Untangling microstructural development: insights from soma, neurite, and myelin imaging on macaques
Poster No:
1315
Submission Type:
Abstract Submission
Authors:
Ting Gong1, Evan Dann1, Chiara Maffei1, Hong-Hsi Lee1, Hansol Lee1, Jocelyne Bachevalierb2, Susie Huang1, Suzanne Haber3,4, Anastasia Yendiki1
Institutions:
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 2Emory National Primate Research Center, Emory University, Atlanta, GA, 3Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, 4McLean Hospital, Belmont, MA
First Author:
Ting Gong
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Co-Author(s):
Evan Dann
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Chiara Maffei
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Hong-Hsi Lee, MD PhD
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Hansol Lee, PhD
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Susie Huang, MD PhD
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Suzanne Haber
Department of Pharmacology and Physiology, University of Rochester|McLean Hospital
Rochester, NY|Belmont, MA
Department of Pharmacology and Physiology, University of Rochester|McLean Hospital
Rochester, NY|Belmont, MA
Anastasia Yendiki
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Introduction:
Early brain development involves dynamic biological processes, such as synaptogenesis and myelination, that shape cellular organization and are essential for normal brain function. Current methods used in developmental studies lack specificity to cellular microstructure. Thus, their findings are hard to interpret, especially in the gray matter (GM). In this study, we use advanced biophysical models to measure changes of cell soma density, neurite density and myelin concentration in developmental brains. Our goal is to validate biomarkers of major biological processes that are relevant to studies of healthy and disordered brain development.
Methods:
Five fixed macaque brains, three developmental (3-week, 3-month, and 1-year old) and two adult, were scanned in a small-bore 4.7 T Bruker BioSpin MRI system.
Quantification of cell soma and neurites. Soma and neurite density imaging (SANDI) [1] models diffusion weighted (DW) signals in a voxel from within the soma (cell bodies of neurons and neuroglia), the neurite (dendritic processes and axons) and the extra-cellular water. This provides histology-correlated estimates of apparent soma density, soma size and neurite density [2]. For SANDI analysis, DW images were collected using a two-shot, 3D echo-planar imaging sequence at 0.5 mm isotropic resolution; DW gradient directions were uniformly sampled over the hemisphere for each of 8 shells: 12 directions for b=1, 2.5, 5, 7.5 and 32 directions for b=11.1, 18.1, 25, 43 ms/μm2. The diffusion gradient pulse duration and separation was 11 and 15 ms.
We also use a subset of the DWI datasets to estimate FA and MD from conventional DTI [3] and neurite density index (NDI) and orientation dispersion index (ODI) from NODDI model [4], which has been widely studied in mainly white matter (WM) development [5]–[7].
Quantification of myelin and R2. Commonly used myelin measures include T1w/T2w ratio [8], R1 [9], [10], R2 and more specific myelin water fraction (MWF) [11]. For our ex vivo data, we collected multi-slice multi-echo images for quantitative R2 mapping and MWF estimation (20 spin echo images with echo times from 8-160 ms and an equal echo spacing of 8 ms; TR = 3000 ms). The data were fitted for voxel-wise R2 values as well as a spectrum of T2 to extract the MWF.
Quantification of cell soma and neurites. Soma and neurite density imaging (SANDI) [1] models diffusion weighted (DW) signals in a voxel from within the soma (cell bodies of neurons and neuroglia), the neurite (dendritic processes and axons) and the extra-cellular water. This provides histology-correlated estimates of apparent soma density, soma size and neurite density [2]. For SANDI analysis, DW images were collected using a two-shot, 3D echo-planar imaging sequence at 0.5 mm isotropic resolution; DW gradient directions were uniformly sampled over the hemisphere for each of 8 shells: 12 directions for b=1, 2.5, 5, 7.5 and 32 directions for b=11.1, 18.1, 25, 43 ms/μm2. The diffusion gradient pulse duration and separation was 11 and 15 ms.
We also use a subset of the DWI datasets to estimate FA and MD from conventional DTI [3] and neurite density index (NDI) and orientation dispersion index (ODI) from NODDI model [4], which has been widely studied in mainly white matter (WM) development [5]–[7].
Quantification of myelin and R2. Commonly used myelin measures include T1w/T2w ratio [8], R1 [9], [10], R2 and more specific myelin water fraction (MWF) [11]. For our ex vivo data, we collected multi-slice multi-echo images for quantitative R2 mapping and MWF estimation (20 spin echo images with echo times from 8-160 ms and an equal echo spacing of 8 ms; TR = 3000 ms). The data were fitted for voxel-wise R2 values as well as a spectrum of T2 to extract the MWF.
Results:
Figure 1 illustrates that SANDI can provide more specific interpretation of DW signal changes in GM during development. Compared to adult brains, the developmental brains generally show higher soma density, lower neurite density and smaller soma size. This agrees with histological evidence of similar or even higher number of neurons in infants than adults [12], but much less complex neuronal projections in the former. A smaller average soma size could reflect microglia proliferation in developmental brains as glia have smaller soma than neurons. Compared to SANDI findings in the same GM ROIs, DTI shows somewhat lower MD in developmental brains than adult but no clear trend in FA. Furthermore NODDI-derived NDI could not differentiate developmental stages, and ODI showed similar information to FA. Descriptions of developmental trends and major processes are provided in Fig1A.
In measuring myelination, MWF estimation is sensitive to SNR, thus less robust in GM of developmental brains due to low concentration. R2 is used as a measure of myelination in some developmental studies because myelin has higher R2 than other tissue components. We show in Figure 2 that R2 is confounded by soma density when there is little myelination, due to higher R2 in soma than neurites and extra-cellular space [13]. This suggests that, while R2 (and other relaxation-based measures [14]) can measure maturation, it cannot be interpreted specifically as a myelin measure in the developing brain.
In measuring myelination, MWF estimation is sensitive to SNR, thus less robust in GM of developmental brains due to low concentration. R2 is used as a measure of myelination in some developmental studies because myelin has higher R2 than other tissue components. We show in Figure 2 that R2 is confounded by soma density when there is little myelination, due to higher R2 in soma than neurites and extra-cellular space [13]. This suggests that, while R2 (and other relaxation-based measures [14]) can measure maturation, it cannot be interpreted specifically as a myelin measure in the developing brain.
Conclusions:
We show the promise of combining SANDI and myelin imaging to disentangle biological processes in brain development. Future work will include histological staining to validate MRI findings.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development 2
Keywords:
Acquisition
Cortex
Development
Modeling
MRI
Myelin
Neuron
Sub-Cortical
1|2Indicates the priority used for review
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