DOI: 10.7272/q6mw2f2n
title: Voxel-wise co-analysis of macro- and microstructural brain alteration in Mild Cognitive Impairment and Alzheimer's disease using anatomical and diffusion MRI

description: Structural and diffusion data from cognitively normal elderly and those
 with mild cognitive impairment. Background and Purpose: To determine if a
 voxel-wise "co-analysis" of structural and diffusion tensor
 magnetic resonance imaging (MRI) together reveals additional brain regions
 affected in mild cognitive impairment (MCI) and Alzheimer's Disease
 (AD) than voxel-wise analysis of the individual MRI modalities alone.
 Conclusion: These results suggest that in corpus callosum and temporal
 regions macroand microstructural variations in MCI can be congruent,
 providing potentially new insight into the mechanisms of brain tissue
 degeneration.
description: Methods: Twenty-one patients with MCI, 21 patients with AD, and 21
 cognitively normal healthy elderly were studied with MRI. Maps of
 deformation and fractional anisotropy (FA) were computed and used as
 dependent variables in univariate and multivariate statistical models.
 Results: Univariate voxel-wise analysis of macrostructural changes in MCI
 showed atrophy in the right anterior temporal lobe, left posterior
 parietal/precuneus region, WM adjacent to the cingulate gyrus, and
 dorsolateral prefrontal regions, consistent with prior research.
 Univariate voxel-wise analysis of microstructural changes in MCI showed
 reduced FA in the left posterior parietal region extending into the corpus
 callosum, consistent with previous work. The multivariate analysis, which
 provides more information than univariate tests when structural and FA
 measures are correlated, revealed additional MCI-related changes in corpus
 callosum and temporal lobe. Participants: Participants were recruited by
 advertisements in the community or referred by one of several memory
 clinics in the San Francisco Bay Area, including the Memory Disorders
 Clinic at the San Francisco Veterans Affairs Medical Center, the Memory
 and Aging Center at the University of California, San Francisco, and the
 Memory Disorders Clinic at the California Pacific Medical Center, for
 inclusion in one of several studies. Study procedures include a
 neurological exam, structural MRI, diffusion MRI, and comprehensive
 neuropsychological testing. In compliance with the Code of Ethics of the
 World Medical Association and the Declaration of Helsinki, study
 procedures were approved by review boards of the University of California
 San Francisco and the San Francisco VA Medical Center, explained to all
 participants, written informed consent was obtained. From the larger
 population studied, only participants with artifact-free structural and
 diffusion MRI were included. Twenty-one patients met Petersen's
 criteria for MCI, age: 71 ± 8 yrs, 11 women, MMSE (Mini-Mental State
 Examination): 29 ± 2. These patients were gender matched with 21
 cognitively normal healthy elderly controls (CN), age: 70 ± 7 yrs, 11
 women, MMSE 29 ± 1. The MCI patients were also gender matched with 21
 patients who met AD criteria of the National Institute of Neurological and
 Communicative Disorders and Stroke-Alzheimer's Disease and Related
 Disorders Association (NINCDS/ADRDA), age: 69 ± 9, 11 women, MMSE 21 ± 7.
 As evidenced by the high MMSE scores, the MCI patients were in an early
 stage of cognitive deficits and far less impaired than the AD patients.
 Magnetic resonance imaging (MRI) acquisition: All scans were performed on
 a 4 Tesla (Bruker/Siemens) MRI system with a birdcage transmit and 8
 channel receive coil arranged in the same housing. The scans included
 T1-weighted and T2-weighted structural MRI data for measurements of brain
 macro-structure and diffusion tensor MRI for measurement of
 micro-structure. T1-weighted images were obtained with a 3D volumetric
 magnetization prepared rapid gradient echo (MPRAGE) sequence,
 TR/TE/TI=2300/3/950 ms, timing; 7º flip angle; 1.0 x 1.0 x 1.0 mm3
 resolution; 157 continuous sagittal slices; acquisition time of 5 min.
 T2-weighted images were acquired with a variable flip (VFL) angle turbo
 spin-echo sequence with TR/TE = 4000/30 ms and with the same resolution
 matrix and field of view of MPRAGE. DTI was based on a dual spin-echo
 refocused echo-planar imaging (EPI) sequence supplemented with parallel
 imaging acceleration (GRAPPA) (Griswold et al., 2002) with a factor 2 to
 reduce susceptibility distortions. Other imaging parameters were:
 TR/TE=6000/77 ms; 2 x 2 mm2 in-plane resolution; 40 continuous 3 mm
 slices. A reference image (no diffusion gradient b=0) and six
 diffusion-weighted images (b=800s/mm2 along six non-collinear direction)
 were acquired. Four DTI scans were acquired and averaged after motion
 correction to boost signal-to-noise. The total acquisition time of DTI was
 4 min. MRI pre-processing: An expectation maximization segmentation (EMS)
 algorithm including correction for intensity inhomogeneity 30,31 was
 applied to T1 weighted MRI, separating skull, scalp, extra-cranial tissue
 from the rest of brain image volume. Each individual skull-stripped and
 bias field corrected brain image volume was affine registered to a
 reference brain image to adjust for global differences in brain
 positioning and scale across individuals. For this study, an unbiased
 average brain image was used as the reference. The unbiased average brain
 was generated from 10 healthy elderly individual brains (i.e., age of 50
 to 70) that were not part of the CN group using an unbiased atlas
 formation technique based on large deformations mapping 32. A large
 deformation diffeomorphic mapping algorithm using fluid-flow registration
 33 was used to register individual scans to standard image space of the
 unbiased atlas brain. The Jacobian determinant of this transformation was
 computed at each voxel (resolution 1x1x1 mm3), giving the pattern of
 volume change required to force the individual anatomy to conform to the
 reference. The Jacobian images were log transformed to achieve a more
 normal distribution and then smoothed using a Gaussian filter (FWHM=10 mm)
 to create tissue volume maps (JAC), where the value at each voxel
 represents the tissue volume relative to the reference (e.g., a voxel
 value of 0.06 denotes a volume volrefx100.06, or about 15 percent greater
 than the reference voxel) DTI pre-processing: The pre-processing pipeline
 aims to align a set of DTI images with the corresponding structural data
 through the following steps. Diffusion tensor images were corrected for
 eddy currents and head motion 34. Based on a mutual information metric,
 the T2-weighted image was rigidly aligned to the B0 image, which is a DTI
 maps without diffusion sensing gradients (b=0 s/mm2). A variational
 image-based approach was used to calculate a deformation field from the B0
 image to the rigidly aligned T2-weighted image to correct for
 susceptibility artifacts (i.e., nonlinear geometric distortion in DTI) 35.
 We selected a variational approach for EPI distortion correction over the
 conventional field map approach, because studies have shown that the local
 deformations in EPI are best handled with a dense displacement field (i.e.
 high order deformations) 35. In contrast, field maps can fail to
 completely correct geometric distortions, presumably due to limits in the
 physical model 36. Concatenated eddy current transformation and the
 deformation field for nonlinear distortion correction were applied to all
 DTIs. Diffusion tensors were estimated for each subject from the diffusion
 tensor images using weighted least squares tensor estimation 37. The
 T2-weighted image was rigidly aligned to the T1-weighted image and the
 transformation was concatenated with the inverse rigid transformation from
 B0 image to T2-weighted image. The resulting rigid transformation was
 applied to the FA image to map onto the T1-weighted structural image space
 of the subject. The FA image was then mapped onto the standard image space
 by applying the diffeomorphic mapping estimated in MRI pre-processing. FA
 images were then smoothed using a Gaussian filter (FWHM=10 mm) in the
 standard image space.