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Exploring structural and functional brain changes in mild cognitive impairment: a whole brain ALE meta-analysis for multimodal MRI Lihua Gu, and Zhi-jun Zhang ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.9b00045 • Publication Date (Web): 26 Feb 2019 Downloaded from http://pubs.acs.org on February 27, 2019
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Exploring structural and functional brain changes in mild cognitive impairment:
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a whole brain ALE meta-analysis for multimodal MRI
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Lihua Gua, Zhijun Zhanga, *
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a
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Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China;
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*Corresponding
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Zhijun Zhang, PhD, MD
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Department of Neurology, Affiliated ZhongDa Hospital, Medical School, Southeast University, No. 87 Dingjiaqiao Road, Nanjing, Jiangsu, China, 210009.
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E-mail:
[email protected];
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Tel: 0086-25-83262241;
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Fax: 0086-25-83285132.
author:
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Abstract:
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Background: Unravelling novel biomarkers for mild cognitive impairment (MCI) was
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highlighted in the prevention and modification of Alzheimer’s disease (AD).
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Inconsistent results for comparison between MCI patients and healthy controls (HC)
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were obtained from previous neuroimaging studies.
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Methods: An activation likelihood estimation (ALE) meta-analysis was made for
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multimodal neuroimaging in MCI. After initial research and step-by-step exclusions
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procedures, n = 101 articles (MCI: n = 2681, HC: n = 2941, respectively) were included
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in the study.
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Results: It detected MCI related gray matter atrophy in the bilateral medial temporal
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lobe and white matter abnormity in the left posterior cingulate, parahippocampal gyrus,
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thalamus, caudate and bilateral precuneus. It revealed MCI-related decreased resting-
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state activity in the left superior temporal gyrus, right posterior cingulate/precuneus,
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uncus and hyperactivation in the inferior parietal lobule, superior parietal lobule
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compared to HC. Task-related functional neuroimaging studies indicated MCI-related
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hypoactivation in the left inferior parietal lobule, right posterior cingulate, the bilateral
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precuneus and hyperactivation in the left middle frontal gyrus, superior parietal lobule,
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insula, superior temporal gyrus and right inferior frontal gyrus. Conclusions: Via this
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ALE meta-analysis, we obtained these key regions suffering from different kinds of 1
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deficits in MCI. These regional abnormalities in MRI studies might serve as biomarkers
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for early diagnosis of MCI.
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Keywords: activation likelihood estimation, meta-analysis, mild cognitive impairment,
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multimodal magnetic resonance imaging, Alzheimer’s disease; biomarker.
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Introduction
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Alzheimer’s disease (AD) is the most common dementia form in the world 1, 2. AD
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is a kind of neurodegenerative disease with a progressive age-related decrease in
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cognitive functions and eventually disability due to sensory, motor impairments and
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psychiatric conditions 3. Two pathological deficits, neuritic plaques formed by amyloid-
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β (Aβ) deposition and neurofibrillary tangles derived from hyperphosphorylated tau
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protein, are critical hallmarks of this neural degenerative disease. The World Health
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Organization (WHO) reported that by 2050, 1 of 85 persons in global world would be
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living with AD 4. Therefore, early and accurate detection of AD is essential for
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lightening the economic load of worldwide healthcare system. Mild cognitive
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impairment (MCI) is considered as the prodromal period of AD 5. Epidemiological
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investigations verified that MCI patients develop into dementia at an annual rate of 10-
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15% 6, whereas normal elderly have a conversion rate of 1-2% per year 7. MCI patients
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fulfilled the following criteria
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impairment in daily activities; absence of dementia, symptoms that were insufficient to
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meet the criteria of National Institute of Neurological and Communicative Disorders
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and Stroke or the AD and Related Disorders Association (NINCDS-ADRDA) for AD.
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MCI diagnosis in the early stage is paramountly crucial for slowing down its
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progression to AD. However, because of the mild symptom of cognitive impairment,
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MCI is difficult to be identified from normal elderly. Unravelling novel biomarkers for
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MCI is highlighted in the prevention and modification of AD.
5, 8:
memory complaints and deficits; no or minimal
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Magnetic resonance imaging (MRI) is a non-invasive tool with high spatial
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resolutions. Over the last few decades, various imaging modalities were implemented
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in AD and MCI studies. Different modalities provided different information for disease
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diagnosis: structural MRI was applied to observe brain volume 9; diffusion tensor
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imaging (DTI) was used to explore microstructural features of white matter
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functional MRI (fMRI) focused on neural activation reflected by hemodynamic
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response 11. Most investigations adopted a separate modality to diagnose AD or MCI.
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More efforts should be made to concentrate on integration of different imaging
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modalities to enhance accuracy of AD or MCI diagnosis. On the other hand,
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inconsistent results for comparison between MCI patients and healthy controls (HC)
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were obtained from studies with specific modality especially from fMRI studies, which
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might result from the heterogeneity of included MCI patients and application of
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different analysis methods. Thus, to get the key brain regional difference between HC
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and MCI patients, we focused on summarizing the MRI studies with different 3
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modalities, respectively. In the systematic review and meta-analysis study, anatomic
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likelihood estimation (ALE) method, which is a powerful technique for voxel-based
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neuroimaging studies, was implemented to help search for the key regions of brain
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pathology in MCI.
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In our investigation, the aim was to perform a comprehensive review of
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multimodal MRI studies on MCI patients to give insight to the structural and functional
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brain changes of MCI and explore key regions suffering from deficits in MCI.
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RESULTS AND DISCUSSION
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Search results
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The initial research and step-by-step exclusions procedures were presented in
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Figure 1. In addition, study characteristics and results were summarized in
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supplementary table 1. In the n = 101 articles (MCI: n = 2681, HC: n = 2941,
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respectively), n = 27 studies (MCI: n = 801, HC: n = 821, respectively) made whole-
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brain voxel-based morphometry (VBM) analysis of MCI-related gray matter (GM)
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abnormalities, n = 6 investigations (MCI: n = 260, HC: n = 417, respectively) reported
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disturbed white matter (WM) integrity in MCI patients, n = 28 studies (n = 24
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blood oxygen level-dependent (BOLD) fMRI studies (MCI: n = 671, HC: n = 671,
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respectively), n = 4 arterial spin labelling (ASL) fMRI studies (MCI: n = 70, HC: n =
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81, respectively)) focused on resting-state activation abnormalities in MCI patients, n
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= 53 task-related fMRI investigations (MCI: n = 879, HC: n = 951, respectively)
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explored brain activation abnormalities during specific cognitive processing in MCI
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patients.
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Results of contingency analysis and Spearman correlation The supplementary table 2 showed the contingency coefficients obtained from contingency analysis between MRI types and abnormality in each region. The present study indicated no significant contingency coefficients for all 49 affected regions, which revealed that the presentation of affected region would not be influenced by MRI types. In addition, Spearman correlation indicated a positive correlation (r = 0.471, p = 0.001) between two variables (Number of anatomical studies in which the area was significantly affected and number of fMRI studies in which the area was significantly affected). The result showed that anatomical and fMRI studies might show concordant results for affected regions. Supplementary figure 1 showed boxplots for age, educational levels or Mini-mental State Examination (MMSE) for both HC and aMCI groups in different types of MRI studies, respectively. The cohorts did not differ in age, educational levels or MMSE.
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Meta-analysis results These studies reflected different aspects (brain atrophy, microstructural abnormity, neuronal network dysfunctions) of MCI-related pathological changes.
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Abnormal gray and white matter in MCI. ALE results indicated that MCI
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patients showed significant GM reductions in the bilateral hippocampus and amygdala,
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the left parahippocampal gyrus and the left uncus, compared to HC (see Figure 2. a and
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Supplementary table 3). Regarding DTI studies, our study revealed that MCI patients
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showed reduced fractional anisotropy (FA) values in the posterior cingulate gyrus,
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parahippocampal gyrus, thalamus, caudate in the left hemisphere and the bilateral
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precuneus (see Figure 2. b and Supplementary table 3). Our result corresponds to a
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previous meta-analysis study published in 2012
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volumetric MRI studies
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suffering from neuronal loss in AD patients in postmortem autopsy 14. In addition, these
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regions are long recognized to participate in memory, particularly in encoding and
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retrieval of memory, which are prominently deficient cognitive domains in MCI
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patients 15. However, AD showed atrophy in both the anterior and posterior parts of the
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hippocampus supported by another ALE study 16. A recent ALE meta-analysis study
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for structural MRI of Parkinson's disease (PD) showed small focal gray matter atrophy
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in the middle occipital gyrus 17, which is quite different from the regions obtained by
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our study in MCI. Regarding DTI, it revealed lower FA in the posterior cingulate gyrus,
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parahippocampal gyrus, thalamus, caudate in the left hemisphere and the bilateral
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precuneus in MCI patients relative to HC, which is consistent with a meta-analysis for
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ROI-based DTI studies for MCI published in 2011
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diffusivity perpendicular (DR), mean diffusivity (MD) in the parahippocampal gyrus,
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posterior cingulate gyrus, precuneus, entorhinal, retrosplenial, supramarginal and FA
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in the parahippocampal gyrus WM is associated with cognitive decline
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study detected that MCI presents more changes in FA and DR over time (from baseline
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to follow-up after 2.5 years) than HC and this WM abnormity is more prominent in
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MCI patients with more neuronal degeneration evaluated by cerebrospinal fluid (CSF)
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total tau 20. An ALE study for DTI studies in PD showed FA reductions in the cingulate
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bundle near the orbital and anterior cingulate gyri 17, which is quite different from the
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regions obtained by our study in MCI. Our results suggest that FDG-PET reliably
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identifies consistent functional brain abnormalities in PD, whereas structural MRI and
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DTI show only focal alterations and rather inconsistent results. Above investigations
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provided evidence for the idea that WM abnormity could be an index to diagnose MCI
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and other ROI-based
Medial temporal lobe is confirmed as a critical structure
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and predict the disease progression.
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Abnormal resting-state activation in MCI. MCI patients showed decreased
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resting-state activation than HC in the left superior temporal gyrus, the right
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posterior cingulate gyrus/precuneus, the right uncus (see Figure 2. c and Supplementary
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table 3). Compared to HC, MCI patients presented an increase in resting-state activation
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in the inferior parietal lobule and superior parietal lobule in the left hemisphere (see
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Figure 2. c and Supplementary table 3). Our result was accordant to a previous meta-
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analysis for resting-state fMRI (rs-fMRI) in aMCI patients
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discrepant from this meta-analysis study that MCI patients showed decreased brain
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activation in the left superior temporal gyrus and the right uncus, which might attribute
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to the heterogeneity of included disease group. The previous meta-analysis only
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focused on the amnestic form of MCI. The posterior cingulate gyrus/precuneus and the
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superior temporal gyrus are considered to play crucial roles in default mode network
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(DMN)
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memory encoding and retrieval
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deposition at early stage of AD and appeared central to AD pathology
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indicated that MCI patients showed resting-state hyperactivation in the left inferior
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parietal lobule, which corresponds to a recent ALE study 26. Another region, the left
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superior parietal lobule is never reported by any ALE study for rs-fMRI study in MCI
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patients. Up to now, we could not verify whether the hyperactivation in the two regions
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is associated with a compensatory response for function decrease of other regions or
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results from other pathological changes, such as an early aberrant excitatory response
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to Aβ. More studies are essential to interpret the result.
22, 23.
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whereas it was quite
The medial temporal lobe (Uncus) and DMN are closely involved in 24, 25.
And these areas are vulnerable to amyloid 24.
Our study
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Abnormal task-related activation in MCI. In the overall analysis on task-related
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studies on MCI patients, it indicated decreased brain activation in the left inferior
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parietal lobule, the right posterior cingulate gyrus and the bilateral precuneus in MCI
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patients than HC (see Figure 2. d and Supplementary table 4). MCI patients showed
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hyperactivation relative to HC mainly in the middle frontal gyrus, superior parietal
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lobule, insula, superior temporal gyrus in the left hemisphere as well as the right inferior
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frontal gyrus (see Figure 2. d and Supplementary table 4). In our study, brain activation
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comparison between MCI patients and HC was analysed during different cognitive
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processing tasks, respectively.
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During memory encoding, it detected MCI-related brain hypoactivation relative to
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HC in the posterior cingulate gyrus, parahippocampal gyrus in the right hemisphere and
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the bilateral precuneus (see Figure 3. a and Supplementary table 4). In addition, MCI 6
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patients showed increased brain activation in the left insula and the right middle frontal
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gyrus, compared to HC (see Figure 3. a and Supplementary table 4). ALE analysis of
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memory retrieval task indicated that MCI-related hypoactivation mainly located in the
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bilateral middle frontal gyrus, the left precuneus and the right middle temporal gyrus
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(see Figure 3. b and Supplementary table 4). Additionally, MCI-related hyperactivation
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relative to HC was observed in the right superior frontal gyrus during memory retrieval
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(see Figure 3. b and Supplementary table 4). Regarding working memory (WM)
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processing, MCI patients showed decreased activation in the lingual gyrus,
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posterior cingulate gyrus, precuneus and middle frontal gyrus in the right hemisphere
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(see Figure 3. c and Supplementary table 4). MCI-related hypoactivation relative to HC
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located in the left insula and left superior temporal gyrus (see Figure 3. c and
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Supplementary table 4). During executive processing, MCI patients presented
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hypoactivation in the left superior temporal gyrus, compared to HC (see Supplementary
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table 4). No significant MCI-related hyperactivation relative to HC was found during
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executive processing. In addition, it showed no significant MCI-related hypoactivation
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during visuospatial processing, compared to HC. Moreover, because of the limited
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number of relevant investigations, this study did not analyse MCI-related
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hypoactivation compared to HC in visuospatial function related tasks. Identically, we
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did not explore the brain activation comparison during semantic processing, divided
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attention tasks, simple-motor tasks, theory of mind related task and lexical decision
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tasks.
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Regarding ALE studies for task-related fMRI investigations, discussion was made
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for specific tasks. During memory encoding, our ALE result corresponds to a recent
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study that hypoactivation was shown in the right parahippocampal gyrus in MCI
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patients relative to HC 27. Our investigation also reported hypoactivation in the right
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posterior cingulate gyrus and bilaterally in the precuneus in MCI patients during this
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task. These regions play a crucial role in DMN, which is strongly associated with
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memory encoding and retrieval 24, 25. And the result was in-line with the brain atrophy
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in these regions 28. During memory retrieval, consistent with our result, another meta-
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analysis study also indicated the hypoactivation in the bilateral middle frontal gyrus
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and the left precuneus 26. Together with the right middle temporal gyrus, these regions
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are important regions associated with episodic memory 29. Hypoactivation in the lingual
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gyrus, posterior cingulate gyrus, precuneus and middle frontal gyrus in the right
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hemisphere was detected in MCI patients relative to HC during WM task. These regions
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are important WM related regions. In addition, multimodal neuroimaging studies, 7
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30,
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including fluorodeoxyglucose positron emission topography (PET)
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imaging
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abnormity in these regions at early stage of AD and MCI patients than HC. Some of
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our results for hyperactivation of regions in MCI relative to HC correspond to another
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ALE studies for MCI in task-related fMRI studies 26. The supplementary materials of
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this article reported the consistent results to our study that hyperactivation in MCI
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relative to HC presented in the right middle frontal gyrus during episodic encoding task
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and in the left superior temporal gyrus, the left insula during WM and executive
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function related tasks. The hyperactivation in these regions for MCI during specific task
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might be compensatory responses to AD pathology 34, 35.
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fMRI
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and β-amyloid imaging
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structural
provided converging evidence for
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Our study reported some limitations. First, heterogeneity between the individual
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studies could not be investigated via ALE meta-analysis. However, the present study
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tried to minimize the heterogeneity via the relatively strict inclusion and exclusion
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criteria. Furthermore, in GingerALE 2.3.6, the ALE algorithm is on the basis of a
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random-effects model which is more conservative than the fixed-effects model and
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incorporates both within-study and between-study variance. Second, in meta-analysis
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for fMRI, different imaging modalities (BOLD or ASL modality) and analysis methods
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might reflect different facets of neural activation abnormity. Thirdly, ALE technique
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could not assess the significance level of contributing results.
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CONCLUSIONS
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The study summarized different aspects of pathological characteristics in MCI
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patients attained from multimodal MRI studies, a technique with high spatial resolution.
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Via this ALE meta-analysis, we obtained key regions suffering from different kinds of
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deficits in MCI. In addition, these regional abnormalities in MRI studies might serve as
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biomarkers for early diagnosis of MCI.
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EXPERIMENTAL SECTION The present study was conducted according to Preferred Reporting Items for
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Systematic Reviews and Meta-Analyses (PRISMA) statement
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checklist is included as supplementary table 5.
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Search strategy
36.
A PRISMA 2009
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PubMed and Web of Science databases were searched for articles published to
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August 2018. Search terms used were: (“mild cognitive impairment” OR “MCI”) AND
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(“neuroimaging” OR “magnetic resonance imaging” OR “MRI”). Searches were 8
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limited to publications in English. Duplicates were removed. A total of 7122 unique
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articles were screened.
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Inclusion and exclusion criteria
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All articles referring to structural MRI, DTI and functional MRI investigations on
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MCI were included in our study. In addition, studies should include both HC and MCI
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patients as participants. Moreover, these investigations must provide information about
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Talairach or Montreal Neurologic Institute (MNI) coordinates for comparisons between
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HC and MCI.
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Articles were eliminated while they were associated with other diseases and
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disorders such as schizophrenia, depression, et al. We dropped secondary processing of
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literature such as reviews and meta-analysis articles. Case studies without group-level
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statistics were excluded. In addition, our study excluded priori region of interest (ROI)
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analyses and seed-based functional connectivity analyses.
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Data collection
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Titles and abstracts were read by two different individuals. A total of 101 articles
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were selected to read full-texts according to the inclusion and exclusion criteria
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displayed above. Following data were recorded from these full-texts: Author,
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publication years, imaging modalities, methods of analysis, participant demographics
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(sample size, age, gender, year of education and MMSE, task for task-related fMRI
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studies, contrasts of included studies, three-dimensional (3D) coordinates and all
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affected regions.
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Contingency analysis and Spearman correlation
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Contingency analysis and Spearman correlation were applied to evaluate whether
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anatomical (structural MRI and DTI) and fMRI studies (rs-fMRI and task-related fMRI)
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give concordant results for all affected regions. On the basis of the previous study 37,
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for each region, every study could be cross classified by MRI types (anatomical or fMRI)
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and according to whether the brain region is affected, producing a 2 × 2 contingency
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table of counts. Via contingency analysis, contingency coefficients were obtained to
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imply the presence of a dependency between MRI types and abnormality in each region
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(i.e., anatomical studies would present abnormality in the left hippocampus more or
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less likely). To stabilize results and ensure all cells had sufficient observations for the
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parametric contingency analysis test, we excluded all regions that were affected in
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fewer than 3% of studies. In addition, we applied Spearman correlation between two
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variables (Number of anatomical studies in which the area was significantly affected
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and number of fMRI studies in which the area was significantly affected) in n = 49 9
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affected regions to further verify our contingency analysis results. Statistical threshold
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was set at a p < 0.05. The boxplots were utilized to evaluate the heterogeneity of age,
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educational levels or MMSE between studies.
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Meta-analysis procedures
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An ALE meta-analysis was performed with Java-based version of GingerALE
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2.3.6 (http://www.brainmap.org/ale). ALE aimed to assess the convergence of
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difference between groups in terms of foci across studies. The
foci data were read as a
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text file and imported into the software. Then, all these Talairach coordinates were
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converted to corresponding MNI coordinates with icbm2tal
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significance was attained with a permutation test (5000 permutations) on randomly
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distributed foci. Full-width-half-maximum (FWHM) was calculated according to
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subjects numbers in each study 40. ALE maps were set a threshold at p < 0.05 using the
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false discovery rate (FDR) with an extent threshold > 200 mm3. Eventually, the maps
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were overlaid onto the MNI 152 template and viewed with Mango software
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(http://rii.uthscsa.edu/mango). ALE studies were performed for structural MRI, DTI,
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rs-fMRI and all task-related fMRI investigations in MCI. In addition, our study focused
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on specific cognitive task-related fMRI studies in MCI, respectively.
38, 39.
Statistical
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AUTHOR INFORMATION
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Corresponding Authors
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* Zhijun Zhang, PhD, MD: Department of Neurology, Affiliated ZhongDa Hospital, Medical School, Southeast University, No. 87 Dingjiaqiao Road, Nanjing, Jiangsu, China, 210009. E-mail:
[email protected]; Tel: 0086-2583262241; Fax: 0086-25-83285132.
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Author Contributions
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Zhijun Zhang contributed to the design and plan of the present study. Zhijun Zhang
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supervised the project. Lihua Gu was in charge of article inclusion and had the major
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responsibility for analysis and manuscript writing.
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Funding
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This study was supported by the National Natural Science Foundation of China
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(No. 81420108012; 81671046), the Key Program for Clinical Medicine and Science
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and Technology: Jiangsu Province Clinical Medical Research Center (No. BL2013025;
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BL2014077).
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Notes
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The authors declare no competing financial interest. 10
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ACKNOWLEDGEMENTS
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We are grateful for the assistance for article inclusion of Lijuan Gao of the
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Department of Neurology, Affiliated ZhongDa Hospital, Medical School, Southeast
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University.
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SUPPORTING INFORMATION: Supplementary material (Supplementary tables 1,
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2, 3, 4 and 5; supplementary figure 1; supplementary references) is available free of
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charge via the internet.
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ABBREVIATIONS USED AD, Alzheimer’s disease; ALE, activation likelihood estimation; Aβ, amyloid-β; ASL, arterial spin labelling; BOLD, blood oxygen level-dependent; CSF, cerebrospinal fluid; DMN, default mode network; DR, diffusivity perpendicular; DTI, diffusion tensor imaging; FDR, false discovery rate; fMRI, functional magnetic resonance imaging; FWHM, full-width-half-maximum; GM, gray matter; HC, healthy controls; MCI, mild cognitive impairment; MD, mean diffusivity; MMSE, Mini-mental State Examination; MNI, Montreal Neurologic Institute; MRI, magnetic resonance imaging; PET, positron emission topography; ROI, region of interest; 3D, three-dimensional; VBM, voxelbased morphometry; WM, white matter.
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References 1. Blennow, K., de Leon, M. J., and Zetterberg, H. (2006) Alzheimer's disease, Lancet (London, England) 368, 387-403. 2. Cummings, J. L. (2004) Alzheimer's disease, The New England journal of medicine 351, 56-67. 3. Reichman, W. E., and Rose, N. S. (2012) History and experience: the direction of Alzheimer's disease, Menopause (New York, N.Y.) 19, 724-734. 4. Brookmeyer, R., Johnson, E., Ziegler-Graham, K., and Arrighi, H. M. (2007) Forecasting the global burden of Alzheimer’s disease, Alzheimers & Dementia 3, 186191. 5. Petersen, R. C., Stevens, J. C., Ganguli, M., Tangalos, E. G., Cummings, J. L., and DeKosky, S. T. (2001) Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology, Neurology 56, 1133-1142. 6. M, G., RC, P., SH, F., RG, T., PS, A., DA, B., NL, F., Jr, J. C., DR, G., R, D., J, K., M, S., R, M., S, G., HT, K., S, J., AN, S., K, S., R, M., CH, v. D., J, M., EY, Z., D, C.-W., LJ, T., and Study, A. s. D. C. (2004) Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials, Jama Neurology 61, 59-66. 7. Bischkopf, J., Busse, A., and Angermeyer, M. C. (2002) Mild cognitive impairment—A review of prevalence, incidence and outcome according to current approaches, Acta Psychiatrica Scandinavica 106, 403-414. 8. Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., and Kokmen, E. (1999) Mild cognitive impairment: clinical characterization and outcome, 11
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Archives of neurology 56, 303-308. 9. Tabatabaei-Jafari, H., Shaw, M. E., and Cherbuin, N. (2015) Cerebral atrophy in mild cognitive impairment: A systematic review with meta-analysis, Alzheimer's & dementia (Amsterdam, Netherlands) 1, 487-504. 10. Zhang, B., Xu, Y., Zhu, B., and Kantarci, K. (2014) The role of diffusion tensor imaging in detecting microstructural changes in prodromal Alzheimer's disease, CNS neuroscience & therapeutics 20, 3-9. 11. Farras-Permanyer, L., Guardia-Olmos, J., and Pero-Cebollero, M. (2015) Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art, Frontiers in psychology 6, 1095. 12. Jing, Y., Pan, P. L., Wei, S., Rui, H., Li, J. P., Ke, C., Gong, Q. Y., Zhong, J. G., Shi, H. C., and Shang, H. F. (2012) Voxelwise meta-analysis of gray matter anomalies in Alzheimer's disease and mild cognitive impairment using anatomic likelihood estimation, Journal of the Neurological Sciences 316, 21-29. 13. Research, C. A. (2009) Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort, Current Alzheimer Research 6, 347-361(315). 14. Braak, H., and Braak, E. (1995) Staging of alzheimer's disease-related neurofibrillary changes, Neurobiology of Aging 16, 271-278. 15. Blennow, K., Leon, M. J. D., and Zetterberg, H. (2006) Alzheimer's disease, Lancet 368, 387-403. 16. Chapleau, M., Aldebert, J., Montembeault, M., and Brambati, S. M. (2016) Atrophy in Alzheimer's Disease and Semantic Dementia: An ALE Meta-Analysis of VoxelBased Morphometry Studies, Journal of Alzheimer's disease : JAD 54, 941-955. 17. Albrecht, F., Ballarini, T., Neumann, J., and Schroeter, M. L. (2018) FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A wholebrain multimodal imaging meta-analysis, NeuroImage. Clinical. 18. Sexton, C. E., Kalu, U. G., Filippini, N., Mackay, C. E., and Ebmeier, K. P. (2011) A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease, Neurobiology of Aging 32, 2322.e2325–2322.e2318. 19. Selnes, P., Aarsland, D., Bjornerud, A., Gjerstad, L., Wallin, A., Hessen, E., Reinvang, I., Grambaite, R., Auning, E., Kjaervik, V. K., Due-Tonnessen, P., Stenset, V., and Fladby, T. (2013) Diffusion tensor imaging surpasses cerebrospinal fluid as predictor of cognitive decline and medial temporal lobe atrophy in subjective cognitive impairment and mild cognitive impairment, Journal of Alzheimer's disease : JAD 33, 723-736. 20. Amlien, I. K., Fjell, A. M., Walhovd, K. B., Selnes, P., Stenset, V., Grambaite, R., Bjornerud, A., Due-Tonnessen, P., Skinningsrud, A., Gjerstad, L., Reinvang, I., and Fladby, T. (2013) Mild cognitive impairment: cerebrospinal fluid tau biomarker pathologic levels and longitudinal changes in white matter integrity, Radiology 266, 295-303. 21. Lau, W. K. W., M-K, L., Lee, T. M. C., and Law, A. C. K. (2016) Resting-state abnormalities in amnestic mild cognitive impairment: a meta-analysis, Translational Psychiatry 6, e790. 22. Petrella, J. R., Sheldon, F. C., Prince, S. E., Calhoun, V. D., and Doraiswamy, P. 12
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M. (2011) Default mode network connectivity in stable vs progressive mild cognitive impairment, Neurology 76, 511-517. 23. Liu, Z., Zhang, Y., Bai, L., Yan, H., Dai, R., Zhong, C., Wang, H., Wei, W., Xue, T., Feng, Y., You, Y., and Tian, J. (2012) Investigation of the effective connectivity of resting state networks in Alzheimer's disease: a functional MRI study combining independent components analysis and multivariate Granger causality analysis, NMR in biomedicine 25, 1311-1320. 24. Sperling, R. A., Dickerson, B. C., Pihlajamaki, M., Vannini, P., LaViolette, P. S., Vitolo, O. V., Hedden, T., Becker, J. A., Rentz, D. M., Selkoe, D. J., and Johnson, K. A. (2010) Functional alterations in memory networks in early Alzheimer's disease, Neuromolecular medicine 12, 27-43. 25. Pihlajamäki, M., Jauhiainen, A. M., and Soininen, H. (2009) Structural and functional MRI in mild cognitive impairment, Current Alzheimer research 6, 179-185. 26. Li, H. J., Hou, X. H., Liu, H. H., Yue, C. L., He, Y., and Zuo, X. N. (2014) Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: A metaanalysis of 75 fMRI studies, Human brain mapping 36, 1217–1232. 27. Browndyke, J. N., Giovanello, K., Petrella, J., Hayden, K., Chiba-Falek, O., Tucker, K. A., Burke, J. R., and Welsh-Bohmer, K. A. (2013) Phenotypic regional functional imaging patterns during memory encoding in mild cognitive impairment and Alzheimer's disease, Alzheimer's & dementia : the journal of the Alzheimer's Association 9, 284-294. 28. Ewers, M., Sperling, R. A., Klunk, W. E., Weiner, M. W., and Hampel, H. (2011) Neuroimaging markers for the prediction and early diagnosis of Alzheimer's disease dementia, Trends in neurosciences 34, 430-442. 29. Rajah, M. N., Languay, R., and Grady, C. L. (2011) Age-related changes in right middle frontal gyrus volume correlate with altered episodic retrieval activity, Journal of Neuroscience the Official Journal of the Society for Neuroscience 31, 17941-17954. 30. MD, S. M. P., Giordani., B., Berent., S., MD, K. A. F. P., Foster, N. L., and Kuhl, D. E. (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease, Annals of Neurology 42, 85–94. 31. Whitwell, J. L., Shiung, M. M., Przybelski, S. A., Weigand, S. D., Knopman, D. S., Boeve, B. F., Petersen, R. C., and Jr, J. C. (2008) MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment, Neurology 70, 512520. 32. Kochan, N. A., Breakspear, M., Slavin, M. J., Valenzuela, M., McCraw, S., Brodaty, H., and Sachdev, P. S. (2010) Functional alterations in brain activation and deactivation in mild cognitive impairment in response to a graded working memory challenge, Dementia and geriatric cognitive disorders 30, 553-568. 33. Kemppainen, N. M., Aalto, S., Wilson, I. A., Någren, K., Helin, S., Brück, A., Oikonen, V., Kailajärvi, M., Scheinin, M., and Viitanen, M. (2007) PET amyloid ligand [11C]PIB uptake is increased in mild cognitive impairment, Neurology 68, 1603-1606. 34. Kircher, T. T., Weis, S., Freymann, K., Erb, M., Jessen, F., Grodd, W., Heun, R., and Leube, D. T. (2007) Hippocampal activation in patients with mild cognitive impairment is necessary for successful memory encoding, Journal of Neurology 13
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Neurosurgery & Psychiatry 78, 812-818. 35. Bokde, A. L., Karmann, M., Born, C., Teipel, S. J., Omerovic, M., Ewers, M., Frodl, T., Meisenzahl, E., Reiser, M., and Möller, H. J. (2014) Altered brain activation during a verbal working memory task in subjects with amnestic mild cognitive impairment, Journal of Alzheimers Disease Jad 10, 103-118. 36. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and Group, T. P. (2009) Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement, Revista Española De Nutrición Humana Y Dietética. 37. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., and Wager, T. D. (2011) Large-scale automated synthesis of human functional neuroimaging data, Nature methods 8, 665-670. 38. Laird, A. R., Robinson, J. L., McMillan, K. M., Tordesillas-Gutierrez, D., Moran, S. T., Gonzales, S. M., Ray, K. L., Franklin, C., Glahn, D. C., Fox, P. T., and Lancaster, J. L. (2010) Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform, NeuroImage 51, 677-683. 39. Lancaster, J. L., Tordesillas-Gutierrez, D., Martinez, M., Salinas, F., Evans, A., Zilles, K., Mazziotta, J. C., and Fox, P. T. (2007) Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template, Human brain mapping 28, 1194-1205. 40. Turkeltaub, P. E., Eickhoff, S. B., Laird, A. R., Fox, M., Wiener, M., and Fox, P. (2012) Minimizing within-experiment and within-group effects in Activation Likelihood Estimation meta-analyses, Human brain mapping 33, 1-13.
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Figure 1. Flow of information through the different phases of a systematic review.
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Figure 2. (a) Gray matter reduction in patients with MCI patients compared with HC
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(in blue). (b) Lower FA in MCI patients relative to HC (in blue). (c) Resting-state
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hypoactivation (in blue) and hyperactivation (in red) in MCI patients relative to HC. (d)
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Hypoactivation (in blue) and hyperactivation (in red) in MCI patients relative to HC in
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task-related studies. Abbreviations: AMYG, amygdala; CAU, caudate; FA, fractional
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anisotropy; HC, healthy controls; HIP, hippocampus; IFG, inferior frontal gyrus; INS,
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insula; IPL, inferior parietal lobule; MCI, mild cognitive impairment; MFG, middle
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frontal gyrus; PHG, parahippocampal; PCG, posterior cingulate; PCUN, precuneus;
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SPL, superior parietal lobule; STG, superior temporal gyrus; THA, thalamus.
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Figure 3. (a) Hypoactivation (in blue) and hyperactivation (in red) in MCI relative to
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HC during memory encoding. (b) Hypoactivation (in blue) and hyperactivation (in red)
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in MCI patients relative to healthy controls during memory retrieval. (c) Hypoactivation
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(in blue) and hyperactivation (in red) in MCI patients relative to healthy controls during
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WM task. Abbreviation: INS, insula; LING, lingual gyrus; MCI, mild cognitive
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impairment; MFG, middle frontal gyrus; MTG, middle temporal gyrus; PHG,
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parahippocampal; PCG, posterior cingulate; PCUN, precuneus; SFG, superior frontal
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gyrus; STG, superior temporal gyrus; WM, working memory.
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Figure 1. Flow of information through the different phases of a systematic review.
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Figure 2. (a) Gray matter reduction in patients with MCI patients compared with HC (in blue). (b) Lower FA in MCI patients relative to HC (in blue). (c) Resting-state hypoactivation (in blue) and hyperactivation (in red) in MCI patients relative to HC. (d) Hypoactivation (in blue) and hyperactivation (in red) in MCI patients relative to HC in task-related studies. Abbreviations: AMYG, amygdala; CAU, caudate; FA, fractional anisotropy; HC, healthy controls; HIP, hippocampus; IFG, inferior frontal gyrus; INS, insula; IPL, inferior parietal lobule; MCI, mild cognitive impairment; MFG, middle frontal gyrus; PHG, parahippocampal; PCG, posterior cingulate; PCUN, precuneus; SPL, superior parietal lobule; STG, superior temporal gyrus; THA, thalamus.
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Figure 3. (a) Hypoactivation (in blue) and hyperactivation (in red) in Mild cognitive impairment relative to healthy controls during memory encoding. (b) Hypoactivation (in blue) and hyperactivation (in red) in MCI patients relative to healthy controls during memory retrieval. (c) Hypoactivation (in blue) and hyperactivation (in red) in MCI patients relative to healthy controls during WM task. Abbreviation: INS, insula; LING, lingual gyrus; MFG, middle frontal gyrus; MTG, middle temporal gyrus; PHG, parahippocampal; PCG, posterior cingulate; PCUN, precuneus; SFG, superior frontal gyrus; STG, superior temporal gyrus; WM, working memory.
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