Nahla M H Elsaid1,2, Pierrick Coupé3,4, Andrew J Saykin1,2, and Yu-Chien Wu1,2
1Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States, 2Indiana Alzheimer Disease Center, Indiana University, Indianapolis, IN, United States, 3LaBRI, UMR 5800, University of Bordeaux, Talence, France, 4LaBRI, UMR 5800, PICTURA, F-33400, CNRS, Talence, France
Synopsis
The aging process is
known to cause morphological and structural alterations in the human brain. Using a sub-millimeter super-resolution hybrid
diffusion imaging (HYDI), we
studied the effects of aging on the structural connectivity between the
hippocampal subfields as well as between the hippocampus and the cerebral
cortex.
Introduction
The recent advancements in high-resolution anatomical magnetic resonance imaging (MRI) such as T1-weighted (T1W) or T2-weighted (T2W) imaging have enabled the feasibility of submillimeter in-plane resolution of the hippocampus, which has allowed the automatic segmentation of the hippocampal subfields1,2. Using anatomical MRI, most of the previous studies focused on the volumetric changes in the hippocampal subfields3,4. However, changes in specific hippocampal fiber pathways within the brain’s structural networks have not been closely investigated, largely due to the limited spatial resolution in diffusion MRI (dMRI). With an adequate spatial resolution, dMRI could provide microstructural measurements and the associated tractography for the in-vivo human hippocampal subfields. In this study, we used super-resolution hybrid diffusion imaging (HYDI) to study the effects of aging on diffusion metrics in the hippocampal subfields and on structural connectivity between the subfields.Methods
Patients: Twelve healthy participants
were recruited from the Indiana Alzheimer Disease Center and communities. There
were five participants with an age larger than 65 years old (73 +/- 9.9 years) and
the remaining seven participants with an age less than 35 years old (28.6 +/-
4.2 years). Each group had two male
participants. Imaging: MRI scans were
performed on the participants in a Siemens MAGNETOM Prisma 3.0 T scanner. High-resolution T1W
images were acquired with an MPRAGE sequence at 0.8 x 0.8 x 0.8 mm3
resolution, TR=2400 ms, TE=2.22 ms, TI=1000 ms, flip-angle=8°, 256 mm field-of-view, 208 slices, and GRAPPA
acceleration iPAT= 2. A high-resolution
Turbo-Spin-Echo T2W images were acquired with a high
in-plane resolution of 0.4 x 0.4 mm in an
oblique plane perpendicular to the main axis of the hippocampus, a slice
thickness of 2 mm, TR=8310 ms, TE=50 ms, flip-angle=122°, 175 mm field-of-view,
32 slices, and GRAPPA acceleration iPAT=2. HYDI5,6
was performed with a single-shot spin-echo
EPI sequence, a multiband factor of 3, TE=74.2 ms, and TR=4164 ms, 220 mm field
of view, 114 slices, isotropic resolution of 1.25mm, and 10:51 (min: sec) acquisition time. The diffusion scheme
included four shells of b-values 500, 800, 1600, 2600 s/mm2, a total of 134 diffusion-directions, and 8 non-diffusion weighted volumes. Two sets of
data were acquired with reversed
phase-encode blips. Preprocessing: All the DW-images were denoised
from Rician noise using overcomplete local-Principal-Component-Analysis as proposed in7.
FSL-topup and FSL-eddy8, part of the FSL package version
6.0.0 (FMRIB, Oxford, UK), were used to
correct motion, susceptibility, and eddy current distortions. To achieve super-resolution, a collaborative-patch-based-method9 was then used to bring the 1.25 mm3 resolution HYDI data to a submillimeter resolution of 0.625 mm3. This submillimeter diffusion
data
was then used to compute diffusion tensor imaging (DTI) metrics and neurite density and orientation dispersion
imaging (NODDI) metrics10. Post-processing: Figure 1 shows the workflow of the
postprocessing. The high-resolution T1W
and T2W images were used for segmenting
the hippocampal subfields with the Automatic Segmentation of Hippocampal
Subfields (ASHS) method2. The hippocampal subfields were transformed from
the T2W image space to the super-resolution diffusion space using ANTs11.
In parallel,
the HYDI multi-shell data was processed for tractography with generalized
q-sampling imaging (GQI) approach12 and the
hippocampal subfields as seed regions-of-interest (ROIs). Connectivity matrices (i.e., connectomes) were
computed between the subfields and between the hippocampus and the cerebral
cortices as in normalized streamline counts. Connectometry statistical analysis: To study the effect of aging on the hippocampus-cortex
streamline connections, a group-wise connectometry analysis was used13,14. In this analysis, the diffusion data were reconstructed in the MNI
space using q-space diffeomorphic reconstruction15
to obtain the spin distribution function12.
The output resolution of the resampling to the
MNI space is 1mm-isotropic. The quantitative anisotropy (QA)12 was extracted as
the local connectome fingerprint14
and used in the connectometry analysis. The analysis used the MNI-space
hippocampal regions16
as ROIs.Results
Figure
2 shows the diffusion metrics of each of the hippocampal subfields, including
the Cornu-Ammonis subfields (CA1-3), dentate gyrus (DG), entorhinal cortex
(ERC) and subiculum (SUB). The older participants had significantly
lower DTI fractional anisotropy in the CA1, DG and CA3 (i.e., Welch two-sample t-test p-value < 0.05, without
multiple-comparison correction). While
not significant, a trend of lower NODDI intracellular volume fraction (ICVF)
was observed in older participants. Figure
3a shows the connectivity matrices computed using the number of streamlines
normalized by the volume of the subfields involved in each connection. Connectivity matrices were computed in the
left and right hippocampal subfields in the older and younger groups; Figure 3b
shows the non-zero connectivity vectors from the left and right hippocampi to cortical regions for both age groups.
Figures 3,4 show a significantly weaker connection in the older participants, especially
in the left hippocampus. The connectometry analysis depicted by Figure 5, shows that the QA was negatively correlated with the subjects’ age in
each of the MNI-hippocampal ROIs with a false discovery
rate (FDR) that ranged between 0.0075 and 0.01.Discussion and Conclusion
Our study suggests that age decreases
the structural organization inside the hippocampal subfields as well as along
the fiber tracks between the hippocampal subfields and the cerebral cortices
with decreased FA and QA. This method
holds promise for providing information regarding hippocampal integrity in
Alzheimer’s disease.
Acknowledgements
This work is supported by grant NIH NIA R01
AG053993.References
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