Tina Jeon1, Aristeidis Sotiras2, Minhui Ouyang1, Min Chen3, Lina Chalak4, Christos Davatzikos2, and Hao Huang1,5
1Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States, 2Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States, 3Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, United States, 4Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, United States, 5Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
Synopsis
From early 3rd trimester
to around birth, the cerebral cortex undergoes dramatic microstructural changes
including dendritic arborization that disrupts the radial scaffold, a well-organized
columnar organization. Decrease of cortical fractional anisotropy (FA) derived
from DTI has been well documented. In this study, we hypothesized that non-Gaussian
water diffusion properties (e.g. mean kurtosis or MK) from diffusion kurtosis
imaging (DKI) offers unique and complementary information on cortical
microstructural changes during this period. The spatiotemporal changes and
patterns of cortical FA and MK from 32 to 41 postmenstrual weeks were revealed,
demonstrating unique cortical MK maps and clustering patterns during preterm
development.Purpose
From the early 3rd trimester
to around birth, the cerebral cortex undergoes dramatic microstructural changes
including dendritic arborization that disrupts the radial scaffold, a well-organized
columnar organization. Decrease of cortical fractional anisotropy (FA) derived
from DTI has been well documented [1-3]. In this study, we hypothesized that
non-Gaussian water diffusion properties (e.g. mean kurtosis or MK) from
diffusion kurtosis imaging (DKI) [4-5] offer unique and complementary
information on cortical microstructural changes during this period. The
spatiotemporal changes and patterns of cortical FA and MK from 32 to 41
postmenstrual weeks (PMW) were revealed. Specifically, distinctive cortical MK
and FA maps at each PMW during preterm development were characterized. Cortical
MK and FA clustering patterns were revealed. The maturational curves of gyral
level cortical MK and FA were also delineated to test the hypothesis.
Methods
Subjects and data
acquisition: 76
normal preterm and term neonates (47 Male and 29 Female; gestational ages of 32
to 42 PMW; 37.1±2.5 PMW) were recruited and scanned. Diffusion weighted images
(DWIs) were acquired from a 3T Philips Achieva system with following imaging
parameters: single-shot EPI sequence (SENSE factor = 2.5) without sedation, FOV=168/168/96mm, imaging matrix=112x112, axial
slice thickness=1.6mm without gap, 30 directions; b-values=1000 and 1600 s/mm
2,
repetitions=2, imaging time=18minutes. Of the 76 subjects, 27 (20 Male and 7 Female; 36.7±2.4 PMW)
were scanned with two high b-values (1000 and 1600 s/mm
2).
Kurtosis and tensor fitting: The tensor
fitting was conducted with DWI of b=1000s/mm
2 after motion and
distortion correction to obtain the FA map. After DWIs of b=1600 s/mm
2
were corrected for motion and distortion, kurtosis was fitted using in-house
software in MATLAB to obtain the MK map.
Extraction
of the cortical skeleton and measuring cortical FA and MK at the core of
cerebral cortex: With TBSS of FSL
(http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS), cortical skeletons were extracted.
Cortical tissue probability map was obtained based on the contrast of the MD
map. At a given cortical skeleton voxel, the offset between the cortical
skeleton and the core of the cerebral cortex was corrected by projecting the FA
(or MK) from surrounding voxels with the highest gray matter probability to the
cortical skeletons [3]. The FA and MK at the core of the cerebral cortex were averaged
for all subjects at the same PMW and mapped to a template cortical surface. Measuring gyral level FA and MK at the
cortical skeleton: The 56 gray matter regions of the JHU neonate atlas
[6] were transformed to the cortical skeleton of each subject to measure the gyral
level cortical FA and MK.
Clustering
of cortical MK and FA: Non-negative matrix factorization (NNMF) was
conducted on the MK and FA measured at the cortical skeleton with the number of
clusters set to 4 [7].
Results
As shown in Fig. 1 and Fig. 2, decreases of both cortical
MK and FA from 32 to 41 PMW can be observed during the 3rd trimester.
However, the averaged cortical MK map is distinctive from the averaged FA map
at the same age. For example, highest MK (Fig. 1a) is located at occipital
region while highest FA (Fig. 2a) is located at prefrontal region at 33 PMW.
The MK cluster distribution (Fig. 1b-1c) is also different from FA cluster
distribution (Fig. 2b-2c). Compared to FA clusters, more separation in MK
clusters can be appreciated. In Fig. 3,
FA is best modeled with a biphasic piecewise
linear fitting with a significant FA decrease from
32 to 37 PMW and relatively flat FA from 37 to 42 PMW. Significant age-dependent
MK decreases were found at the precentral and postcentral gyrus, while there
was no significant change in FA for these cortical areas.
Discussion and Conclusion
The cortical MK and FA maps are distinctive at the
same age, as well as MK and FA maturational curves for the same cortical
region. In addition, decrease patterns of cortical MK are different from those
of cortical FA with MK clusters more clearly separated. These findings
indicated that unique and complementary information on cortical
microstructural changes can be offered by cortical MK measurements during this
period. Cortical FA is sensitive to cellular processes
such as dendritic arborization and disruption of radial glial scaffold [e.g.
1-3] which take place early in primary motor (precentral gyrus in Fig. 3a) and
somatosensory (postcentral gyrus in Fig. 3b) regions, resulting in lower FA
values in these regions as early as around 32PMW. Significant MK decreases all
over the brain may be related to continuous decrease of diffusion barriers
possibly associated with continuous decrease of neuronal density from 32 to 42PMW
[8].
Acknowledgements
This study is sponsored by NIH MH092535 and NIH
MH092535-S1 and U54HD086984.References
[1] McKinstry et al (2002)
Cereb Cortex 12:1237. [2] Huang et al (2013) Cereb Cortex 23: 2620. [3] Ball et
al (2013) PNAS 110: 9541. [4] Jensen et al (2005) MRM
53: 1432. [5] Cheung et al (2009)
Neuroimage 45:386. [6] Oishi et al (2011) Neuroimage 56:8. [7] Sotiras et al
(2015) Neuroimage 108: 1. [8] Huttenlocher
(1990) Neuropsychologia 28: 517.