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DOI: 10.1148/radiol.2411051051
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(Radiology 2006;241:197-205.)
© RSNA, 2006

Neuroradiology

Mild Cognitive Impairment: Apparent Diffusion Coefficient in Regional Gray Matter and White Matter Structures1

Kimberly M. Ray, MD, Huali Wang, MD, PhD, Yong Chu, PhD, Ya-Fang Chen, MD, Alberto Bert, PhD, Anton N. Hasso, MD and Min-Ying Su, PhD

1 From the Department of Radiological Sciences (K.M.R., H.W., A.N.H., M.Y.S.), Tu and Yuen Center for Functional Onco-Imaging (H.W., Y.C., M.Y.S.), and Department of Electrical Engineering and Computer Science (Y.C.), University of California, Irvine, 164 Irvine Hall, Irvine, CA 92697-5020; Department of Geriatric Psychiatry, Peking University Institute of Mental Health, Beijing, China (H.W.); Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan (Y.F.C.); and Unit of Radiology, Institute for Cancer Research and Treatment, Mauriziano Hospital, and ISI Foundation, Turin, Italy (A.B.). From the 2004 RSNA Annual Meeting. Received June 22, 2005; revision requested August 23; revision received September 20; accepted October 14; final version accepted November 17. Supported in part by grant NIH/NIA P50 AG16573, R01 AG17066, AG-019681, and M01 RR00827 from the National Center for Research Resources. Address correspondence to M.Y.S. (e-mail: msu{at}uci.edu).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 
Purpose: To prospectively evaluate regional alterations in the apparent diffusion coefficient (ADC) of cortical gray and white matter and subcortical structures that are known to be involved in mild cognitive impairment (MCI).

Materials and Methods: Magnetic resonance (MR) imaging was performed in 13 patients with MCI (nine men, four women; mean age, 74 years ± 6 [standard deviation]) and 13 healthy elderly control subjects (seven men, six women; mean age, 75 years ± 4). This study was approved by the institutional review board and was HIPAA compliant. Each subject gave informed consent. ADC was measured from manually drawn regions of interest (ROIs) of the hippocampus, parahippocampal gyrus, amygdala, corpus callosum, and anterior and posterior cingulate gyrus and from automatically defined frontal, parietal, occipital, and temporal lobes by using template masking. ROIs were outlined on anatomic images then mapped onto ADC maps by using coregistration transformation matrix. A skeleton-based region competition segmentation algorithm was used for segmentation of gray and white matter. The group difference in ADC values was assessed with independent-sample t tests. Pearson correlation analysis was used to examine the correlation of ADC values with age and memory test scores.

Results: Higher ADCs were found in hippocampus, temporal lobe gray matter, and corpus callosum of patients with MCI compared with that of control subjects (P < .05). By pooling all subjects together, an elevated hippocampal ADC was significantly correlated with worse memory performance scores in 5-minute and 30-minute delayed word-list recall tasks (P < .05).

Conclusion: ADCs from gray and white matter of different brain regions can be analyzed by applying an automated template-masking method in conjunction with a skeleton-based region competition segmentation algorithm.

© RSNA, 2006


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 
The syndrome of mild cognitive impairment (MCI) is associated with an increased risk of progression to Alzheimer disease (AD) at a rate of 12%–15% per year. Accurate identification of this subset of patients would enable therapeutic intervention to slow the progression of dementia. Clinical criteria for the diagnosis of MCI have been established (1). In addition to neuropsychologic assessment, imaging is another important component in the diagnosis of dementia. Many studies have shown that hippocampal atrophy is associated with memory impairment, and it has been established that hippocampal atrophy is not only one reliable criterion for diagnosis of AD but also a consistent predictor for conversion of MCI to AD (2,3).

Diffusion-weighted magnetic resonance (MR) imaging provides a means of noninvasively probing tissue microarchitecture. The basis for image contrast in diffusion-weighted MR imaging resides in its sensitivity to the molecular motion of water. The random motion of water molecules is restricted by the local tissue microenvironment, which includes cell membranes and myelination. Early pathologic alterations of biologic tissue at the cellular and molecular levels may thus be detectable by using diffusion-weighted MR imaging and quantified with calculation of the apparent diffusion coefficient (ADC) (4,5).

MR diffusion studies have demonstrated widespread alterations in regional ADC in AD (6–11), but only a few studies included patients with MCI (11–13). Most of these studies used a manually defined region of interest (ROI) from a small white matter area in the frontal, temporal, parietal, and occipital lobes and in subcortical structures, such as the corpus callosum, hippocampus, and thalamus. The ADC was found to be elevated in the hippocampus and in certain white matter regions in patients with AD when compared with that of control subjects, but the hippocampus was the only structure that consistently exhibited a higher ADC in MCI patients when compared with that of control subjects.

To our knowledge, the ADC of gray matter in various lobes has never been reported, presumably due to difficulty in defining a consistent ROI by using manual drawing. Atrophy or hypoperfusion of medial temporal structures, however, has been associated with conversion from MCI to AD, which implicates possible early pathologic changes (14,15). Thus, the purpose of our study was to prospectively evaluate regional alterations in the ADC of cortical gray matter and white matter and subcortical structures that are known to be involved in MCI.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 
Subjects
Thirteen patients with MCI (nine men, four women; mean age, 74 years ± 6 [standard deviation]) and 13 healthy elderly control subjects (seven men, six women; mean age, 75 years ± 4) were recruited through the Alzheimer Disease Research Center at our institution. The study was approved by the institutional review board and was Health Insurance Portability and Accountability Act compliant. All participants gave written informed consent. Subjects were recruited prospectively from November 2002 to May 2004. They were consecutive patients and control subjects who met inclusion criteria. Patients with MCI met the Petersen criteria, with memory complaint but otherwise normal cognition and normal daily living activities (1). These individuals had a clinical dementia rating score of 0.5 or lower (16). Control subjects had a clinical dementia rating score of 0.

Anatomic and Diffusion-weighted MR Imaging
The MR imaging and diffusion-weighted imaging studies were performed with a 1.5-T imager (Eclipse; Philips Medical Systems, Cleveland, Ohio). All participants underwent a standardized imaging protocol, with acquisition of intermediate-weighted with T2-weighted, fluid-attenuated inversion-recovery, three-dimensional (3D) T1-weighted, and diffusion-weighted (with x, y, z directions and trace encoding) images. The 3D volumetric spoiled gradient-recalled-echo sequence was performed to acquire the high-spatial-resolution T1-weighted anatomic images. The acquisition parameters were as follows: repetition time msec/echo time msec, 11/4; flip angle, 20°; field of view, 22 cm; matrix, 256 x 256 x 120; and section thickness, 1.5 mm. Single-shot echo-planar diffusion-weighted MR imaging was performed in the transverse plane to acquire 20 multisection images. The parameters were as follows: 6300/100; matrix, 128 x 64; field of view, 24 cm; section thickness, 6 mm; and intersection gap, 1 mm. A reference echo-planar image (b = 0 sec/mm2) and four diffusion-weighted echo-planar images (b = 1000 sec/mm2) in three orthogonal directions (x, y, z), individually and simultaneously (the trace), were acquired. Then the ADC maps and trace were calculated and output as Digital Imaging and Communication in Medicine images ready for analysis. The acquired brain images for each participant were independently reviewed by three radiologists (K.M.R., Y.F.C., A.N.H.) for atrophy and lesions.

Manual Delineation of ROI and Template Masking of Four Brain Lobes
On coronal plane T1-weighted images, one investigator (H.W., 6 years of experience in brain volumetric measurement) blinded to all clinical information used a mouse-oriented method to manually trace the boundaries of the medial temporal structures, including the hippocampus, parahippocampal gyrus, amygdala, and corpus callosum (17–19). The tracing was performed with a program (ROITOOL) that was based on Matlab (Mathworks, Natick, Mass), which was developed in-house and could display the real-time ROI drawing simultaneously on three orthogonal planes (Fig 1). The borders of the intracranial vault were also traced sequentially from posterior to anterior on each section by using previously defined boundaries (17–19). The hippocampal volumes were normalized with the individual intracranial volumes to obtain normalized volumetric ratios in the analysis. Two other investigators (K.M.R. and Y.F.C., 4 years and 8 years of experience in brain MR imaging, respectively) working together and blinded to clinical diagnosis manually traced the boundaries of the anterior and posterior cingulate gyrus.


Figure 1
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  		Figure 1: Traced MR images of ROI of bilateral hippocampi displayed in three orthogonal planes with ROITOOL software after section-by-section drawing is completed (left = coronal, middle = sagittal, right = transverse). Original images were acquired with coronal view by using 3D spoiled gradient-recalled-echo pulse sequence (11/4, flip angle of 20°). Sagittal and transverse images were reconstructed from coronal images.

 
An automated atlas-based masking program (WFU_PickAtlas; ANSIR Core at Wake Forest University, Winston-Salem, NC; http://www.fmri.wfubmc.edu/download.htm) was used to define the ROI for the frontal, parietal, temporal, and occipital lobes. The 3D anatomic images of each participant were coregistered to the Montreal Neurological Institute template images (MR images of a digital brain developed by the Montreal Neurological Institute) by using software (SPM2; Wellcome Department of Imaging Neuroscience, London, England; http://www.fil.ion.ucl.ac.uk/spm/software/spm2/) to obtain the transformation matrix. The ROI mask for each lobe on the Montreal Neurological Institute template was available in the automated atlas-based masking tool (20,21). The ROI mask was mapped onto the participant's anatomic images by using the inverse of the transformation matrix to obtain four lobal mask files (frontal, temporal, parietal, and occipital) for each participant (Fig 2).


Figure 2
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  		Figure 2: Temporal ROI on three orthogonal plane MR images from one control subject. ROI was mapped from temporal lobe mask provided by automated atlas-based masking tool onto Montreal Neurological Institute template by using transformation matrix, obtained by coregistration of participant's anatomic images to Montreal Neurological Institute template images (left = coronal, middle = sagittal, right = transverse). Imaging sequence was same as for Figure 1.

 
Segmentation with the Skeleton-based Region Competition Algorithm
The segmentation to separate gray matter, white matter, and cerebrospinal fluid on images was performed by using a skeleton-based region competition algorithm (22). This algorithm has been applied to analyze the Montreal Neurological Institute digital brain images and shown to reach 96% accuracy. Region competition has been demonstrated as an effective method for image segmentation by minimizing a generalized Bayes/minimum-description-length criterion. However, it is sensitive to initial conditions—the "seeds"; therefore, an optimal choice of seeds is necessary for accurate segmentation. In this study, skeletons were used as the seeds. Three steps were required: First, cerebrospinal fluid, gray matter, and white matter were roughly segmented by using a shape-based histogram algorithm; second, the Hilditch sequential thinning algorithm was used to extract the skeletons of a segmented region; third, the iteration with the skeletons as the initial condition was started. When the algorithm converged, cerebrospinal fluid, gray matter, and white matter were successfully segmented. The gray matter and white matter map on each imaging section of each participant was multiplied with the lobal mask file to obtain the gray matter and white matter contained within each lobe (Fig 3).


Figure 3
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  		Figure 3: Original anatomic MR images (top row) and segmented gray matter maps (bottom row) from one coronal section of one control subject (left), one MCI patient without severe atrophy (middle), and another MCI patient with severe atrophy (right). Lateral temporal lobe is obtained on top row, which was obtained by using method demonstrated in Figure 2. Imaging sequence was same as for Figure 1.

 
Calculation of ADC for Each ROI
The ADC analysis program was written by two authors (Y.C. and A.B.), and calculation was performed by one author (K.M.R.). Because all ROIs were defined on the 3D anatomic images, the 3D anatomic images were first coregistered to the ADC maps; then the ROIs could be mapped onto the ADC maps. The diffusion-weighted images (b = 0 or 1000 sec/mm2) and ADC maps were from a corresponding region, and because the reference images (b = 0 sec/mm2) had the best image quality, they were chosen for coregistration. For each participant, the multiple two-dimensional images with gaps were combined into a 3D file; then the high-spatial-resolution 3D anatomic images were coregistered to the generated 3D reference diffusion-weighted image file to obtain the transformation matrix. The ROI defined on the anatomic images could then be mapped onto the ADC trace maps by using the obtained transformation matrix (Fig 4). Because the voxel size on the ADC maps was larger (1.875 x 1.875 x 6 mm) than the voxel size on the anatomic images (0.86 x 0.86 x 1.5 mm), each ADC voxel might only partially contain the tissue of interest (ie, with partial volume effect). The tissue ROI was first mapped to the ADC images (eg, hippocampus on Fig 4, right) to determine the ADC voxels contained within that ROI. Then for each involved ADC voxel, the percentage of tissue of interest contained within that voxel was calculated. Because each ROI contained multiple ADC voxels, a mean ADC was calculated by using the weighted average of all voxels contained within that ROI. The weighting factor was the percentage of tissue of interest contained within each voxel. However, if a pixel contained less than 30% of tissue of interest, it was discarded to minimize partial volume contamination.


Figure 4
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  		Figure 4: Hippocampus ROI traced on transverse anatomic MR image (left), mapped ROI onto reference echo-planar image (middle), and ADC trace map (right) from one patient with MCI. Reference image (b = 0 sec/mm2) was acquired with a multisection two-dimensional echo-planar imaging sequence (6300/100). ADC map was calculated by using the diffusion-weighted image (b = 1000 sec/mm2) relative to the reference image.

 
Cognitive Performance Assessment
All 13 patients with MCI and 10 control subjects completed a neuropsychologic test battery conducted by neuropsychologists at our Alzheimer Disease Research Center. Three control subjects—one neurologist who was too familiar with the battery and two non–English-speaking persons—did not undergo the test. General cognitive status was assessed with the Mini-Mental State Examination (23). Because memory is considered to be the only impaired cognitive domain in MCI patients, only the memory test scores were retrieved for the statistical analysis (1,24). The performance scores on the 5-minute and 30-minute delayed verbal word-list recall tests from the Consortium to Establish a Registry for Alzheimer Disease battery (25) were obtained. The average scores of three trials for 5-minute and 30-minute delayed recall tests were used in the analysis. The scores on the Wechsler Memory Scale, third revision (WMS-3) (26) logic memory tests 1 and 2, were obtained. For the logic memory tests 1 and 2, the raw scores were transformed into the scaled scores; a lower score indicated more severe memory impairment.

Statistical Analyses
Statistical software (SPSS for Windows, release 12.0; SPSS, Chicago, Ill) was used for statistical analysis. The age between patients with MCI and control subjects was compared by using the independent-sample t test. The group difference in the neuropsychologic performance scores and the ADC values was assessed with the independent-sample t test. The side-to-side ADC trace values between the left and right hippocampus were assessed with the paired-sample t test. Pearson correlation analysis was employed to examine the correlation of ADC values with age and memory test scores. Results of all statistical tests were regarded as significant at P < .05.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 
Demographic and Cognitive Performance Data
There was no significant difference in age between patients with MCI and control subjects (P > .05) (Table 1). The Mini-Mental State Examination scores of patients with MCI were lower than those of control subjects and were at borderline significance level (P = .05). Patients with MCI had a significantly poorer performance score on the 5- and 30-minute delayed recall tests and on logic memory test 2 than did the control subjects (P < .05). The performance score on logic memory test 1 was lower in patients with MCI than in control subjects but did not reach the significance level (P > .05).


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  		Table 1. Demographic Data and Neuropsychologic Test Results in Control Subjects and Patients with MCI

 
ADC Values
Atrophy and white matter hyperintensity were the common findings. For some subjects, atrophy may be more pronounced in one side than the other, but no midline shift or apparent asymmetry was noted for any participants. There was no significant side-to-side difference in ADC trace values among the left and right hippocampus, parahippocampal gyrus, amygdala, and cortical lobes (P > .05), so the left and right data were combined for the group comparison. Since no brain asymmetry was noted, laterality differences in larger lobar ROIs were not assessed. The ADC trace value of the hippocampus was significantly elevated (Table 2) in patients with MCI (1143 x 10–6 mm2/sec ± 161) when compared with that of control subjects (982 x 10–6 mm2/sec ± 67) (P < .05) (Fig 5a). It was not correlated with the normalized volume of each lateral hippocampus (ie, not associated with atrophy). ADC trace value of the temporal lobe gray matter was significantly higher in patients with MCI (1078 x 10–6 mm2/sec ± 81) when compared with that of control subjects (1017 x 10–6 mm2/sec ± 56) (P < .05) (Fig 5a). Finally, the ADC trace value of the corpus callosum was also elevated in patients with MCI (1098 x 10–6 mm2/sec ± 302), compared with that of control subjects (890 x 10–6 mm2/sec ± 87) (P < .05) (Fig 5b). Except these three ROIs, all other analyzed gray matter or white matter regions did not show significant differences of ADC trace values between patients with MCI and control subjects. There was a significant negative correlation between the ADC trace value of the hippocampus and performance scores on the 5-minute delayed recall test (Fig 6a) and the 30-minute delayed recall test (Fig 6b) (P < .05). The ADC was also negatively correlated with the performance of logic memory test 2, but not with logic memory test 1.


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  		Table 2. ADC Trace Values of Brain Regions in Control Subjects and Patients with MCI

 

Figure 5
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  		Figure 5a: (a,b) Graphs of ADC trace values measured from gray matter and white matter structures. Mean value with standard deviation are shown. ADC trace values from hippocampus (HP), temporal lobe (TL) gray matter, and corpus callosum (CC) in patients with MCI were significantly higher relative to those of control subjects (P < .05). AC = anterior cingulate gyru, AM = amygdala, FL = frontal lobe, OL = occipital lobe, PC = posterior cingulate gyru, PHG = parahippocampal gyrus, PL = parietal lobe.

 

Figure 5
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  		Figure 5b: (a,b) Graphs of ADC trace values measured from gray matter and white matter structures. Mean value with standard deviation are shown. ADC trace values from hippocampus (HP), temporal lobe (TL) gray matter, and corpus callosum (CC) in patients with MCI were significantly higher relative to those of control subjects (P < .05). AC = anterior cingulate gyru, AM = amygdala, FL = frontal lobe, OL = occipital lobe, PC = posterior cingulate gyru, PHG = parahippocampal gyrus, PL = parietal lobe.

 

Figure 6
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  		Figure 6a: Correlation plots between performance scores (a) on 5-minute delayed recall test (CWLT5) and (b) 30-minute delayed recall test (CWLT30) with age-adjusted ADC trace values from hippocampus. Lower scores were significantly associated with higher ADC values (P < .05). One outlier ({circ}) of ADC value more than 3.5 standard deviations above sample mean was excluded in regression analysis.

 

Figure 6
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  		Figure 6b: Correlation plots between performance scores (a) on 5-minute delayed recall test (CWLT5) and (b) 30-minute delayed recall test (CWLT30) with age-adjusted ADC trace values from hippocampus. Lower scores were significantly associated with higher ADC values (P < .05). One outlier ({circ}) of ADC value more than 3.5 standard deviations above sample mean was excluded in regression analysis.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
ADVANCES IN KNOWLEDGE
 References
 
The diffusivity of water in tissues is determined by the presence of microscopic structural barriers such as cell membranes, intracellular organelles, axons, and myelin sheaths. The pathologic disruption of these barriers would result in an increase in water diffusivity. The finding of increased hippocampal ADC is in accordance with pathologic studies that demonstrated involvement of the hippocampus very early in the progression of AD. Although the exact mechanisms of increased water diffusivity in AD and MCI have not been established to our knowledge, possible mechanisms include the disruption of the intracellular cytoskeletal framework due to neurofibrillary tangle formation. Neuron loss also accompanies neurofibrillary changes, and the resultant expansion of the extracellular space would also be expected to cause an elevation of ADC. Finally, glial activation has been associated with neuritic plaque formation, and this inflammatory response may also result in expansion of the extracellular space, thus causing a higher ADC (27).

Our finding of increased hippocampal ADC in patients with MCI (P < .001) is in agreement with three prior studies in patients with MCI (11–13). In these previous studies, the ROIs were placed over the structure of interest on diffusion-weighted images. In our analysis, ROI tracing was performed on volumetrically acquired 3D anatomic images, then was subsequently mapped onto the low-spatial-resolution ADC maps. Although weighted averaging was performed and edge voxels (those containing less than 30% hippocampal tissue) were excluded to minimize partial volume contamination, ADC values in our study were higher than the reported values in previous studies, possibly owing to residual cerebrospinal fluid contamination. Nevertheless, our ADC measurements were not correlated with normalized hippocampal volume, indicating that the value was not confounded by atrophy and suggesting that alterations in hippocampal ADC were an independent indicator of microstructural changes resulting from early AD pathologic status. In addition, the results also demonstrated that the ROI selection with the mapping technique employed in this study could replicate previous findings.

The mapping technique allowed one to evaluate the ADC in large regions. In conjunction with segmentation, it also facilitated measurement from gray and white matter of various brain lobes, which could not be done with a manually placed ROI. We found increased diffusivity in the temporal lobe gray matter but not in parietal, frontal, or occipital lobes. The results were consistent with the finding that the temporal lobe was the earliest structure involved in development of pathologic changes in MCI or AD. Widespread neurofibrillary changes may exist in the temporal lobe cortex without dementia as long as polymodal association areas are spared (28). Patients with extensive neurofibrillary burden may be at a high risk of conversion to AD. ADC mapping may provide a means of detecting these subclinical pathologic changes. In the studies of ADC in white matter, an elevated ADC was attributed either to vascular or ischemic origin or to the wallerian degeneration secondary to adjacent cortical degeneration (11,13). Our results showed that temporal gray matter had an elevated ADC, which supported the wallerian degeneration theory for the increased temporal white matter ADC in MCI reported by Fellgiebel et al (13).

We also found elevated diffusivity in the corpus callosum in MCI patients compared with that of control subjects (P < .05). Pathologic studies of the corpus callosum in patients with AD have demonstrated a significant decrease in the number and diameter of nerve fibers in the anterior corpus callosum (29). In a diffusion-weighted imaging study of AD, investigators found an elevated ADC in the anterior and posterior corpus callosum despite the lack of significant atrophy (30). In another study of 3.0-T MR imaging, a lower fractional anisotropy in the posterior corpus callosum was found in patients with AD compared with that of control subjects (10).

Only one study reported the results for MCI. Fellgiebel et al (13) measured the ADC from the genu and splenium of the corpus callosum in AD patients, MCI patients, and in control subjects but did not find significant changes among these three groups. In our study, the ADC from the entire corpus callosum was measured. The difference in the analyzed ROI regions might partially contribute to different findings. Altered diffusivity in the corpus callosum in AD and MCI likely reflects axonal degeneration and/or demyelination secondary to cortical neuronal damage. ADC mapping may be able to help detect disease involvement in the absence of macroscopic atrophy.

Diffusion-tensor MR imaging is a more advanced technique that allows accurate measurement of diffusion anisotropy. Diffusion-tensor imaging is particularly useful in depicting the pathologic changes in white matter where diffusion is directional along the myelin tract. In our study, because the ADC from a large area was studied only the ADC trace value (with x, y, z gradients all on), which was insensitive to head positioning, was reported.

Our finding that hippocampal ADC correlated with severity of memory impairment raises the possibility that elevated hippocampal ADC might correlate with an increased conversion rate to AD. A recent article (31) has demonstrated that an elevated baseline hippocampal ADC in individuals with amnesic MCI predicted an increased likelihood of conversion to AD.

In this study we reported a method to measure ADC from structures that were manually outlined from high-spatial-resolution images and from large regions that were obtained by using automated mapping techniques. However, our study is limited in that there were only 13 participants in each group. Although the elevated hippocampal ADC was consistent with that in previously published studies (11–13), the finding of an elevated ADC in temporal gray matter and corpus callosum will need to be evaluated in studies with a larger sample size.

In conclusion, we have demonstrated that the ADC from various medial temporal lobe structures and the gray and white matter of different brain lobes can be obtained with mapping techniques by using an ROI obtained from 3D high-spatial-resolution anatomic images. Although some cerebrospinal fluid contamination was inevitable, findings consistent with previously published results were obtained. The technique may provide an efficient method for assessing regional changes not limited by placement of small ROI. In combination with other MR-based techniques, such as hippocampal volumetry and hydrogen MR spectroscopy, our technique may provide a means for discriminating between normal aging and MCI and may provide additional information to possibly help predict conversion of MCI to AD. Additional studies with larger numbers of subjects are needed, however, for further evaluation.


   ADVANCES IN KNOWLEDGE
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
References
 


   ACKNOWLEDGMENTS
 
The authors thank Edward Golob, PhD, for his assistance in the initial study setup and subject recruitment and Dwight Tapp, PhD, for his assistance in statistical analysis.


   FOOTNOTES
 

Abbreviations: AD = Alzheimer disease • ADC = apparent diffusion coefficient • MCI = mild cognitive impairment • ROI = region of interest • 3D = three-dimensional

Authors stated no financial relationship to disclose.

Author contributions: Guarantor of integrity of entire study, M.Y.S.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, K.M.R., H.W., Y.F.C., A.N.H., M.Y.S.; statistical analysis, all authors; and manuscript editing, all authors


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 

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