Quantification of Human Brain White Matter Myelination using ViSTa MRI Fingerprinting
Poster No:
1716
Submission Type:
Abstract Submission
Authors:
Jialan Zheng1,2, Cong Yang3, Wen Zhong1, Haoxiang Li1, Ziang Wang4, Xiaozhi Cao5, Congyu Liao5, Qiyuan Tian1
Institutions:
1School of Biomedical Engineering, Tsinghua University, Beijing, China, 2Tanwei College, Tsinghua University, Beijing, China, 3Tsinghua Lablratory of Brain and Intelligence, Tsinghua University, Beijing, China, 4Tsinghua Medicine, Tsinghua University, Beijing, China, 5Stanford University, Stanford, CA
First Author:
Jialan Zheng
School of Biomedical Engineering, Tsinghua University|Tanwei College, Tsinghua University
Beijing, China|Beijing, China
School of Biomedical Engineering, Tsinghua University|Tanwei College, Tsinghua University
Beijing, China|Beijing, China
Co-Author(s):
Introduction:
Myelination is crucial for neuronal signal transmission and indicates brain development and function. Various MRI techniques, like multi-compartment T2 mapping, magnetization transfer, and ViSTa method were used to study myelination (Piredda, 2021). Nonetheless, most previous studies focused on long-range association fibers (LAFs) in deep white matter (DWM), overlooking short-range association fibers (SAFs) in superficial white matter (SWM).
This study employs the advanced ViSTa-MR fingerprinting (MRF) sequence (Liao, 2023) in combination with diffusion and T1w sequences to assess myelin water fraction (MWF) and microstructural parameters from the Neurite Orientation Dispersion and Density Imaging (NODDI) model (Zhang, 2012). MWF and NODDI parameters are used to calculate the g-ratio (Stikov, 2015) that reflects relative myelin thickness, aiming to enhance our understanding of brain's structural and functional organization.
This study employs the advanced ViSTa-MR fingerprinting (MRF) sequence (Liao, 2023) in combination with diffusion and T1w sequences to assess myelin water fraction (MWF) and microstructural parameters from the Neurite Orientation Dispersion and Density Imaging (NODDI) model (Zhang, 2012). MWF and NODDI parameters are used to calculate the g-ratio (Stikov, 2015) that reflects relative myelin thickness, aiming to enhance our understanding of brain's structural and functional organization.
Methods:
Data. With written consent forms and IRB approval, T1w-MPRAGE, diffusion MRI and ViSTa-MRF data were acquired on 10 healthy young adults on a 3-Tesla scanner (Siemens, MAGNETOM Prisma) equipped with a 32-channel head coil. Diffusion data were acquired using a product 2D SMS-PGSE single-shot EPI sequence with 32 and 64 uniform directions at b=1000 and 2500 s/mm2, respectively.
Processing. Freesurfer was used for cortical reconstruction and segmentation of T1w data (Fischl, 2012). The diffusion data were preprocessed using "topup" and "eddy" function from FSL (Smith, 2004). NODDI parameters were derived using inventors' MATLAB toolbox. Multi-modal data were coregistered using Boundary-Based Registration (Douglas, 2009). G-ratio was calculated as described in Figure 1(A).
ROI extraction. ROIs for 42 deep white matter tracts were extracted by registering diffusion data to FSL xtract atlas in the MNI space. Whole-brain tractography was conducted using MSMT-CSD (Jeurissen, 2014) and iFOD2 methods with (Tournier, 2010) SIFT correction. Voxels for LAFs and SAFs between any two cortical regions were extracted.
Statistics. MWF and g-ratio results were averaged across DWM tract voxels, and across cortical vertices, SAF and LAF voxels and subjects (then displayed on cortical surface). The correlation of the mean MWF, g-ratio and connectivity strength between any two cortices, along with the mean value of other imaging parameters of the two cortices were computed.
Processing. Freesurfer was used for cortical reconstruction and segmentation of T1w data (Fischl, 2012). The diffusion data were preprocessed using "topup" and "eddy" function from FSL (Smith, 2004). NODDI parameters were derived using inventors' MATLAB toolbox. Multi-modal data were coregistered using Boundary-Based Registration (Douglas, 2009). G-ratio was calculated as described in Figure 1(A).
ROI extraction. ROIs for 42 deep white matter tracts were extracted by registering diffusion data to FSL xtract atlas in the MNI space. Whole-brain tractography was conducted using MSMT-CSD (Jeurissen, 2014) and iFOD2 methods with (Tournier, 2010) SIFT correction. Voxels for LAFs and SAFs between any two cortical regions were extracted.
Statistics. MWF and g-ratio results were averaged across DWM tract voxels, and across cortical vertices, SAF and LAF voxels and subjects (then displayed on cortical surface). The correlation of the mean MWF, g-ratio and connectivity strength between any two cortices, along with the mean value of other imaging parameters of the two cortices were computed.
Results:
Validation. Mean MWFs were projected to middle cortical surface (Figure 2A), showing higher myelination in primary motor, sensory, visual and auditory region, following expected anatomy and demonstrating validity of MWFs.
DWM tracts. Mean MWF and g-ratio values were depicted for each DWM tract (Figure 1B, 1C). Tracts like cingulum, AC and UF exhibit low MWF. FX tract exhibits low MWF and g-ratio, suggesting thinner axons. CST exhibits high MWF and g-ratio, indicating thicker axons. OR, AF, FMA, and FMI tracts show high MWF.
Association Fibers. Figure 2C shows comparison of myelination in association fibers between cortical regions. LAFs exhibit higher myelination than SAFs. SAFs under entorhinal cortex, parahippocampal region and temporal pole exhibit lower MWFs. SAFs from posterior cingulate show higher MWF. Figure 2B shows connectivity strength is negatively correlated with MWF (yellow box), whereas cortical volume and curvature are positively correlated with MWF (green box).
DWM tracts. Mean MWF and g-ratio values were depicted for each DWM tract (Figure 1B, 1C). Tracts like cingulum, AC and UF exhibit low MWF. FX tract exhibits low MWF and g-ratio, suggesting thinner axons. CST exhibits high MWF and g-ratio, indicating thicker axons. OR, AF, FMA, and FMI tracts show high MWF.
Association Fibers. Figure 2C shows comparison of myelination in association fibers between cortical regions. LAFs exhibit higher myelination than SAFs. SAFs under entorhinal cortex, parahippocampal region and temporal pole exhibit lower MWFs. SAFs from posterior cingulate show higher MWF. Figure 2B shows connectivity strength is negatively correlated with MWF (yellow box), whereas cortical volume and curvature are positively correlated with MWF (green box).
Conclusions:
In DWM, myelination in fibers relevant to higher cognitive function is relatively low. Commissural fibers show higher myelination. Additionally, the spatial pattern of the myelination of the association fibers indicates the balance of connectivity strength and myelination.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Cortical Cyto- and Myeloarchitecture 2
Keywords:
Cortex
Data analysis
Modeling
MRI
Myelin
Neuron
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - MRI Fingerprinting; Superficial White Matter
1|2Indicates the priority used for review
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