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Abnormal white matter independent of hippocampal atrophy in amnestic type mild cognitive impairment
Neuroscience Letters 462 (2009) 147–151
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Abnormal white matter independent of hippocampal atrophy in amnestic type mild cognitive impairment Feng Bai a,b , Zhijun Zhang a,b,∗ , David R. Watson c , Hui Yu a,b , Yongmei Shi a,b , Yonggui Yuan a,d a
School of Clinical Medicine, Southeast University, Nanjing 210009, China The Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China Psychiatry, School of Medicine and Dentistry, Queen’s University Belfast, BT9 7BL, Belfast, UK d Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China b c
a r t i c l e
i n f o
Article history: Received 2 April 2009 Received in revised form 1 July 2009 Accepted 5 July 2009 Keywords: Amnestic type mild cognitive impairment Diffusion tensor imaging Hippocampus White matter Gray matter
a b s t r a c t Hippocampal atrophy is the key marker in the pathogenesis of Alzheimer’s disease (AD), which is associated with white matter (WM) disruption. This type of WM disruption could partly explain AD-related pathology. However, relatively little attention has been directed toward WM disruption which may be independent of these fundamental gray matter (GM) changes in amnestic mild cognitive impairment (aMCI) which is associated with high risk of AD. To evaluate the differences of WM integrity between aMCI patients (N = 32) and healthy controls (N = 31), whole-brain voxel-based methods were applied to diffusion tensor imaging. To explore the possible independence of WM changes from GM loss, an index of hippocampal atrophy was used to partial out GM effects. aMCI patients showed WM disruption in frontal lobe, temporal lobe, internal capsule, cingulate gyrus and precuneus. The findings supported the evidence of independent patterns of degeneration in WM tracts which may co-act in the WM pathological process of aMCI patients. As aMCI is a putatively prodromal syndrome to AD, these data may assist with a better understanding of WM pathological change associated with the development of AD. Crown Copyright © 2009 Published by Elsevier Ireland Ltd. All rights reserved.
Mild cognitive impairment (MCI) refers to a transitional stage between normal aging and dementia [23]. This can be divided into subgroups of amnestic and non-amnestic type according to memory impairment, and both subtypes can further be classified as single or multiple domains basing on the number of cognitive domains affected [11,15,24,35]. The outcome of nonamnestic MCI is often frontotemporal or Lewy body dementia [11,37], whereas amnestic subtype of MCI (aMCI) is believed to represent a high risk of Alzheimer’s disease (AD) [11,13]. Histological research has demonstrated that the disruption of white matter (WM) microstructure [4,29] which has been described as the underlying pathology of the progressive cortical disconnection syndrome in AD [7]. In addition, a recently developed magnetic resonance imaging modality, diffusion tensor imaging (DTI), has shown extensive alteration in WM integrity of frontal, temporal, and parietal lobes in both MCI [18,27,31,38] and AD patients [14,21]. Therefore, the integrity of WM is believed to be an important neural substrate of AD-related brain structural changes.
∗ Corresponding author at: Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China. Tel.: +86 25 83272023; fax: +86 25 83272023. E-mail address: [email protected] (Z. Zhang).
The hippocampus is one of the first areas to demonstrate gray matter (GM) pathological changes as AD develops, and at the endstage of the disease these regions are also the ones most severely affected [3]. Previous studies have suggested that reduction in hippocampal volume represents the most important structural hallmark for conversion from MCI to AD [2,6,22]. Importantly, previous studies have also shown that hippocampal GM changes are associated with altered WM. For example in AD patients, hippocampal atrophy is concurrent with WM disruption of posterior cingulate bundle [10,34], and frontal and parieto-occipital lobes [8]. In addition, Xie and colleagues in a DTI study of mild AD patients reported that the disruption in WM of medial temporal lobe (hippocampus, parahippocampal gyrus and amygdale) was closely related to the observed cortical GM volumetric reductions, and Walleriantype degeneration was considered to be the possible pathological mechanism for the WM changes noted [36]. However, they also concluded that there was evidence that WM showed a different pattern of degeneration and suggested that partial WM degeneration may be an independent factor in AD progression [36]. Others have also suggested that WM disruption was somewhat independent of hippocampal atrophy in AD patients [26]. It is still unknown what if any role these WM changes play in conversion from aMCI to AD. Therefore, it may be prudent to try and explore WM integrity in circumstances where effects of hippocampal atrophy have been partially out, as it may contribute to our understanding of the
0304-3940/$ – see front matter. Crown Copyright © 2009 Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.neulet.2009.07.009
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exact contribution WM pathology makes in sufferers of AD-related disease. The present study attempted to investigate this issue, using the DTI technique, examining microstructural alterations in WM by measuring the directionality of molecular diffusion of aMCI patients. Some scalar indices such as fractional anisotropy (FA), relative anisotropy (RA), volume ratio (VR), are quite sensitive to tissue inhomogeneity from crossing fibers and partial volume, and have been proposed to characterize the diffusion anisotropic extent and indicate the preferred index of decreasing WM health. Based on the important role damage to the hippocampus is believed to play in AD-related WM disruption, it is reasonable to try remove the effect of this damage, in an attempt to clarify other independent WM disturbances contributing to any observed changes in aMCI patients. The present study recruited 63 elderly individuals (all Chinese Han) including 32 aMCI subjects and 31 healthy controls (all right handed), through normal community health screening and newspaper advertisement. Presence of aMCI (including aMCIsingle domain and aMCI-multiple domain) was made essentially following Petersen’s [23] and others’ [35] recommendations: (1) subjective memory impairment corroborated by subject and an informant; (2) objective memory performance documented by delayed recall of auditory verbal learning test; (3) Clinical Dementia Rating (CDR) of 0.5, with at least a 0.5 in the memory domain; (4) normal general cognitive functioning evaluated by the Mini Mental State Exam (MMSE) score greater than or equal to 24; (5) no or minimal impairment in 14 daily living activities (as determined by clinician interview with the subject and informant); (6) absence of dementia, not sufficient to meet National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association criteria for AD. Participants were excluded from the study if they had a past history of known stroke (modified Hachinski score > 4), alcoholism, head injury, Parkinson’s disease, epilepsy, major depression (excluded by Self-rating Depression Scale) or other neurological or psychiatric illness (excluded by clinical assessment and case history), major medical illness (e.g. cancer, anaemia, thyroid dysfunction), severe visual or hearing loss. Controls were required to have a CDR of 0, an MMSE score ≥26, delayed recall score >4 for those with 8 or more years of education. The study was approved by the Research Ethics Committee of Affiliated ZhongDa Hospital, Southeast University and written informed consent was obtained from all participants. The subjects were scanned using a General Electric 1.5 T scanner (General Electric Medical Systems, USA) with a homogeneous birdcage head coil. High-resolution T1-weighted axial images covering the whole brain were acquired using a 3D spoiled gradient echo (SPGR) sequence as shown in the follow: repetition time (TR) = 9.9 ms; echo time (TE) = 2.1 ms; flip angle = 15◦ ; acquisition matrix = 256 × 192; field of view (FOV) = 240 mm × 240 mm; thickness = 2.0 mm; gap = 0 mm; number of excitations (NEX) = 1.0. Diffusion weighted imaging was acquired with single shot echo planar imaging sequence in alignment with the anterior–posterior commissure plane. The diffusion sensitizing gradients were applied along 25 non-collinear directions (b = 1000 s/mm2 ), together with an acquisition without diffusion weighting (b = 0). Thirty contiguous axial slices were acquired with a slice thickness of 4 mm and no gap. The acquisition parameters were as follows: TR = 10 000 ms; TE = 81.2 ms; flip angle = 90◦ ; acquisition matrix = 128 × 128; FOV = 240 mm × 240 mm; NEX = 2.0. Structural data analysis was performed using Statistical Parametric Mapping software (SPM5, http://www.fil.ion. ucl.ac.uk/spm). First, the images were normalized to the Montreal Neurological Institute (MNI) template using an affine and nonlinear spatial normalization, and re-sampled to a voxel size of 1 mm × 1 mm × 1 mm. Second, the normalized images were
segmented into gray matter, white matter and cerebrospinal fluid segments according to MNI prior probability maps. Then, Jacobian modulation was applied to the segmented gray matter image [12], which can be incorporated to compensate for the effect of spatial normalization. Finally, the extracted gray matter set was smoothed with 8-mm full width at half maximum Gaussian filter to reduce effects of individual variation in gyral anatomy and to increase the signal-to-noise ratio. Following the described process, region of interest (hippocampus, left and right separately) was isolated using automated anatomical labeling [33] implemented through wfu PickAtlas software [16,17]. Then, the hippocampal regions were interpolated to the same dimension, size and origin with individual image. Finally, a mean volume index of all voxels of hippocampal region (left and right) was computed for each subject. These analyses were performed using a semi-automated imaging analysis program developed at Institute of Automation, Chinese Academy of Sciences (Dr. W.L. Zhu). DTI data was calculated off-line with DTI studio software, Version 2.40 (Johns Hopkins University, Baltimore, MD). Firstly, the maps of FA, RA and VR were created for individual images. Secondly, to facilitate voxel by voxel comparisons between groups, the b = 0 images of all subjects were normalized to the standard T2 template of Statistical Parametric Mapping software (SPM2, www.fil.ion.ucl.ac.uk/spm) using an affine and nonlinear spatial normalization algorithm. Then the FA, RA and VR maps were normalized by applying the normalization parameters determined from the normalization of the b = 0 images. This normalization process re-sampled the volumes into a 1 mm × 1 mm × 1 mm voxel size. Finally, to remove possible effects of hippocampal atrophy on the results, mean volume index of hippocampus, obtained from the structural scan hippocampus data, was introduced as a covariate into random effects, voxel by voxel between-group comparisons of FA, RA and VR were investigated at a statistical threshold of p < 0.005 and an extent threshold more than 100 mm3 using the two-sample t-test. Nonparametric Mann–Whitney U-tests (MWU) were used for group comparisons of demographic, neuropsychological performance and hippocampal volume (statistical significance was set at p < 0.05). These statistical analyses were performed using SPSS 11.5 software (SPSS, Inc., Chicago, IL, USA). Demographic and neuropsychological scores were shown in Table 1. Compared with healthy controls, aMCI patients had poor performance in the Auditory Verbal Learning Test, the Trail Making Test-A and B, Symbol Digit Modality Test and Digit Span Test. Other test battery indices showed no significant differences between groups. The volume index extracted from the automated segmentation of hippocampus for the groups show that hippocampal volumes of aMCI patients were 6.3% smaller on the left side (p < 0.037), and 7.2% smaller on the right side (p < 0.013) than healthy controls. Compared with the healthy controls, aMCI patients also displayed significant and extensive disruption of WM in frontal lobe, sub-lobar/extra nuclear, temporal lobe and parietal lobe (see Table 2 and Fig. 1). Importantly, WM disruption of the right internal capsule, right cingulate gyrus, right superior temporal gyrus, right middle temporal gyrus and right precuneus were consistently observed in FA, RA and VR maps in the aMCI patients (Fig. 1). The present study characterized in vivo changes in WM integrity of aMCI patients using whole-brain diffusion tensor imaging. Importantly, even when the primary brain changes reflected by hippocampal atrophy which has been are corrected, aMCI patients showed independent WM disruption in frontal lobe, temporal lobe, internal capsule, cingulate gyrus and precuneus. These findings may assist with a better understanding the essential differences between GM and WM pathological change associated with the development of AD. It provided further evidence of independent
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Table 1 Demographic and neuropsychological data between groups. Items
aMCI subjects (n = 32)
Healthy controls (n = 31)
Range
Mean ± SD
Range
Age Education Gender Clinical Dementia Rating (CDR) Mini Mental State Exam (MMSE) Auditory verbal learning test-delayed recall Trail making test-A (s) Trail making test-B (s) Symbol digit modalities test Clock drawing test Digit span test
62–83 6–18 16m/16f 0.5 24–30 0–4 36–180 100–400 11–46 2–10 8–16
71.4 ± 13.6 ± – – 26.8 ± 2.5 ± 101.9 ± 212.9 ± 24.5 ± 8.1 ± 12.0 ±
63–80 6–18 16m/15f 0 26–30 4–12 30–148 64–270 11–60 5–10 7–17
5.0 3.3
1.5 1.3 36.5 75.5 9.6 1.8 2.0
p (MWU)
Mean ± SD 70.4 ± 14.4 ± – – 28.3 ± 7.8 ± 70.7 ± 137.6 ± 34.4 ± 8.9 ± 13.0 ±
5.1 3.1
1.3 1.9 27.9 46.1 10.8 1.1 2.2
NS NS NS – < 0.001 < 0.001 <0.001 <0.001 0.001 NS 0.047
Values are mean (SD); m: male; f: female; MWU: Mann–Whitney U-test.
GM and WM disturbances in aMCI patients prior to conversion to AD. In previous AD-related studies, WM disruption has been noted to occur in concert with adjacent [10,25,34,36] and distant GM atrophy [8]. However, recently both Xie et al. [36] and Salat et al. [26] reported that WM pathology may be somewhat independent of hippocampal volume changes in AD patients. The present study has replicated this finding in aMCI patients, suggesting that WM changes independent of developing GM deficits were part of the early stage of AD neuropathology. This WM disruption may reflect different underlying neuropathology. For example, Englund [9] reported WM rarefaction might be associated with axonal damage and gliosis, and this type of change could be diffuse. Budde et al. [4] found WM changes were independent of regional cortical degeneration and may represent a unique myelin-related pathology. Sjobeck et al. [29] have supported the idea of Wallerian-type degeneration in AD progression, and WM impaired regions appear close to cortical areas with the pathological burden. Importantly, Wallerian-type degeneration is also considered to be a possible
pathological mechanism of WM changes in the development of AD [36]. Although AD-related disease is generally considered to affect primarily GM regions (especially hippocampus), WM disruption was found independent of hippocampal GM changes in the present study, supporting that WM disruption might play a role in the neuropathology and the development of AD. In this study, the aMCI patients were observed to have cognitive impairment mainly in episodic memory (auditory verbal learning test-delayed recall), sensorimotor function (trail making test-A, B) and attention function (symbol digit modalities test). The processes involved in all forms of higher cognitive function, which are not isolated to specific brain regions but instead result from highly interconnected neural circuits encompassing often remote brain regions. WM fiber tracts play an important role in connecting different cortical regions through direct and indirect fibers communication. For example, the internal capsule is the main structure of the fasciculi of the corticothalamic and thalamocortical connections and has complicated input and output fiber to extensive cortical areas [19]. In addition, anterograde and retrograde tracer
Fig. 1. Distribution of significantly WM disruption of aMCI patients compared with healthy controls at a statistical threshold of p < 0.005 and an extent threshold more than 100 mm3 . FA: fractional anisotropy; RA: relative anisotropy; VR: volume ratio; A: right superior temporal gyrus; B: right middle temporal gyrus; C: right precuneus; D: right internal capsule; E: right cingulate gyrus.
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Table 2 Regions of significant disruption in WM in aMCI patients (n = 32) compared with healthy controls (n = 31). Brain region
Peak MNI coordiates x, y, z (mm)
Peak Z score
Cluster size
Fractional anisotropy (FA) L precentral gyrus R precentral gyrus R superior temporal gyrus R middle temporal gyrus R internal capsule L parahippocampal gyrus R cingulate gyrus R precuneus
−37, −8, 50 29, −32, 58 45, −62, 20 42, −72, 9 18, −7, 17 −17, −33, −13 7, −5, 41 19, −49, 34
4.11 3.79 4.02 4.57 4.44 4.31 4.00 4.23
187 119 115 265 1400 122 429 376
Relative anisotropy (RA) R frontal lobe//sub-gyral L precentral gyrus R precentral gyrus R temporal lobe//sub-gyral R superior temporal gyrus R middle temporal gyrus R internal capsule R cingulate gyrus R precuneus
15, 24, 44 −37, −8, 49 29, −31, 57 42, −3, −17 45, −62, 20 42, −72, 9 18, −7, 17 7, −5, 41 19, −49, 34
4.05 4.01 3.76 3.58 4.02 4.53 4.29 4.06 4.14
109 172 125 128 109 260 1230 334 375
Volume ratio (VR) R frontal lobe//sub-gyral L superior frontal gyrus R superior frontal gyrus R temporal lobe//sub-gyral R superior temporal gyrus R middle temporal gyrus R internal capsule R cingulate gyrus R parietal lobe//sub-gyral L precuneus R precuneus
33, −10, 44 −19, 28, 48 12, 18, 55 42, −3, −17 45, −62, 20 42, −72, 9 19, −7, 17 8, −5, 42 24, −52, 35 −17, −52, 33 19, −49, 34
4.01 3.21 3.77 3.68 3.63 4.14 4.36 4.11 3.76 3.41 3.99
137 125 113 208 103 221 1361 317 157 109 169
Note: The threshold was set at p < 0.005 (cluster extent = 100 mm3 ); R: right; L: left; BA: Brodmann’s area; MNI: Montreal Neurological Institute; Cluster size is in mm3 .
studies have demonstrated that there are large cortico-cortical direct reciprocal WM connections between the frontal cortex, temporal cortex and the medial temporal lobe [1,32]. The precuneus also has reciprocal cortico-cortical connections with the adjacent areas of the posterior cingulate and retrosplenial cortices [5]. The cingulum bundle is adjacent to the parahippocampal cortex and provides connections to prefrontal cortical areas [20]. These connections are known as the frontolimbic network [32]. Emerging evidence has indicated that prefronto-temporal pathways may subserve memory processes [28,30]. Therefore, cortico-cortical disconnection due to disruptions in WM integrity in pathways involved in subserving these circuits may be the structural basis for cognitive impairment in aMCI patients, which has been argued to partly explain AD symptomatology [7]. The disruption of WM tracts, independent to GM atrophy, was clearly seen in aMCI neuropathologic changes in this study. However, the functional details are not eldicated by this current work and will require further investigation. The study had several limitations. Firstly, it was not longitudinal study, so although data is suggestive, it remains to be seen whether the present findings here in aMCI are in anyway related to a progression to AD. Further studies are needed to follow-up such patients and examine whether disturbed white matter can act as a neuroimaging marker for AD-related illness progression. Secondly, although aMCI-single domain and aMCI-multiple domain are supported conceptually by the finding that these individuals were associated with the high risk of AD [11,13], the details of these two subtypes need to be explored in the future neuroimaging study through the large sample size. Thirdly, although voxel-based analysis can automatically reveal abnormalities of whole brain, DTI parameters are decreased in the peripheral parts of fiber tracts due to partial volume effects, and further improvement in analy-
sis methods is required in this field. Finally, this study inherited the normal problems of achieving statistical robustness in wholebrain morphometry studies. Therefore, the findings still need to be confirmed by future work. In spite of these limitations, the present investigative approach did provide an impetus to explore the vulnerability of AD patients to WM pathology independent of GM atrophy in relation to AD. In summary, the present study contributed useful information to our understanding of WM pathology in aMCI patients. The findings supported previous evidence of independent WM changes which suggested multiple patterns of degeneration acting in concert in the pathological process of AD-related disease. This structural neuroimaging approach could enhance our understanding of the neurobiological mechanisms and their appearance in neuroimaging about aMCI patients.
Acknowledgments This research was partly supported by National Basic Research Program of China (973 Program) (No. 2007CB512308), National HiTech Research and Development Program of China (863 Program) (No. 2007AA0200Z435), National Natural Science Foundation of China (Nos. 30770779; 30825014), and the Scientific Research of Foundation of Graduate of School of Southeast University (YBJJ0824).
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Abnormal white matter independent of hippocampal atrophy in amnestic type mild cognitive impairment Feng Bai a,b , Zhijun Zhang a,b,∗ , David R. Watson c , Hui Yu a,b , Yongmei Shi a,b , Yonggui Yuan a,d a
School of Clinical Medicine, Southeast University, Nanjing 210009, China The Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China Psychiatry, School of Medicine and Dentistry, Queen’s University Belfast, BT9 7BL, Belfast, UK d Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China b c
a r t i c l e
i n f o
Article history: Received 2 April 2009 Received in revised form 1 July 2009 Accepted 5 July 2009 Keywords: Amnestic type mild cognitive impairment Diffusion tensor imaging Hippocampus White matter Gray matter
a b s t r a c t Hippocampal atrophy is the key marker in the pathogenesis of Alzheimer’s disease (AD), which is associated with white matter (WM) disruption. This type of WM disruption could partly explain AD-related pathology. However, relatively little attention has been directed toward WM disruption which may be independent of these fundamental gray matter (GM) changes in amnestic mild cognitive impairment (aMCI) which is associated with high risk of AD. To evaluate the differences of WM integrity between aMCI patients (N = 32) and healthy controls (N = 31), whole-brain voxel-based methods were applied to diffusion tensor imaging. To explore the possible independence of WM changes from GM loss, an index of hippocampal atrophy was used to partial out GM effects. aMCI patients showed WM disruption in frontal lobe, temporal lobe, internal capsule, cingulate gyrus and precuneus. The findings supported the evidence of independent patterns of degeneration in WM tracts which may co-act in the WM pathological process of aMCI patients. As aMCI is a putatively prodromal syndrome to AD, these data may assist with a better understanding of WM pathological change associated with the development of AD. Crown Copyright © 2009 Published by Elsevier Ireland Ltd. All rights reserved.
Mild cognitive impairment (MCI) refers to a transitional stage between normal aging and dementia [23]. This can be divided into subgroups of amnestic and non-amnestic type according to memory impairment, and both subtypes can further be classified as single or multiple domains basing on the number of cognitive domains affected [11,15,24,35]. The outcome of nonamnestic MCI is often frontotemporal or Lewy body dementia [11,37], whereas amnestic subtype of MCI (aMCI) is believed to represent a high risk of Alzheimer’s disease (AD) [11,13]. Histological research has demonstrated that the disruption of white matter (WM) microstructure [4,29] which has been described as the underlying pathology of the progressive cortical disconnection syndrome in AD [7]. In addition, a recently developed magnetic resonance imaging modality, diffusion tensor imaging (DTI), has shown extensive alteration in WM integrity of frontal, temporal, and parietal lobes in both MCI [18,27,31,38] and AD patients [14,21]. Therefore, the integrity of WM is believed to be an important neural substrate of AD-related brain structural changes.
∗ Corresponding author at: Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China. Tel.: +86 25 83272023; fax: +86 25 83272023. E-mail address: [email protected] (Z. Zhang).
The hippocampus is one of the first areas to demonstrate gray matter (GM) pathological changes as AD develops, and at the endstage of the disease these regions are also the ones most severely affected [3]. Previous studies have suggested that reduction in hippocampal volume represents the most important structural hallmark for conversion from MCI to AD [2,6,22]. Importantly, previous studies have also shown that hippocampal GM changes are associated with altered WM. For example in AD patients, hippocampal atrophy is concurrent with WM disruption of posterior cingulate bundle [10,34], and frontal and parieto-occipital lobes [8]. In addition, Xie and colleagues in a DTI study of mild AD patients reported that the disruption in WM of medial temporal lobe (hippocampus, parahippocampal gyrus and amygdale) was closely related to the observed cortical GM volumetric reductions, and Walleriantype degeneration was considered to be the possible pathological mechanism for the WM changes noted [36]. However, they also concluded that there was evidence that WM showed a different pattern of degeneration and suggested that partial WM degeneration may be an independent factor in AD progression [36]. Others have also suggested that WM disruption was somewhat independent of hippocampal atrophy in AD patients [26]. It is still unknown what if any role these WM changes play in conversion from aMCI to AD. Therefore, it may be prudent to try and explore WM integrity in circumstances where effects of hippocampal atrophy have been partially out, as it may contribute to our understanding of the
0304-3940/$ – see front matter. Crown Copyright © 2009 Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.neulet.2009.07.009
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exact contribution WM pathology makes in sufferers of AD-related disease. The present study attempted to investigate this issue, using the DTI technique, examining microstructural alterations in WM by measuring the directionality of molecular diffusion of aMCI patients. Some scalar indices such as fractional anisotropy (FA), relative anisotropy (RA), volume ratio (VR), are quite sensitive to tissue inhomogeneity from crossing fibers and partial volume, and have been proposed to characterize the diffusion anisotropic extent and indicate the preferred index of decreasing WM health. Based on the important role damage to the hippocampus is believed to play in AD-related WM disruption, it is reasonable to try remove the effect of this damage, in an attempt to clarify other independent WM disturbances contributing to any observed changes in aMCI patients. The present study recruited 63 elderly individuals (all Chinese Han) including 32 aMCI subjects and 31 healthy controls (all right handed), through normal community health screening and newspaper advertisement. Presence of aMCI (including aMCIsingle domain and aMCI-multiple domain) was made essentially following Petersen’s [23] and others’ [35] recommendations: (1) subjective memory impairment corroborated by subject and an informant; (2) objective memory performance documented by delayed recall of auditory verbal learning test; (3) Clinical Dementia Rating (CDR) of 0.5, with at least a 0.5 in the memory domain; (4) normal general cognitive functioning evaluated by the Mini Mental State Exam (MMSE) score greater than or equal to 24; (5) no or minimal impairment in 14 daily living activities (as determined by clinician interview with the subject and informant); (6) absence of dementia, not sufficient to meet National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association criteria for AD. Participants were excluded from the study if they had a past history of known stroke (modified Hachinski score > 4), alcoholism, head injury, Parkinson’s disease, epilepsy, major depression (excluded by Self-rating Depression Scale) or other neurological or psychiatric illness (excluded by clinical assessment and case history), major medical illness (e.g. cancer, anaemia, thyroid dysfunction), severe visual or hearing loss. Controls were required to have a CDR of 0, an MMSE score ≥26, delayed recall score >4 for those with 8 or more years of education. The study was approved by the Research Ethics Committee of Affiliated ZhongDa Hospital, Southeast University and written informed consent was obtained from all participants. The subjects were scanned using a General Electric 1.5 T scanner (General Electric Medical Systems, USA) with a homogeneous birdcage head coil. High-resolution T1-weighted axial images covering the whole brain were acquired using a 3D spoiled gradient echo (SPGR) sequence as shown in the follow: repetition time (TR) = 9.9 ms; echo time (TE) = 2.1 ms; flip angle = 15◦ ; acquisition matrix = 256 × 192; field of view (FOV) = 240 mm × 240 mm; thickness = 2.0 mm; gap = 0 mm; number of excitations (NEX) = 1.0. Diffusion weighted imaging was acquired with single shot echo planar imaging sequence in alignment with the anterior–posterior commissure plane. The diffusion sensitizing gradients were applied along 25 non-collinear directions (b = 1000 s/mm2 ), together with an acquisition without diffusion weighting (b = 0). Thirty contiguous axial slices were acquired with a slice thickness of 4 mm and no gap. The acquisition parameters were as follows: TR = 10 000 ms; TE = 81.2 ms; flip angle = 90◦ ; acquisition matrix = 128 × 128; FOV = 240 mm × 240 mm; NEX = 2.0. Structural data analysis was performed using Statistical Parametric Mapping software (SPM5, http://www.fil.ion. ucl.ac.uk/spm). First, the images were normalized to the Montreal Neurological Institute (MNI) template using an affine and nonlinear spatial normalization, and re-sampled to a voxel size of 1 mm × 1 mm × 1 mm. Second, the normalized images were
segmented into gray matter, white matter and cerebrospinal fluid segments according to MNI prior probability maps. Then, Jacobian modulation was applied to the segmented gray matter image [12], which can be incorporated to compensate for the effect of spatial normalization. Finally, the extracted gray matter set was smoothed with 8-mm full width at half maximum Gaussian filter to reduce effects of individual variation in gyral anatomy and to increase the signal-to-noise ratio. Following the described process, region of interest (hippocampus, left and right separately) was isolated using automated anatomical labeling [33] implemented through wfu PickAtlas software [16,17]. Then, the hippocampal regions were interpolated to the same dimension, size and origin with individual image. Finally, a mean volume index of all voxels of hippocampal region (left and right) was computed for each subject. These analyses were performed using a semi-automated imaging analysis program developed at Institute of Automation, Chinese Academy of Sciences (Dr. W.L. Zhu). DTI data was calculated off-line with DTI studio software, Version 2.40 (Johns Hopkins University, Baltimore, MD). Firstly, the maps of FA, RA and VR were created for individual images. Secondly, to facilitate voxel by voxel comparisons between groups, the b = 0 images of all subjects were normalized to the standard T2 template of Statistical Parametric Mapping software (SPM2, www.fil.ion.ucl.ac.uk/spm) using an affine and nonlinear spatial normalization algorithm. Then the FA, RA and VR maps were normalized by applying the normalization parameters determined from the normalization of the b = 0 images. This normalization process re-sampled the volumes into a 1 mm × 1 mm × 1 mm voxel size. Finally, to remove possible effects of hippocampal atrophy on the results, mean volume index of hippocampus, obtained from the structural scan hippocampus data, was introduced as a covariate into random effects, voxel by voxel between-group comparisons of FA, RA and VR were investigated at a statistical threshold of p < 0.005 and an extent threshold more than 100 mm3 using the two-sample t-test. Nonparametric Mann–Whitney U-tests (MWU) were used for group comparisons of demographic, neuropsychological performance and hippocampal volume (statistical significance was set at p < 0.05). These statistical analyses were performed using SPSS 11.5 software (SPSS, Inc., Chicago, IL, USA). Demographic and neuropsychological scores were shown in Table 1. Compared with healthy controls, aMCI patients had poor performance in the Auditory Verbal Learning Test, the Trail Making Test-A and B, Symbol Digit Modality Test and Digit Span Test. Other test battery indices showed no significant differences between groups. The volume index extracted from the automated segmentation of hippocampus for the groups show that hippocampal volumes of aMCI patients were 6.3% smaller on the left side (p < 0.037), and 7.2% smaller on the right side (p < 0.013) than healthy controls. Compared with the healthy controls, aMCI patients also displayed significant and extensive disruption of WM in frontal lobe, sub-lobar/extra nuclear, temporal lobe and parietal lobe (see Table 2 and Fig. 1). Importantly, WM disruption of the right internal capsule, right cingulate gyrus, right superior temporal gyrus, right middle temporal gyrus and right precuneus were consistently observed in FA, RA and VR maps in the aMCI patients (Fig. 1). The present study characterized in vivo changes in WM integrity of aMCI patients using whole-brain diffusion tensor imaging. Importantly, even when the primary brain changes reflected by hippocampal atrophy which has been are corrected, aMCI patients showed independent WM disruption in frontal lobe, temporal lobe, internal capsule, cingulate gyrus and precuneus. These findings may assist with a better understanding the essential differences between GM and WM pathological change associated with the development of AD. It provided further evidence of independent
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Table 1 Demographic and neuropsychological data between groups. Items
aMCI subjects (n = 32)
Healthy controls (n = 31)
Range
Mean ± SD
Range
Age Education Gender Clinical Dementia Rating (CDR) Mini Mental State Exam (MMSE) Auditory verbal learning test-delayed recall Trail making test-A (s) Trail making test-B (s) Symbol digit modalities test Clock drawing test Digit span test
62–83 6–18 16m/16f 0.5 24–30 0–4 36–180 100–400 11–46 2–10 8–16
71.4 ± 13.6 ± – – 26.8 ± 2.5 ± 101.9 ± 212.9 ± 24.5 ± 8.1 ± 12.0 ±
63–80 6–18 16m/15f 0 26–30 4–12 30–148 64–270 11–60 5–10 7–17
5.0 3.3
1.5 1.3 36.5 75.5 9.6 1.8 2.0
p (MWU)
Mean ± SD 70.4 ± 14.4 ± – – 28.3 ± 7.8 ± 70.7 ± 137.6 ± 34.4 ± 8.9 ± 13.0 ±
5.1 3.1
1.3 1.9 27.9 46.1 10.8 1.1 2.2
NS NS NS – < 0.001 < 0.001 <0.001 <0.001 0.001 NS 0.047
Values are mean (SD); m: male; f: female; MWU: Mann–Whitney U-test.
GM and WM disturbances in aMCI patients prior to conversion to AD. In previous AD-related studies, WM disruption has been noted to occur in concert with adjacent [10,25,34,36] and distant GM atrophy [8]. However, recently both Xie et al. [36] and Salat et al. [26] reported that WM pathology may be somewhat independent of hippocampal volume changes in AD patients. The present study has replicated this finding in aMCI patients, suggesting that WM changes independent of developing GM deficits were part of the early stage of AD neuropathology. This WM disruption may reflect different underlying neuropathology. For example, Englund [9] reported WM rarefaction might be associated with axonal damage and gliosis, and this type of change could be diffuse. Budde et al. [4] found WM changes were independent of regional cortical degeneration and may represent a unique myelin-related pathology. Sjobeck et al. [29] have supported the idea of Wallerian-type degeneration in AD progression, and WM impaired regions appear close to cortical areas with the pathological burden. Importantly, Wallerian-type degeneration is also considered to be a possible
pathological mechanism of WM changes in the development of AD [36]. Although AD-related disease is generally considered to affect primarily GM regions (especially hippocampus), WM disruption was found independent of hippocampal GM changes in the present study, supporting that WM disruption might play a role in the neuropathology and the development of AD. In this study, the aMCI patients were observed to have cognitive impairment mainly in episodic memory (auditory verbal learning test-delayed recall), sensorimotor function (trail making test-A, B) and attention function (symbol digit modalities test). The processes involved in all forms of higher cognitive function, which are not isolated to specific brain regions but instead result from highly interconnected neural circuits encompassing often remote brain regions. WM fiber tracts play an important role in connecting different cortical regions through direct and indirect fibers communication. For example, the internal capsule is the main structure of the fasciculi of the corticothalamic and thalamocortical connections and has complicated input and output fiber to extensive cortical areas [19]. In addition, anterograde and retrograde tracer
Fig. 1. Distribution of significantly WM disruption of aMCI patients compared with healthy controls at a statistical threshold of p < 0.005 and an extent threshold more than 100 mm3 . FA: fractional anisotropy; RA: relative anisotropy; VR: volume ratio; A: right superior temporal gyrus; B: right middle temporal gyrus; C: right precuneus; D: right internal capsule; E: right cingulate gyrus.
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Table 2 Regions of significant disruption in WM in aMCI patients (n = 32) compared with healthy controls (n = 31). Brain region
Peak MNI coordiates x, y, z (mm)
Peak Z score
Cluster size
Fractional anisotropy (FA) L precentral gyrus R precentral gyrus R superior temporal gyrus R middle temporal gyrus R internal capsule L parahippocampal gyrus R cingulate gyrus R precuneus
−37, −8, 50 29, −32, 58 45, −62, 20 42, −72, 9 18, −7, 17 −17, −33, −13 7, −5, 41 19, −49, 34
4.11 3.79 4.02 4.57 4.44 4.31 4.00 4.23
187 119 115 265 1400 122 429 376
Relative anisotropy (RA) R frontal lobe//sub-gyral L precentral gyrus R precentral gyrus R temporal lobe//sub-gyral R superior temporal gyrus R middle temporal gyrus R internal capsule R cingulate gyrus R precuneus
15, 24, 44 −37, −8, 49 29, −31, 57 42, −3, −17 45, −62, 20 42, −72, 9 18, −7, 17 7, −5, 41 19, −49, 34
4.05 4.01 3.76 3.58 4.02 4.53 4.29 4.06 4.14
109 172 125 128 109 260 1230 334 375
Volume ratio (VR) R frontal lobe//sub-gyral L superior frontal gyrus R superior frontal gyrus R temporal lobe//sub-gyral R superior temporal gyrus R middle temporal gyrus R internal capsule R cingulate gyrus R parietal lobe//sub-gyral L precuneus R precuneus
33, −10, 44 −19, 28, 48 12, 18, 55 42, −3, −17 45, −62, 20 42, −72, 9 19, −7, 17 8, −5, 42 24, −52, 35 −17, −52, 33 19, −49, 34
4.01 3.21 3.77 3.68 3.63 4.14 4.36 4.11 3.76 3.41 3.99
137 125 113 208 103 221 1361 317 157 109 169
Note: The threshold was set at p < 0.005 (cluster extent = 100 mm3 ); R: right; L: left; BA: Brodmann’s area; MNI: Montreal Neurological Institute; Cluster size is in mm3 .
studies have demonstrated that there are large cortico-cortical direct reciprocal WM connections between the frontal cortex, temporal cortex and the medial temporal lobe [1,32]. The precuneus also has reciprocal cortico-cortical connections with the adjacent areas of the posterior cingulate and retrosplenial cortices [5]. The cingulum bundle is adjacent to the parahippocampal cortex and provides connections to prefrontal cortical areas [20]. These connections are known as the frontolimbic network [32]. Emerging evidence has indicated that prefronto-temporal pathways may subserve memory processes [28,30]. Therefore, cortico-cortical disconnection due to disruptions in WM integrity in pathways involved in subserving these circuits may be the structural basis for cognitive impairment in aMCI patients, which has been argued to partly explain AD symptomatology [7]. The disruption of WM tracts, independent to GM atrophy, was clearly seen in aMCI neuropathologic changes in this study. However, the functional details are not eldicated by this current work and will require further investigation. The study had several limitations. Firstly, it was not longitudinal study, so although data is suggestive, it remains to be seen whether the present findings here in aMCI are in anyway related to a progression to AD. Further studies are needed to follow-up such patients and examine whether disturbed white matter can act as a neuroimaging marker for AD-related illness progression. Secondly, although aMCI-single domain and aMCI-multiple domain are supported conceptually by the finding that these individuals were associated with the high risk of AD [11,13], the details of these two subtypes need to be explored in the future neuroimaging study through the large sample size. Thirdly, although voxel-based analysis can automatically reveal abnormalities of whole brain, DTI parameters are decreased in the peripheral parts of fiber tracts due to partial volume effects, and further improvement in analy-
sis methods is required in this field. Finally, this study inherited the normal problems of achieving statistical robustness in wholebrain morphometry studies. Therefore, the findings still need to be confirmed by future work. In spite of these limitations, the present investigative approach did provide an impetus to explore the vulnerability of AD patients to WM pathology independent of GM atrophy in relation to AD. In summary, the present study contributed useful information to our understanding of WM pathology in aMCI patients. The findings supported previous evidence of independent WM changes which suggested multiple patterns of degeneration acting in concert in the pathological process of AD-related disease. This structural neuroimaging approach could enhance our understanding of the neurobiological mechanisms and their appearance in neuroimaging about aMCI patients.
Acknowledgments This research was partly supported by National Basic Research Program of China (973 Program) (No. 2007CB512308), National HiTech Research and Development Program of China (863 Program) (No. 2007AA0200Z435), National Natural Science Foundation of China (Nos. 30770779; 30825014), and the Scientific Research of Foundation of Graduate of School of Southeast University (YBJJ0824).
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