Journal of Neurology

The neural correlates of motor intentional disorders in patients with subcortical vascular cognitive impairment

Geon Ha Kim, MD, PhD1,2,3, Sang Won Seo, MD, PhD 2,4,5, Kihyo Jung, PhD 6, Oh-Hun Kwon, MS 7,
Hunki Kwon, MS7, Jong Hun Kim, MD, PhD8, Jee Hoon Roh, MD, PhD9, Min-Jeong Kim, MD, PhD10, Byung Hwa Lee, MA2,4, Doo Sang Yoon, MD2,4, Jung Won Hwang, BS11, Jong Min Lee, PhD7, Jee Hyang Jeong, MD, PhD 1, Heecheon You, PhD12, Kenneth M. Heilman, MD, PhD13 and Duk L. Na, MD, PhD 2,4,14

1 Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, Seoul, Korea; 2 Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea ; 3 Ewha Brain Institute, Ewha Womans University, Seoul, Korea

4 Neuroscience center, Samsung Medical Center, Seoul, Korea; 5 Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea; 6School of Industrial Engineering, University of Ulsan, Ulsan, Korea; 7 Department of Biomedical Engineering, Hanyang University, Seoul, Korea; 8Department of Neurology, National Health Insurance Corporation, Ilsan Hospital, Goyang, Korea; 9 Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; 10 Department of Neurology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea ;11 Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea ; 12 Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea; 13 Department of Neurology, University of Florida College of Medicine, and the Veterans Affairs Medical Center, Gainesville, FL, USA; 14 Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea

Correspondence: Duk L. Na, MD, PhD, Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea (Tel: +82-2-3410-3591, 3599; Fax: +82-2-3410-0052; e-mail: )

Supplementary Methods

MR image acquisition

Cortical thickness was analyzed using the 3D T1 turbo field echo (TFE) MR images according to the following imaging parameters: sagittal slice thickness, 1.0 mm; no gap; repetition time (TR), 9.9 ms; echo time (TE), 4.6 ms; flip angle, 8 °; and a matrix size of 480x480 pixels. Fluid attenuated inversion recovery (FLAIR) MR images were used to evaluate the abnormal white matter hyperintensities. These were assessed using the following imaging parameters: axial slice thickness of 2 mm, no gap, TR of 11000.0 ms, TE of 125.0 ms, flip angle of 90°, and a matrix size of 512x512 pixels. The third technique was T1 reference (REF) MR imaging. In this step, images were acquired with an axial slice thickness of 4 mm, with; no gap, TR of 545 ms, TE of 10 ms, flip angle of 70°, and a matrix size of 512x512 pixels. In order to evaluate if there were abnormal signal intensities including lacunes, T2 MR images were acquired using an axial slice thickness of 5.0 mm, inter-slice thickness of 1.5 mm, TR of 3000.0 ms, TE of 80.0 ms, flip angle of 90 °, and a matrix size of 512 x 512 pixels. Information regarding microbleeds was obtained with T2 fast field echo(FFE) images according to the following parameters: axial slice thickness, 5.0 mm; inter-slice thickness, 2 mm; TR, 669 ms; TE 16 ms; flip angle, 18 °, and a matrix size of 560x 560 pixels. For the Tract-Based Spatial Statistics (TBSS), diffusion tensor images (DTI) were acquired by diffusion-weighted single shot echo-planar imaging with the following parameters: TE, 60 ms; TR, 7,696 ms; flip angle, 90 °; b-factor, 600 s/mm2; matrix dimensions, 128 x 128; 70 axial sections. All diffusion-weighted images, including the baseline image that was not weighted, were acquired from 45 different directions. All axial sections were obtained parallel to the anterior commissure-posterior commissure line.

Image processing

Cortical thickness

The CIVET anatomical pipeline was used to extract cortical thickness [1]. In brief, surfaces of the inner and outer cortex for each hemisphere were generated through the following process. Linear transformation was used to register the raw MR images into a standardized stereotaxic space [2] for the normalization to the MNI 152 template, Then, the N3 algorithm was used to correct the images for non-uniformities in the intensity from heterogeneities in the magnetic field [3]. A 3D stereotaxic brain mask and the Intensity-Normalized Stereotaxic Environment for Classification of Tissues algorithm were used to remove non-brain tissues and to classify the registered and corrected volumes into the white matter, gray matter, cerebrospinal fluid (CSF), and background [2-6]. The presence of extensive white matter hyperintensities (WMH) in the MRI scans made it difficult to complete tissue classification process. To overcome this technical limitation, we automatically defined the WMH region using a FLAIR image and substituted it for the intensity of peripheral, normal-appearing tissue after affine co-registration [7]. The Constrained Laplacian-based Automated Segmentation with Proximities algorithm was used to extract the surfaces of the inner and outer cortices that contained 40,962 vertices on each hemisphere in native space [8]. Finally, cortical thickness was defined as the Euclidean distance between the linked vertices of the inner and outer surfaces [9]. The thicknesses of the subjects were compared by spatially registering them to a group template and smoothing with a full-width half-maximum of 20mm[10, 11] [12]. Intracranial volume (ICV) was calculated by measuring the total volume of gray matter, white matter, and cerebrospinal fluid. [9]Detailed processing for cortical thickness was described in many previous study [13-16] .

Tract-Based Spatial Statistics (TBSS)

DTI data was processed using FMRIB’s Sorftware Library software (FSL v5.0.2.1) (http://www.fmrib.ox.ac.uk/fsl). Motion artifacts and eddy current distortions were corrected by normalizing each diffusion weighted volume to the non-diffusion weighted volume (b0) using affine registration method in the FMRIB's Linear Image Registration Tool (FLIRT). The diffusion weighted Images were skull stripped with the Brain Extraction Tool (BET) from the FSL Tool [17]. The DTIFIT Tool (part of FSL) calculated the diffusion tensor from the diffusion weighted images using nonlinear estimation of the diffusion tensor model [18]. Then, fractional anisotropy (FA) [19] was extracted for each voxel based on diagonal elements (λ1, λ2 and λ3) of diffusion tensor. Tract-Based Spatial Statistics (TBSS) was used to conduct whole brain analysis of the fractional anisotrophy (FA) images [20]. First, the FA maps were realigned to a common target. Next, using the FMRIB58_FA template, the aligned FA volumes were normalized to a 16161 mm3 Montreal Neurological Institute (MNI152) standard space. The registered FA images were averaged to generate a cross-subject mean FA image. This mean FA image was used to create a mean FA skeleton, which represents the main fiber tracks and the center of all of the tracts common to the group. A threshold of 0.2 was applied to the mean FA skeleton. This threshold was used to exclude peripheral tracts where there was significant inter-subject variability and/or partial volume effects with grey matter. Each participant’s aligned FA data were projected onto the mean skeleton to create a skeletonized FA map.

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