Superficial White Matter Architecture in Histology and High-field MRI
Poster No:
1730
Submission Type:
Abstract Submission
Authors:
Youngeun Hwang1,2, Raúl Rodríguez-Cruces1,2, Jordan DeKraker1,2, Donna Gift Cabalo1,2, Ilana Leppert2, Risavarshni Thevakumaran2, Christine Tardif2, David Rudko2, Alan Evans2, Boris Bernhardt1,2
Institutions:
1Multimodal Imaging and Connectome Analysis Lab, McGill University, Montreal, Canada, 2McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Canada
First Author:
Youngeun Hwang
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Co-Author(s):
Raúl Rodríguez-Cruces
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Jordan DeKraker, PhD
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Donna Gift Cabalo
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Ilana Leppert
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
Risavarshni Thevakumaran
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
Christine Tardif, PhD
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
David Rudko
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
Alan Evans
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada
Boris Bernhardt
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada
Introduction:
The superficial white matter (SWM) is a layer of white matter (WM) located immediately underneath the cortex. This SWM contains subcortical U-fibers interconnecting adjacent brain gyri, which remain incompletely myelinated until later in life. [1] Due to the key role of U-fibers in brain plasticity and aging, alterations in their density are observed in various disorders. [2,3,4] Despite its importance, the SWM has been understudied, primarily due to technical difficulties and limitations. [5] Recent advances in ultra-high field 7 Tesla (T) magnetic resonance imaging (MRI) technology have enabled precise imaging and mapping of brain microstructure, leading to reliable research on the SWM. Specifically, quantitative MRI (qMRI) could unravel complex microstructural properties by measuring diffusion MRI parameters and by quantifying changes in myelin-sensitive contrasts. In this study, we introduce an open repository [6] for sampling the SWM surfaces and demonstrate the reliability of SWM mapping using qMRI.
Methods:
Histological dataset and processing: Two post-mortem human brains were utilized in this study: (i) The 200μm BigBrain dataset, an ultra-high-resolution Merker-stained 3D histological reconstruction of a post-mortem human brain, and (ii) The 400μm AHEAD dataset, which includes multiple stainings from a post-mortem human brain. We utilized Silver (Bielschowsky) and Parvalbumin stainings from this dataset.
Multimodal MRI dataset and preprocessing: This study utilized data acquired at the Montreal Neurological Institute using a 7T Siemens Terra system. The dataset included ten healthy participants (5 females) with a mean age of 26.8±4.61 years. The following scans were acquired: (i) T1-weighted images and T1 relaxation time maps (T1 maps), (ii) Myelin-sensitive magnetization transfer (MT) ratio maps computed from gradient echo data with and without MT, and MT saturation (MTsat) maps generated using qMRLab [7] based on MT and T1-weighted images. We preprocessed all MRI data using micapipe [8].
To examine the SWM, we solved the Laplacian equation over the WM domain. This was achieved by initially computing a Laplace field across the WM and subsequently shifting an existing WM surface along that gradient. Stopping conditions were set by the geodesic distance traveled.
For the ex-vivo dataset, SWM surfaces were sampled at fifty depths, each spaced 0.06 mm apart beneath the gray-white matter interface. (Fig. 1A) In contrast, for the in-vivo dataset, SWM surfaces were sampled at fifteen depths, each separated by 0.2 mm. (Fig. 1C)
Multimodal MRI dataset and preprocessing: This study utilized data acquired at the Montreal Neurological Institute using a 7T Siemens Terra system. The dataset included ten healthy participants (5 females) with a mean age of 26.8±4.61 years. The following scans were acquired: (i) T1-weighted images and T1 relaxation time maps (T1 maps), (ii) Myelin-sensitive magnetization transfer (MT) ratio maps computed from gradient echo data with and without MT, and MT saturation (MTsat) maps generated using qMRLab [7] based on MT and T1-weighted images. We preprocessed all MRI data using micapipe [8].
To examine the SWM, we solved the Laplacian equation over the WM domain. This was achieved by initially computing a Laplace field across the WM and subsequently shifting an existing WM surface along that gradient. Stopping conditions were set by the geodesic distance traveled.
For the ex-vivo dataset, SWM surfaces were sampled at fifty depths, each spaced 0.06 mm apart beneath the gray-white matter interface. (Fig. 1A) In contrast, for the in-vivo dataset, SWM surfaces were sampled at fifteen depths, each separated by 0.2 mm. (Fig. 1C)
Results:
The microstructure intensity profiles within the SWM, representing the intensity values of histological and qMRI features, are shown in Fig. 1B and Fig. 1D, respectively. We then further analyzed the intensity profiles from the GM to the WM for both histology and qMRI and performed statistical analysis.(Fig. 1E) Overall, the mean intensity changes occur mainly within the first 1 mm, although the statistical measures indicate notable variation extending up to 2 mm. This suggests that beyond 2 mm, the structural complexity stabilizes, likely reflecting the presence of long fiber bundles.
To evaluate how brain geometry influences SWM intensity, we analyzed the correlation between qMRI intensity values and curvature at various SWM depths (Fig 2A). The results demonstrated a moderate correlation, leading us to generate curvature-corrected qMRI intensity maps (Fig. 2B)
To evaluate how brain geometry influences SWM intensity, we analyzed the correlation between qMRI intensity values and curvature at various SWM depths (Fig 2A). The results demonstrated a moderate correlation, leading us to generate curvature-corrected qMRI intensity maps (Fig. 2B)
Conclusions:
In this study, we investigated the microstructural intensity profiles of the SWM using histology and 7T qMRI, supported by our open repository. Our findings reveal that the deep WM interface begins approximately 2 mm below the gray-white matter boundary. By accounting for the brain geometry effect, these insights highlight the potential of SWM mapping as a valuable biomarker for advancing future research and clinical applications.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Cortical Cyto- and Myeloarchitecture
White Matter Anatomy, Fiber Pathways and Connectivity 2
Neuroinformatics and Data Sharing:
Workflows
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
MRI
Open-Source Code
White Matter
Other - Superficial white matter; Quantitative MRI; Multiparametric mapping
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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