Figures
Abstract
Compared to normal aging adults, individuals with amnestic mild cognitive impairment (aMCI) have significantly increased risk for progressing into Alzheimer’s disease (AD). Autopsy studies found that most of the brains of aMCI cases showed anatomical features associated with AD pathology. The recent development of non-invasive neuroimaging technique, such as diffusion tensor imaging (DTI), makes it possible to investigate the microstructures of the cerebral white matter in vivo. We hypothesized that disrupted white matter (WM) integrity existed in aMCI. So we used DTI technique, by measuring fractional anisotropy (FA) and mean diffusivity (MD), to test the brain structures involved in patients with aMCI. DTI scans were collected from 40 patients with aMCI, and 28 normal controls (NC). Tract-based spatial statistics (TBSS) analyses of whole-brain FA and MD images in each individual and group comparisons were carried out. Compared to NC, aMCI patients showed significant FA reduction bilaterally, in the association and projection fibers of frontal, parietal, and temporal lobes, corpus callosum, bilateral corona radiation, right posterior thalamic radiation and right sagittal stratum. aMCI patients also showed significantly increased MD widespreadly in the association and projection fibers of frontal, parietal and temporal lobes, and corpus callosum. Assessment of the WM integrity of the frontal, parietal, temporal lobes, and corpus callosum by using DTI measures may aid early diagnosis of aMCI.
Citation: Liu J, Yin C, Xia S, Jia L, Guo Y, Zhao Z, et al. (2013) White Matter Changes in Patients with Amnestic Mild Cognitive Impairment Detected by Diffusion Tensor Imaging. PLoS ONE 8(3): e59440. https://doi.org/10.1371/journal.pone.0059440
Editor: Karl Herholz, University of Manchester, United Kingdom
Received: October 6, 2012; Accepted: February 14, 2013; Published: March 21, 2013
Copyright: © 2013 Liu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the National Natural Science Foundation of China (30970823) and the key project of the National Natural Science Foundation of China (30830045). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Mild cognitive impairment (MCI) is a descriptive category that identifies patients with memory impairment beyond that expected for age and education, who do not qualify for a diagnosis of dementia [1]. The term MCI has become widely used, with the suggestion that it is a prodromal state of Alzheimer’s disease (AD) and possibly other dementias [2], [3].
MCI has been classified broadly into amnestic and nonamnestic subtypes. Amnestic MCI (aMCI) presents clinically with memory disturbance as the main feature, although other cognitive domains may be affected as well. aMCI is thought to progress into AD, whereas nonamnestic MCI may progress to the other types of dementia [4]. Individuals with aMCI have heightened risk of progressing to AD [5]. Recent autopsy studies indicate that AD pathology associated anatomical features were found in most of the brains from aMCI patients [6], [7]. A meta-analysis reported the annual conversion rate from aMCI to AD at 5%–10% [8].
Since individuals with aMCI have increased risk of progressing to AD, it is important to appropriately diagnose aMCI and identify features that would predict an early progression to AD. Biomarkers reliably identifying those aMCI patients who are most at risk of progressing to AD would therefore be useful, especially as preventive interventions become available. Diffusion tensor imaging (DTI) may be one such technique. DTI is a quantitative MRI technique that measures the movement of water within the tissue microstructure [9]. Loss of anisotropic diffusion could be related to abnormalities within the cellular microstructure, which provide information about disrupted structural integrity in that area. The integrity of white matter (WM) fiber bundles in the nervous system can be evaluated by fractional anisotrophy (FA) and mean diffusivity (MD) measures [10], [11], [12].
Reduced FA values were reported in WM structures from the temporal and parietal lobes and posterior cingulum bundle in patients with aMCI, compared to controls [13], [14], [15], [16], [17]. In addition, compared to normal aging adults, widespread WM structural abnormalities have been found in patients with AD, including reduced FA and increased MD within the splenium of the corpus callosum [18], [19], [20], [21], [22], the cingulum bundle [14], [17], [23], [24], [25], [26], and cortical regions [13], [15], [18], [20], [26], [27].
Despite of the advances, a discrepancy exists among the findings of the previous DTI studies of AD and aMCI. To give a comprehensive view of the degeneration patterns of the WM in aMCI patients, we used the newly developed tract-based spatial statistics (TBSS) method and multiple diffusion measures (FA and MD) to systematically study aMCI-associated changes in WM tracts across the whole brain. The present study included 40 patients with aMCI and 28 demographically-matched normal control (NC) participants. We hypothesized that WM anomalies in corpus callosum and regions connecting the posterior cingulum and limbic system may be the neurobiological marker of aMCI.
Subjects and Methods
Subjects
Patients with aMCI were recruited from Beijing Xuanwu Hospital of Capital Medical University in China, from January 2009 to December 2011. Normal controls were recruited from local residents of Beijing Xuanwu District. The study was approved by the institutional review board of Beijing Xuanwu Hospital. Written informed consents were obtained from all participants. The patient group included 40 subjects diagnosed with aMCI (age 65.7±7.2 years; 17 males and 23 females; mean education duration 10.3±4.3 years) according to Petersen’s diagnostic criteria [1]. The aMCI patients had the ability to understand and write the informed consents. The NC group included 28 subjects who were cognitively normal and had a Clinical Dementia Rating (CDR) of 0 [28], [29] (63.5±7.2 years; 11 males and 17 females; mean education duration10.5±4.1 years). All participants underwent a series of neurological tests and a battery of neuropsychological assessments, which included the Montreal cognitive assessment (MoCA) [30] and the Clinical Dementia Rating Scale (CDR) [29].
There were no demographic differences between the groups with regards to age, sex and education. In addition, the prevalence of vascular factors such as hypertension, hypercholesterolemia, and heart attack, did not differ between the groups. The subjects had no history of a psychiatric or neuropsychological disease.
DTI Acquisition Protocol
DTI data was acquired from each subject on a Siemens 3.0 Trio Tim MRI system with cranial 12-channel phased array surface coil using an echo planar imaging (EPI) sequence in 32 independent, non-collinear directions of a b-value = 1000 s/mm2, and one additional image with no diffusion weighting (b = 0). TR = 11000 ms, TE = 98 ms, flip angle = 90°, field of view (FOV) = 256 mm×256 mm, imaging matrix = 128×128, number of slices = 60, and slice thickness = 2 mm. Three acquisitions were averaged to increase the signal-to-noise ratio.
Data Processing
DTI data processing was carried out using FSL software (FMRIB Software Library, http: //www.fmrib.ox.ac.uk/fsl) [31]. Initially, eddy current correction was run to correct gradient-coil distortions and small-head motions using affine registration to a reference image (b0 volume) [32]. The brain voxels of DTI data were extracted using the Brain Extraction Tool (BET) [33]. The maps of diffusion tensor parameters including FA and MD were calculated using DTI-FIT tool, which fits a diffusion tensor model to diffusion-weighted images for each voxel.
Voxel-wise statistical analysis of the FA and MD data was performed for regional differences using TBSS [34], which has been reported to be more precise than conventional VBM style analysis. Firstly, images from all individuals were aligned into a 1 × 1 × 1 mm3 Montreal Neurological Institute (MNI) 152 space using the nonlinear registration (b-spline representation of the registration warp field). Next, a mean FA image of all aligned FA images was calculated and thinned to create the mean FA skeleton, which represents the centers of all tracts common for the group. The skeleton's threshold was FA ≥0.20 to ensure that areas of low FA and/or high inter-subject variability were excluded from the analyses. Finally, each subject's aligned FA data was projected onto the skeleton by searching perpendicular to the local skeleton structure for the maximum value in the subject’s skeletonized FA image, and the resulting data was fed into voxel-wise cross-subject statistics. The John Hopkinson University (JHU) white-matter atlas was applied to identify the names of WM bundles that contained the clusters of significant between-group FA differences.
Statistical Analysis
Group statistical analysis was then conducted only on voxels within the white-matter skeleton mask. Differences in FA and MD between aMCI group and normal control were assessed using voxel-wise two-sample t -tests. Nonparametric permutation tests and Threshold-Free Cluster Enhancement (TFCE) option were conducted based on 5,000 random permutations. The clusters with a TFCE-corrected P-value of less than 0.05 or 0.0001 were reported. The fiber tracts and their corresponding clusters were identified using the Johns Hopkins University DTI-based White Matter Atlas [35].
Demographic data was analyzed by using two-sample t-test.
Results
Demographic Information
As shown in Table 1, there were no significant demographic differences between the two groups.
Reduction of FA in aMCI Patients
Between-group comparisons of the voxel-wise FA values showed that patients with aMCI exhibited significantly reduced FA values (TFCE-corrected p<0.05) in WM clusters over large areas bilaterally, mainly in the frontal, parietal, and temporal lobes, the whole corpus callosum, and its association fibers including the superior longitudinal fasciculus, external capsule, cingulum bundle, sagittal stratum and fornix, projection fibers including internal capsule, corona radiation, and thalamic radiation (Figure 1). The clusters with even higher between-group differences (with a TFCE–corrected significance of p<0.0001) were observed in regions of corpus callosum, bilateral corona radiation, right posterior thalamic radiation and right sagittal stratum (Figure 2 and Table 2). We did not find brain clusters with significantly increased FA values in the aMCI group, compared to the normal controls.
Increase of MD in aMCI Patients
Between-group comparisons of the voxel-wise MD values showed that patients with aMCI exhibited significantly increased MD values (TFCE-corrected p<0.05) in WM clusters bilaterally, especially in the frontal, parietal, and temporal lobes, corpus callosum, association fibers (including the superior longitudinal fasciculus, external capsule, cingulum bundle, sagittal stratum), projection fibers (including internal capsule, corona radiation, thalamic radiation) (see details from Figure 3). The clusters with even higher between-group differences (with a TFCE–corrected significance of p<0.0001) were observed in the right Retrolenticular part of internal capsule, right posterior thalamic radiation and right sagittal stratum (Figure 4 and Table 3). We did not find brain clusters with significantly decreased MD values in the aMCI group, compared to the normal controls.
Discussion
Since aMCI is a high-risk status for developing AD, it is important to appropriately diagnose aMCI and identify features that would predict an early progression to AD. By comparing the FA and MD from DTI data, the present study indicated that, in aMCI patients, the FA and MD were significantly changed over large brain areas. These results suggest that FA and MD can reflect the pathological features of aMCI patients and may be considered as a tool for the early prediction of aMCI.
A recent study found that FA in the left frontal superior gyrus and MD in the genu of the corpus callosum, left anterior cingulated gyrus, and right corona radiata emerged as the best combination for predicting aMCI [36]. Our results are consistent with previous studies which have reported changes in anterior and posterior cingulate, corpus callosum, and frontal lobe [13], [14], [17], [37]. Besides these, we observed increased MD in subcortical regions such as the right posterior thalamic radiation and internal capsule. The internal capsule and corona radiata contain projection fibers traveling from the cortices to the brainstem and from the thalamus to the cortices. These regions are supplied by penetrating arteries and thus are susceptible to ischemic damage. Further studies are needed to determine the contribution of vascular disease and risk factors to changes in the basal ganglia and corona radiata.
Decreased FA in the corpus callosum has been seen in AD [38]. Gray matter (GM) atrophy has been observed in parietal and temporal lobes, posterior cingulate, and hippocampus in aMCI [39], [40], [41]. Other studies also indicated GM atrophic change in the hippocampus of aMCI brain [42], [43]. Therefore, hippocampal atrophy and disruption of hippocampo-parieto-frontal WM network are thought as the key pathogenesis of AD and aMCI [44]. Both AD and aMCI patients suffer gradually impaired memory [45], which is highly associated with the disturbance of hippocampal synaptic plasticity that is modulated by multiple molecules and mechanisms [46], [47]. Although the present study did not observe significant change of FA and MD around hippocampus, we did find change of the frontal and parietal WM as well as frontal WM and tracts connecting with limbic system, suggesting that WM disturbance may be independent of GM changes in aMCI patients [48].
Teipel et al. [49] showed decreased functional connectivity in posterior regions in aMCI using combined EEG and DTI. Some studies have found especially large reductions in FA in parahippocampal regions [17], [26], [50]. Furthermore, another study proved the association between parahippocampal WM change and declarative memory performance in aMCI patients [51], while the present study did not detect change in parahippocampal WM but did detect changes in many associate fibers and projecting fibers. These inconsistence may be resulted from different methods and standardization. Ward et al [52] suggested standardized definition and better techniques for aMCI and AD was needed.
Previous studies have found changes in the anterior cingulate in aMCI using DTI [36]. A more recent study found that aMCI participants showed lower posterior cingulated (PC) WM integrity relative to those with non-aMCI. Findings implicate involvement of posterior microstructural WM degeneration in the development of MCI-related cognitive changes and suggest that reduced FA of the PC may be a candidate neuroimaging marker of AD risk [53]. In Delano-Wood L’s study, both non-aMCI and aMCI subgroups showed degradation of the PC, although those with aMCI showed more profound changes in this region coupled with additional changes to the splenium and parahippocampal white matter. In our study, we did not find difference of PC between aMCI and NC. The variation may be due to differences in the study population and method of DTI analysis. Future tractography studies may be able to determine the extent of changes more reliably in both the anterior and posterior cingulate.
We observed changes in almost the whole corpus callosum. But the mechanisms of the changes are not the same. The changes in the splenium of the corpus callosum might because of Wallerian degeneration. On the other hand, the genu of the corpus callosum is known to myelinate much later in development when compared to other WM regions, and the changes in the genu of the corpus callosum in aMCI patientsof our results might support the retrogenesis hypothesis [54] which posits that later-myelinating fibers (e.g., limbic pathways such as the cingulum, see from [55], are more susceptible to myelin breakdown than earlier myelinating fibers (e.g., posterior limb of the internal capsule). So it seems plausible to affirm that both Wallerian degeneration and myelin breakdown mechanisms are responsible for the region-specific illness effects. In this view, Wallerian degeneration affects the posterior corpus callosum subregion that receives axons directly from those brain areas (temporo-parietal lobe regions) that are primarily affected by AD pathology. Differently, the myelin breakdown process might affect the later myelinating corpus callosum subregion, causing changes in the genu of the corpus callosum [56]. In addition, Agosta and collages found aMCI patients only showed increased axial diffusivity in tracts projecting to the frontal cortex and splenium of the corpus callosum [57], which is consistent with present study and previous study [58] showing significant change of corpus callosum, and suggests the importance of corpus callosum in aMCI patients. By the approach of TBSS, Preti et al detected voxels with a statistically significant FA reduction in every corpus callosum portion of aMCI patients compared to health control [59]. We tested the hypothesis that both Wallerian degeneration and myelin breakdown might be responsible for the region-specific callosal change in aMCI patients and the results were coincidence with those of the mild AD patients in a VBM and DTI study [60] which suggest that both mechanisms affecting the callosal WM are present. Overall, all of these findings suggest that damage of corpus callosum may be a significant feature of aMCI.
Sperling et al. [61] suggested that, WM changes may be initiated through the accumulation of amyloid-β which may subsequently trigger the release and activation of various pro-inflammatory neurotoxic substances and microglia [62], [63]. Although amyloid plaques and neurofibrillary tangle formation have been considered as two hallmarks of AD [45], they are not so easy to be detected by non-invasive methods, particular in the early aMCI stage. Recently, DTI-based FA and MD measures are considered useful for predicting disease progression. It was reported that FA, MD and hippocampal volumes were good predictors of progression of AD, with likelihood ratios >83, and more than 90% accuracy [64]. In another study, MD was found to be correlated positively with the atrophy level in aMCI patients in the right temporal portion of the uncinate, middle longitudinal and inferior longitudinal fasciculi and in the parathalamic WM, the fornix and the posterior cingulum of the right hemisphere; whilst, FA was associated with atrophy in all WM areas except the middle longitudinal fasciculus [65]. Furthermore, it was reported that DTI had a sensitivity of 80% and specificity of 60.3% in distinguishing aMCI from non-aMCI and the normal group [66]. These studies indicated that FA and MD from DTI data are two indices with significant values in early diagnosis of aMCI. Of course, there were some inconsistentce about it. For example, it was found that although there was a significant increase of MD in parahippocampal WM but no difference of FA [51]. And there were also inconsistency about the changes of FA and MD in different brain area [52]. These inconsistence remains to be elucidated by further study using more sophisticated techniques.
The findings of the present study suggest that FA reduction and/or MD increase in the cingulum bundle may be a reliable marker of the onset of aMCI, and the progressing of the disrupted cingulum bundle WM integrity could be useful for assessing the progressing of the disease from aMCI into AD. To validate these hypotheses, the results of this study need to be replicated in a larger sample. Longitudinal studies are needed to follow-up the clinical status of the participants involved in this study, and to examine whether the participants, who developed into AD from aMCI, would have significantly severe and faster cingulum bundle WM anomalies, compared to the WM status in other brain regions in the same subjects with AD and the whole brain WM status in those who did not develop into AD.
Author Contributions
Conceived and designed the experiments: YH. Performed the experiments: JL CY. Analyzed the data: SX XL. Contributed reagents/materials/analysis tools: LJ YG SX ZZ JJ. Wrote the paper: JL CY XL YH.
References
- 1. Petersen RC (2004) Mild cognitive impairment as a diagnostic entity. J Intern Med 256: 183–194.
- 2. Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, et al. (2001) Current concepts in mild cognitive impairment. Arch Neurol 58: 1985–1992.
- 3. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, et al. (1999) Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 56: 303–308.
- 4. Knopman DS, Boeve BF, Petersen RC (2003) Essentials of the proper diagnoses of mild cognitive impairment, dementia, and major subtypes of dementia. Mayo Clin Proc 78: 1290–1308.
- 5.
Petersen RC, Morris JC (2005) Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol 62: 1160–1163; discussion 1167.
- 6. Bennett DA, Schneider JA, Bienias JL, Evans DA, Wilson RS (2005) Mild cognitive impairment is related to Alzheimer disease pathology and cerebral infarctions. Neurology 64: 834–841.
- 7. Jicha GA, Parisi JE, Dickson DW, Johnson K, Cha R, et al. (2006) Neuropathologic outcome of mild cognitive impairment following progression to clinical dementia. Arch Neurol 63: 674–681.
- 8. Mitchell AJ, Shiri-Feshki M (2009) Rate of progression of mild cognitive impairment to dementia–meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand 119: 252–265.
- 9. Le Bihan D, Turner R, Douek P, Patronas N (1992) Diffusion MR imaging: clinical applications. AJR Am J Roentgenol 159: 591–599.
- 10. Basser PJ, Jones DK (2002) Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR Biomed 15: 456–467.
- 11. Beaulieu C, Does MD, Snyder RE, Allen PS (1996) Changes in water diffusion due to Wallerian degeneration in peripheral nerve. Magn Reson Med 36: 627–631.
- 12. Mori S, Crain BJ, Chacko VP, van Zijl PC (1999) Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45: 265–269.
- 13. Medina D, DeToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M, et al. (2006) White matter changes in mild cognitive impairment and AD: A diffusion tensor imaging study. Neurobiol Aging 27: 663–672.
- 14. Fellgiebel A, Müller MJ, Wille P, Dellani PR, Scheurich A, et al. (2005) Color-coded diffusion-tensor-imaging of posterior cingulate fiber tracts in mild cognitive impairment. Neurobiol Aging 26: 1193–1198.
- 15. Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF, et al. (2007) White matter damage in Alzheimer disease and mild cognitive impairment: assessment with diffusion-tensor MR imaging and parallel imaging techniques. Radiology 243: 483–492.
- 16. Rose SE, McMahon KL, Janke AL, O'Dowd B, de Zubicaray G, et al. (2006) Diffusion indices on magnetic resonance imaging and neuropsychological performance in amnestic mild cognitive impairment. J Neurol Neurosurg Psychiatry 77: 1122–1128.
- 17. Zhang Y, Schuff N, Jahng GH, Bayne W, Mori S, et al. (2007) Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 68: 13–19.
- 18. Bozzali M, Falini A, Franceschi M, Cercignani M, Zuffi M, et al. (2002) White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry 72: 742–746.
- 19. Duan JH, Wang HQ, Xu J, Lin X, Chen SQ, et al. (2006) White matter damage of patients with Alzheimer's disease correlated with the decreased cognitive function. Surg Radiol Anat 28: 150–156.
- 20. Naggara O, Oppenheim C, Rieu D, Raoux N, Rodrigo S, et al. (2006) Diffusion tensor imaging in early Alzheimer's disease. Psychiatry Res 146: 243–249.
- 21. Rose SE, Chen F, Chalk JB, Zelaya FO, Strugnell WE, et al. (2000) Loss of connectivity in Alzheimer's disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. J Neurol Neurosurg Psychiatry 69: 528–530.
- 22. Sydykova D, Stahl R, Dietrich O, Ewers M, Reiser MF, et al. (2007) Fiber connections between the cerebral cortex and the corpus callosum in Alzheimer's disease: a diffusion tensor imaging and voxel-based morphometry study. Cereb Cortex 17 2276–2282.
- 23. Cho H, Yang DW, Shon YM, Kim BS, Kim YI, et al. (2008) Abnormal integrity of corticocortical tracts in mild cognitive impairment: a diffusion tensor imaging study. J Korean Med Sci 23: 477–483.
- 24. Ding B, Chen KM, Ling HW, Zhang H, Chai WM, et al. (2008) Diffusion tensor imaging correlates with proton magnetic resonance spectroscopy in posterior cingulate region of patients with Alzheimer's disease. Dement Geriatr Cogn Disord 25: 218–225.
- 25. Fellgiebel A, Schermuly I, Gerhard A, Keller I, Albrecht J, et al. (2008) Functional relevant loss of long association fibre tracts integrity in early Alzheimer's disease. Neuropsychologia 46: 1698–1706.
- 26. Takahashi S, Yonezawa H, Takahashi J, Kudo M, Inoue T, et al. (2002) Selective reduction of diffusion anisotropy in white matter of Alzheimer disease brains measured by 3.0 Tesla magnetic resonance imaging. Neurosci Lett 332: 45–48.
- 27. Choi SJ, Lim KO, Monteiro I, Reisberg B (2005) Diffusion tensor imaging of frontal white matter microstructure in early Alzheimer's disease: a preliminary study. J Geriatr Psychiatry Neurol 18: 12–19.
- 28. Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL (1982) A new clinical scale for the staging of dementia. Br J Psychiatry 140: 566–572.
- 29. Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43: 2412–2414.
- 30. Nasreddine ZS, Phillips NA, Bedirian V, Charbonneau S, Whitehead V, et al. (2005) The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 53: 695–699.
- 31. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, et al. (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 Suppl 1S208–219.
- 32. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17: 825–841.
- 33. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17: 143–155.
- 34. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, et al. (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31: 1487–1505.
- 35. Hua K, Zhang J, Wakana S, Jiang H, Li X, et al. (2008) Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39: 336–347.
- 36. Thillainadesan S, Wen W, Zhuang L, Crawford J, Kochan N, et al. (2012) Changes in mild cognitive impairment and its subtypes as seen on diffusion tensor imaging. Int Psychogeriatr 24: 1483–1493.
- 37. Shim YS, Yoon B, Shon YM, Ahn KJ, Yang DW (2008) Difference of the hippocampal and white matter microalterations in MCI patients according to the severity of subcortical vascular changes: neuropsychological correlates of diffusion tensor imaging. Clin Neurol Neurosurg 110: 552–5561.
- 38. Xie S, Xiao JX, Gong GL, Zang YF, Wang YH, et al. (2006) Voxel-based detection of white matter abnormalities in mild Alzheimer disease. Neurology 66: 1845–1849.
- 39. Whitwell JL, Petersen RC, Negash S, Weigand SD, Kantarci K, et al. (2007) Patterns of atrophy differ among specific subtypes of mild cognitive impairment. Arch Neurol 64: 1130–1138.
- 40. He J, Farias S, Martinez O, Reed B, Mungas D, et al. (2009) Differences in brain volume, hippocampal volume, cerebrovascular risk factors, and apolipoprotein E4 among mild cognitive impairment subtypes. Arch Neurol 66: 1393–1399.
- 41. van de Pol LA, Verhey F, Frisoni GB, Tsolaki M, Papapostolou P, et al. (2009) White matter hyperintensities and medial temporal lobe atrophy in clinical subtypes of mild cognitive impairment: the DESCRIPA study. J Neurol Neurosurg Psychiatry 80: 1069–1074.
- 42. Han Y, Lui S, Kuang W, Lang Q, Zou L, et al. (2012) Anatomical and functional deficits in patients with amnestic mild cognitive impairment. PLoS One 7: e28664.
- 43.
Son SJ, Lee KS, Na DL, Seo SW, Kim CH, et al.. (2011) The effect of anemia and white matter hyperintensities (WMH) on cognitive impairment in patients with amnestic mild cognitive impairment (MCI). Arch Gerontol Geriatr Nov 15. [Epub ahead of print].
- 44. Fouquet M, Desgranges B, La Joie R, Rivière D, Mangin JF, et al. (2012) Role of hippocampal CA1 atrophy in memory encoding deficits in amnestic Mild Cognitive Impairment. Neuroimage 59: 3309–3315.
- 45. Satio Y, Murayama S (2007) Neuropathology of mild cognitive impairment. Neuropathology 27: 578–584.
- 46. Xie YF, Macdonald JF, Jackson MF (2010) TRPM2, calcium and neurodegenerative diseases. Int J Physiol Pathophysiol Pharmacol 2: 95–103.
- 47. Xie YF, Belrose JC, Lei G, Tymianski M, Mori Y, et al. (2011) Dependence of NMDA/GSK-3β mediated metaplasticity on TRPM2 channels at hippocampal CA3-CA1 synapses. Mol Brain 4: 44.
- 48. Bai F, Zhang Z, Watson DR, Yu H, Shi Y, et al. (2009a) Abnormal white matter independent of hippocampal atrophy in amnestic type mild cognitive impairment. Neurosci Lett 462: 147–151.
- 49. Teipel SJ, Pogarell O, Meindl T, Dietrich O, Sydykova D, et al. (2009) Regional networks underlying interhemispheric connectivity: an EEG and DTI study in healthy ageing and amnestic mild cognitive impairment. Hum Brain Mapp 30: 2098–2119.
- 50. Jak AJ, Houston WS, Nagel BJ, Corey-Bloom J, Bondi MW (2007) Differential cross-sectional and longitudinal impact of APOE genotype on hippocampal volumes in nondemented older adults. Dement Geriatr Cogn Disord 23: 382–389.
- 51. Rogalski EJ, Murphy CM, deToledo-Morrell L, Shah RC, Moseley ME, et al. (2009) Changes in parahippocampal white matter integrity in amnestic mild cognitive impairment: a diffusion tensor imaging study. Behav Neurol 21: 51–61.
- 52. Ward A, Arrighi HM, Michels S, Cedarbaum JM (2012) Mild cognitive impairment: disparity of incidence and prevalence estimates. Alzheimers Dement 8: 14–21.
- 53. Delano-Wood L, Stricker NH, Sorg SF, Nation DA, Jak AJ, et al. (2012) Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment. J Alzheimers Dis. 29: 589–603.
- 54. Ringman JM, O'Neill J, Geschwind D, Medina L, Apostolova LG, et al. (2007) Diffusion tensor imaging in preclinical and presymptomatic carriers of familial Alzheimer's disease mutations. Brain 130: 1767–1776.
- 55. Kinney HC, Brody BA, Kloman AS, Gilles FH (1988) Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants. J Neuropathol Exp Neurol 47: 217–234.
- 56. Di Paola M, Spalletta G, Caltagirone C (2010) In vivo structural neuroanatomy of corpus callosum in Alzheimer's disease and mild cognitive impairment using different MRI techniques: a review. J Alzheimers Dis 20(1): 67–95.
- 57. Agosta F, Pievani M, Sala S, Geroldi C, Galluzzi S, et al. (2011) White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology 258: 853–863.
- 58. Bai F, Zhang Z, Watson DR, Yu H, Shi Y, et al. (2009b) Abnormal integrity of association fiber tracts in amnestic mild cognitive impairment. J Neurol Sci 278: 102–106.
- 59. Preti MG, Baglio F, Laganà MM, Griffanti L, Nemni R, et al. (2012) Assessing corpus callosum changes in Alzheimer's disease: comparison betweentract-based spatial statistics and atlas-based tractography. Plos One 7(4): e35856.
- 60. Di Paola M, Di Iulio F, Cherubini A, Blundo C, Casini AR, et al. (2010) When, where, and how the corpus callosum changes in MCI and AD: a multimodal MRI study. Neurology 74(14): 1136–1142.
- 61. Sperling RA, Dickerson BC, Pihlajamaki M, Vannini P, LaViolette PS, et al. (2010) Functional alterations in memory networks in early Alzheimer's disease. Neuromolecular Med 12 27–43.
- 62. Chen K, Iribarren P, Hu J, Chen J, Gong W, et al. (2006) Activation of Toll-like receptor 2 on microglia promotes cell uptake of Alzheimer disease-associated amyloid beta peptide. J Biol Chem 281: 3651–3659.
- 63. Tahara K, Kim HD, Jin JJ, Maxwell JA, Li L, et al. (2006) Role of toll-like receptor signalling in Abeta uptake and clearance. Brain 129: 3006–3019.
- 64. Mielke MM, Okonkwo OC, Oishi K, Mori S, Tighe S, et al. (2012) Fornix integrity and hippocampal volume predict memory decline and progression to Alzheimer's disease. Alzheimers Dement 8: 105–113.
- 65. Cacciari C, Moraschi M, Di Paola M, Cherubini A, Orfei MD, et al. (2010) White matter microstructure and apathy level in amnestic mild cognitive impairment. J Alzheimers Dis 20: 501–507.
- 66. Chua TC, Wen W, Chen X, Kochan N, Slavin MJ, et al. (2009) Diffusion tensor imaging of the posterior cingulate is a useful biomarker of mild cognitive impairment. Am J Geriatr Psychiatry 17: 602–613.
- 67. Lu J, Li D, Li F, Zhou A, Wanf F, et al. (2011) Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: a population-based study. J Geriatr Psychiatry Neurol 24: 184–190.