 72 slices were collected in standard
interleaved acquisition (2mm isotropic resolution, matrix 128×128). Total
scan time was 120s.
Calculation of T2 maps again used a purpose written algorithm (Matlab) which
performed voxel-wise non-linear least squares fitting to the standard 2
parameter model for transverse relaxation.
𝑆(𝑇𝐸) = 𝑆𝑜 𝑒
−𝑇𝐸
𝑇2
where TE is the echo time, T2 is the pixel T2 to be determined from the data
and So is the proton density. Images of proton density, T2, and the goodness
of fit were saved for evaluation.
Scans were collected using both sequences in aqueous and gel based test
objects of known T1 and T2 and compared to data collection using a single
slice inversion recovery sequence for T1 and a single slice CPMG sequence
for T2 to validate the method.
Quality Assurance: Since MRI data were to be collected over an extended
period (and the study also include a longitudinal phase with 12 month
interscan delay, not reported in this paper) a quality assurance protocol was
implemented. Measurements were made using both T1 and T2 mapping
sequences every 4 weeks in a sealed aqueous gadolinium doped phantom
and relaxation time measurements were found to be stable throughout the
duration of the study.
E2. VBM analysis of grey matter volume
We reproduced the results of grey matter volume reduction in DLB compared
to controls based on T1 weighted structural images, which we have reported
previously using the same method [3]. That is, in the analysis of structural
MRI, modulation and smoothing with an isotropic Gaussian kernel were
applied to structural T1 images although we have used a smaller kernel of
6mm full width at half maximum (FWHM) than the previous study in order to
match the parameters used for qMRI analysis. We have also used age,
gender and the total intracranial volume (the sum of grey matter, white matter
and CSF volume) as covariates in the GLM.
E3. Group differences in grey matter volume
As shown in Figure e2, our finding is consistent with the previously reported
grey matter atrophy [3]. Regions of significant difference (p<0.0001, FWE) in
GM volume were in bilateral hippocampus extending into parahippocampal
gyrus, amygdala, lingual gyrus, superior and middle temporal lobes,
precuneus and posterior cingulate; see Table e1. As expected, there was no
region in the brain where the DLB patients had greater GM volume than the
controls.
Insert Figure e2 here
Insert Table e1 here
E4. Comparing grey matter lose with quantitative MRI
In comparison to the relatively distributed GM volume reduction, we have
found a different pattern of tissue changes using quantitative MRI. That is, the
regions of microstructural abnormality are more focal in DLB. Moreover, brain
areas that are affected by tissue alterations only partly overlap with regions
where atrophy was found, i.e. in hippocampal areas and bilateral
superior/middle temporal lobes. In other regions where qT1 and qT2 were
different between the DLB and control groups, we did not find significant
atrophy. Thus, we argue that quantitative MRI parameters reflect specific
pathological markers for DLB that may not be suggested by volume reduction.
In particular, we did not find a significant volume change in subcortical
regions, except in amygdala, but qT1 and qT2 are sensitive to subtle changes
in tissue properties in putamen and caudate. The relative sparing of GM
volume reduction in these subcortical regions as well as in the cortex
comparing to AD suggests that DLB is a disease that is predominantly caused
by synaptic and neurochemical dysfunction and tissue microstructure
abnormalities rather than neuronal loss.
E5. Discussions of partial volume effect
In normal brain tissue, the longitudinal relaxation time qT1s are around 800
ms in white matter, 1100 -1500 ms in grey matter and above 1500 ms in CSF;
while the transverse relaxation time qT2s are around 70 ms in white matter,
80 ms in grey matter and much higher, i.e. above 500 ms, in CSF [4].
Due to the potential confound of partial volume effect, in all studies using
quantitative measures of MRI, consideration should be taken when the design
involve patients with potential atrophy. In our current study, we think it is
unlikely that the decreases of qT1 or qT2 parameters were caused by partial
volume effect because grey matter reduction, which predominantly leads to
the expansion of the CSF, would increase rather than decrease the T1 or T2
relaxation time. As previously discussed, qT1s and qT2s are higher in CSF
than in grey matter, thus, partial volume effect will only introduce an increase
in quantitative MRI images. However, it is still possible that our results
showing increased qT2 values for the DLB group were caused by partial
volume effect. Hence, in our data analysis, we have undertaken a multimodal
approach to minimize partial volume effect based on the standard voxelbased quantification (VBQ) method [5]. In particular, the tissue types were
classified using high-resolution T1 weighted structural images, and then the
resulting GM probability map was used to mask the quantitative MR images.
This procedure makes sure that the subsequent analysis is only applied to the
voxels with high likelihood of being grey matter rather than CSF.
In addition, we retain the qT1 and qT2 images in their native space at as
many processing steps as possible before applying spatial normalization and
smoothing for group statistics. This maximizes the accuracy of co-registration,
which is a critical part of the VBM method. As previously discussed, we have
shown that areas with alternation of microstructure revealed by quantitative
MRI parameters are not correlated with areas of atrophy in DLB. This pattern
of tissue changes further confirms that the differences between DLB and
controls groups in qT1 and qT2 measures are unlikely to be caused by partial
volume effect.
References
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