Multiparametric Mapping of Superficial White Matter Architecture Using 7T Quantitative MRI

2199 

Abstract Submission 

Youngeun Hwang^1,2, Jordan DeKraker^1,2, Donna Gift Cabalo^1,2, Yezhou Wang^1,2, Ilana Leppert², Risa Thevakumaran², Christine Tardif², David Rudko², Raúl Rodriguez-Cruces^1,2, Alan Evans², Boris Bernhardt^1,2

¹Multimodal Imaging and Connectome Analysis Lab, McGill University, Montreal, Canada, ²McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Canada

Youngeun Hwang  
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada

Jordan DeKraker  
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

Yezhou Wang  
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

Risa Thevakumaran  
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada

Christine Tardif  
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada

David Rudko  
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada

Raúl Rodriguez-Cruces  
Multimodal Imaging and Connectome Analysis Lab, McGill University|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Canada|Montreal, Canada

Alan Evans  
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

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 (Parazzini, 2002). Due to the key role of U-fibers in brain plasticity and aging, alterations in their density are observed in various disorders (Zikopoulos, 2010; Liu, 2016). Particularly noteworthy is the report that this WM aspect is more advanced in humans compared to other mammals, making the SWM study an area of significant interest. Despite its importance, the SWM has been understudied, primarily due to technical difficulties and limitations (Kirilina, 2020). Recent advances in ultra-high field 7 Tesla 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. This study focuses on standardizing qMRIs on the SWM, validating the reliability of SWM mapping, and contributing to a more comprehensive understanding of its microstructural features.

This study utilized data acquired at the Montreal Neurological Institute on a 7T Siemens Terra system. The dataset included ten healthy participants with a mean±SD age of 26.8±4.61 years (5females). For each MRI protocol parameters were as follows: (i) T1 relaxation time maps (T1 map) and T1-weighted images (MP2RAGE; 0.5mm isovoxels; TR=5170ms; TE=2.44ms; TI1=1000ms; TI2=3200ms), (ii) apparent diffusion coefficient (ADC) and fractional anisotropy (FA) derived from diffusion-weighted MRI (1.1mm isovoxels; TR=7383m; TE=70.60ms; b-values=0, 300, 700, and 2,000 s/mm²; 10, 40, and 90 diffusion directions) (iii) Myelin-sensitive magnetization transfer (MT) ratio maps computed from gradient echo data with and without MT (0.7mm isovoxels; TR=95ms; TE=3.8ms, 50ms, shaped off-resonance MT pulse with a custom offset frequency of -2.0 kHz) MT saturation (MTsat) maps were generated using qMRLab based on MT and T1w images. (iv) iron-sensitive T2* relaxation time maps derived from multi-echo gradient echo (0.7mm isovoxels; TR=43ms, TEs=6.46-11.89-17.33-22.76-28.19-33.62ms).
We preprocessed all MRI data using micapipe (Cruces, 2022). To examine the SWM, we solved the Laplace 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.

SWM surfaces were sampled at six depths, each separated by 0.5 mm, beneath the gray and WM interface (Fig. 1A). The microstructure intensity profiles, depicting the intensity values of qMRI features, are presented in Fig. 1B. The matrix illustrates the subject mean value of the profile on each SWM surface, and this mean value was subsequently mapped onto the brain mask. This mapping allows for the examination of variations in qMRI feature intensity concerning alterations in SWM depth. Fig. 2A presents a matrix illustrating the average microstructure intensity profile across all SWM surfaces for each qMRI. The Spearman correlation coefficient between MTsat and FA intensity profiles was found to be the highest, and there were high negative correlation coefficients between T1 map and MTsat, as well as T1 map and FA. Fig. 2B demonstrates vertex-wise similarities among highly correlated qMRI pairs, showing high correlations for each feature pair across all SWM depths.

In this study, we investigate the microstructural intensity profile of the SWM using 7T qMRI. By establishing quantitative relationships between qMRI features and standardizing microstructural profiles, our work will contribute to a deeper understanding of the SWM, potentially enhancing abnormal connectivity estimation.

Anatomy and Functional Systems

Cortical Anatomy and Brain Mapping ²

White Matter Anatomy, Fiber Pathways and Connectivity ¹

MRI

White Matter

Other - Superficial white matter; Quantitative MRI; Brain mapping