White Matter Cerebrovascular and Microstructure Imaging Biomarkers in Normal Cognition and MCI
Poster No:
314
Submission Type:
Abstract Submission
Authors:
Youngkyoo Jung1, Donghoon Kim1, Sarah Yoon1, Timothy Hughes2, Yu-Chien Wu3, Danielle Harvey1, Megan Lipford2, Samuel Lockhart2, Suzanne Craft2, Laura Baker2, Christopher Whitlow2, Stephanie Okonmah-Obazee2, Christina Hugenschmidt2, Matthew Bobinski1
Institutions:
1University of California, Davis, CA, 2Wake Forest School of Medicine, Winston-Salem, NC, 3Indiana University School of Medicine, Indianapolis, IN
First Author:
Co-Author(s):
Introduction:
Mild cognitive impairment (MCI) has been related to impairment in cerebrovascular perfusion and microstructural MRI parameters. However, the relationship between cerebrovascular perfusion and microstructure remains understudied, especially within normal-appearing white matter (NAWM). In this study, we examined whether cerebrovascular perfusion is related to NAWM microstructure in different cognitive statuses, including normal cognition (NC) and MCI.
Methods:
Seventy-nine participants (Sex: 61F/28M; Age: 68.7±7.2;55NC/24MCI) underwent an MRI exam including T1-weighted, T2-FLAIR, dynamic single-PLD pseudo continuous ASL (PCASL), multi-PLD PCASL, and NODDI. The T1-weghted structural images were acquired using MPRAGE sequence with resolution of 1 x 1 x 1 mm3. T2-FLAIR images were acquired using 3D IR-SPACE with resolution of 1 x 1 x 1 mm3. Dynamic single-PLD PCASL images were obtained under a hypercapnia respiratory challenge (Kim et al. 2021) (2D EPI; TR = 4,000 ms; TE = 25 ms; resolution = 3.2 x 3.2 x 5 mm3; labeling duration = 1.8 s; PLD = 1.2 s). The dynamic single-PLD PCASL provided baseline and hypercapnic CBF. Baseline and hypercapnic CBF images were solely used to calculate CVRCBF. The baseline and hypercapnic BOLD were also acquired from dynamic single-PLD PCASL by averaging tag and control images of the dynamic single-PLD PCASL. The baseline and hypercapnic BOLD images were also used to compute CVRBOLD. The multi-PLD PCASL images were acquired with a total of 6 PLDs (0~3000 ms with increments of 600 ms) (Johnston et al. 2015). The multi-PLD PCASL provided ATT and CBF (Kim et al., 2023). Diffusion MRI for ICVF measurement (Zhang et al. 2012) was acquired with the following parameters: 2 mm isotropic resolution; 9 b0 images; 30 directions at b-value = 711 s/mm2 and 60 directions at b-value = 2855 s/mm2. White matter hyperintensity (WMH) detection in this study was achieved with a U-Net with multi-scale highlighting foregrounds (HF) (Park et al., 2021). WMH regions were excluded from all image data of each participant to investigate NAWM regions in this study.
Participant demographics were compared across MCI and NC using chi-square tests and t-tests. In the global NAWM, separate multiple linear regression analyses were performed to investigate the relationship between each perfusion or microstructural metric (CBF, ATT, CVRCBF, CVRBOLD, or ICVF) and cognitive status, adjusted for covariates: age, sex, years of education, and vascular risk factors such as hypertension status, impaired glycemic status, and the presence of APOE-ε4 allele. A voxel-wise analysis was performed for each imaging parameter in the same manner.
Participant demographics were compared across MCI and NC using chi-square tests and t-tests. In the global NAWM, separate multiple linear regression analyses were performed to investigate the relationship between each perfusion or microstructural metric (CBF, ATT, CVRCBF, CVRBOLD, or ICVF) and cognitive status, adjusted for covariates: age, sex, years of education, and vascular risk factors such as hypertension status, impaired glycemic status, and the presence of APOE-ε4 allele. A voxel-wise analysis was performed for each imaging parameter in the same manner.
Results:
In the voxel-wise analysis, prolonged ATT was also observed in voxel clusters associated with MCI (Figure 1A). The voxel clusters were globally located in the WM. Voxel-wise analysis demonstrated no significant statistical relationships between CVRCBF and any vascular risk factors or MCI. In contrast, CVRBOLD revealed two voxel clusters that have statistically significant relationships with MCI (Figure 1B). The voxel-wise analysis identified voxel clusters with statistically significantly lower ICVF values in participants with MCI (Figure 1C). The overlapping voxels between ATT and ICVF were underwent a linear regression analysis demonstrating a significantly negative relationship between residual-adjusted ATT and ICVF with MCI. This relationship was observed exclusively among participants with MCI, as indicated by a p-value of 0.010 in Figure 2B.
·Figure 1. The voxel clusters and the violin plots that showed statistically significant difference in (A) ATT, (B) BOLD CVR, and (C) ICVF between NC and MCI participants
·Figure 2. Illustration of the relationship between ATT and ICVF in clusters across different groups: (A) all participants, (B) MCI, and (C) NC.
Conclusions:
Impaired ATT and ICVF appeared to be closely interlinked in MCI, while CVR served as an independent imaging biomarker. These findings highlight the necessity for further research into the intrinsic link between ATT and ICVF in NAWM.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Novel Imaging Acquisition Methods:
Diffusion MRI
Non-BOLD fMRI 2
Keywords:
Aging
Cerebral Blood Flow
Cognition
Degenerative Disease
MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
Kim D, Hughes TM, Lipford ME, Craft S, Baker LD, Lockhart SN, et al. (2021), 'Relationship between cerebrovascular reactivity and cognition among people with risk of cognitive decline', Frontiers in Physiology, 12:645342.
Kim D, Lipford ME, He H, Ding Q, Ivanovic V, Lockhart SN, et al. (2023), 'Parametric cerebral blood flow and arterial transit time mapping using a 3D convolutional neural network', Magnetic Resonance in Medicine. 90(2):583-595.
Park G, Hong J, Duffy BA, Lee J-M, Kim H. (2021), 'White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds', Neuroimage, 237:118140.
Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. (2012), 'NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain', Neuroimage, 61:1000-16.