Yu Sun

Abnormal Resting-State Functional Connectivity Strength in Mild Cognitive Impairment and Its Conversion to Alzheimer’s Disease

Individuals diagnosed with mild cognitive impairment (MCI) are at high risk of transition to Alzheimers disease (AD). However, little is known about functional characteristics of the conversion from MCI to AD. Resting-state functional magnetic resonance imaging was performed in 25 AD patients, 31 MCI patients, and 42 well-matched normal controls at baseline. Twenty-one of the 31 MCI patients converted to AD at approximately 24 months of follow-up. Functional connectivity strength (FCS) and seed-based functional connectivity analyses were used to assess the functional differences among the groups. Compared to controls, subjects with MCI and AD showed decreased FCS in the default-mode network and the occipital cortex. Importantly, the FCS of the left angular gyrus and middle occipital gyrus was significantly lower in MCI-converters as compared with MCI-nonconverters. Significantly decreased functional connectivity was found in MCI-converters compared to nonconverters between the left angular gyrus and bilateral inferior parietal lobules, dorsolateral prefrontal and lateral temporal cortices, and the left middle occipital gyrus and right middle occipital gyri. We demonstrated gradual but progressive functional changes during a median 2-year interval in patients converting from MCI to AD, which might serve as early indicators for the dysfunction and progression in the early stage of AD.

Subjective Cognitive Decline: Mapping Functional and Structural Brain Changes—A Combined Resting-State Functional and Structural MR Imaging Study

Purpose To determine whether individuals with subjective cognitive decline (SCD) exhibit functional and structural brain alterations by using resting-state functional and structural magnetic resonance (MR) imaging. Materials and Methods This study received institutional review board approval, and all participants gave informed consent. Resting-state functional MR imaging and structural MR imaging techniques were used to measure amplitude of low-frequency fluctuations (ALFF) and regional gray matter volume in 25 subjects with SCD (mean age, 65.52 years ± 6.12) and 61 control subjects (mean age, 64.11 years ± 8.59). Voxel-wise general linear model analyses were used to examine between-group differences in ALFF or in gray matter volume and to further determine the brain-behavioral relationship. Results Subjects with SCD exhibited higher ALFF values than did control subjects in the bilateral inferior parietal lobule (left: 0.44 ± 0.25 vs 0.27 ± 0.18, respectively; P = .0003; right: 1.46 ± 0.45 vs 1.10 ± 0.37, respectively; P = .0015), right inferior (0.45 ± 0.15 vs 0.37 ± 0.08, repectively; P = .0106) and middle (1.03 ± 0.32 vs 0.83 ± 0.20, respectively; P = .0008) occipital gyrus, right superior temporal gyrus (0.11 ± 0.07 vs 0.07 ± 0.04, respectively; P = .0016), and right cerebellum posterior lobe (0.51 ± 0.27 vs 0.39 ± 0.15, respectively; P = .0010). In the SCD group, significant correlations were found between Auditory Verbal Learning Test recognition scores and ALFF in the left inferior parietal lobe (r = -0.79, P < .001) and between Auditory Verbal Learning Test immediate recall scores and ALFF values in the right middle occipital gyrus (r = -0.64, P = .002). Nonsignificant group differences were found in gray matter volume (P > .05, corrected). Conclusion Individuals with SCD had altered spontaneous functional activity, suggesting that resting-state functional MR imaging may be a noninvasive method for characterizing SCD. (©) RSNA, 2016 Online supplemental material is available for this article.

Identification of Amnestic Mild Cognitive Impairment Using Multi-Modal Brain Features: A Combined Structural MRI and Diffusion Tensor Imaging Study

Identifying amnestic mild cognitive impairment (aMCI) is of great clinical importance because aMCI is a putative prodromal stage of Alzheimers disease. The present study aimed to explore the feasibility of accurately identifying aMCI with a magnetic resonance imaging (MRI) biomarker. We integrated measures of both gray matter (GM) abnormalities derived from structural MRI and white matter (WM) alterations acquired from diffusion tensor imaging at the voxel level across the entire brain. In particular, multi-modal brain features, including GM volume, WM fractional anisotropy, and mean diffusivity, were extracted from a relatively large sample of 64 Han Chinese aMCI patients and 64 matched controls. Then, support vector machine classifiers for GM volume, FA, and MD were fused to distinguish the aMCI patients from the controls. The fused classifier was evaluated with the leave-one-out and the 10-fold cross-validations, and the classifier had an accuracy of 83.59% and an area under the curve of 0.862. The most discriminative regions of GM were mainly located in the medial temporal lobe, temporal lobe, precuneus, cingulate gyrus, parietal lobe, and frontal lobe, whereas the most discriminative regions of WM were mainly located in the corpus callosum, cingulum, corona radiata, frontal lobe, and parietal lobe. Our findings suggest that aMCI is characterized by a distributed pattern of GM abnormalities and WM alterations that represent discriminative power and reflect relevant pathological changes in the brain, and these changes further highlight the advantage of multi-modal feature integration for identifying aMCI.

White matter degeneration in subjective cognitive decline: a diffusion tensor imaging study

Subjective cognitive decline (SCD) may be an at-risk stage of Alzheimers disease (AD) occurring prior to amnestic mild cognitive impairment (aMCI). To examine white matter (WM) defects in SCD, diffusion images from 27 SCD (age=65.3±8.0), 35 aMCI (age=69.2±8.6) and 25 AD patients (age=68.3±9.4) and 37 normal controls (NC) (age=65.1±6.8) were compared using Tract-Based Spatial Statistics (TBSS). WM impairments common to the three patient groups were extracted, and fractional anisotropy (FA) values were averaged in each group. As compared to NC subjects, SCD patients displayed widespread WM alterations represented by decreased FA (p<0.05), increased mean diffusivity (MD; p<0.05), and increased radial diffusivity (RD; p<0.05). In addition, localized WM alterations showed increased axial diffusivity (AxD; p<0.05) similar to what was observed in aMCI and AD patients (p<0.05). In the shared WM impairment tracts, SCD patients had FA values between the NC group and the other two patient groups. In the NC and SCD groups, the AVLT-delayed recall score correlated with higher AxD (r=−0.333, p=0.045), MD (r=−0.351, p=0.03) and RD (r=−0.353, p=0.025). In both the aMCI and AD groups the diffusion parameters were highly correlated with cognitive scores. Our study suggests that SCD patients present with widespread WM changes, which may contribute to the early memory decline they experience.

Abnormal organization of white matter networks in patients with subjective cognitive decline and mild cognitive impairment

Network analysis has been widely used in studying Alzheimers disease (AD). However, how the white matter network changes in cognitive impaired patients with subjective cognitive decline (SCD) (a symptom emerging during early stage of AD) and amnestic mild cognitive impairment (aMCI) (a pre-dementia stage of AD) is still unclear. Here, structural networks were constructed respectively based on FA and FN for 36 normal controls, 21 SCD patients, and 33 aMCI patients by diffusion tensor imaging and graph theory. Significantly lower efficiency was found in aMCI patients than normal controls (NC). Though not significant, the values in those with SCD were intermediate between aMCI and NC. In addition, our results showed significantly altered betweenness centrality located in right precuneus, calcarine, putamen, and left anterior cingulate in aMCI patients. Furthermore, association was found between network metrics and cognitive impairment. Our study suggests that the structural network properties might be preserved in SCD stage and disrupted in aMCI stage, which may provide novel insights into pathological mechanisms of AD.

Decreased resting-state brain signal complexity in patients with mild cognitive impairment and Alzheimer’s disease: a multi-scale entropy analysis

Multiscale entropy (MSE) analysis is a novel entropy-based analysis method for quantifying the complexity of dynamic neural signals and physiological systems across multiple temporal scales. This approach may assist in elucidating the pathophysiologic mechanisms of amnestic mild cognitive impairment (aMCI) and Alzheimers disease (AD). Using resting-state fNIRS imaging, we recorded spontaneous brain activity from 31 healthy controls (HC), 27 patients with aMCI, and 24 patients with AD. The quantitative analysis of MSE revealed that reduced brain signal complexity in AD patients in several networks, namely, the default, frontoparietal, ventral and dorsal attention networks. For the default and ventral attention networks, the MSE values also showed significant positive correlations with cognitive performances. These findings demonstrated that the MSE-based analysis method could serve as a novel tool for fNIRS study in characterizing and understanding the complexity of abnormal cortical signals in AD cohorts.

Local-to-remote cortical connectivity in amnestic mild cognitive impairment

Alterations in both local and remote connectivity were reported in amnestic mild cognitive impairment (aMCI) patients but rarely in the same group of patients. In the present study, we employed a novel resting-state functional magnetic resonance imaging (rfMRI) connectome index, regional functional homogeneity on the 2-dimensional cortical surface, to detect full-cortex vertex-wise changes of the local rfMRI connectivity in 32 aMCI patients compared with 40 healthy controls. We further used the seed-based functional connectivity to explore the remote rfMRI connectivity in aMCI. The results revealed significantly lower local connectivity in the default network and higher local connectivity in the somatomotor network in aMCI patients. Abnormal remote connectivity relevant to local connectivity was primarily detectable within the default network (decrease) and in the somatomotor and attention networks (increase). The abnormalities in the remote (not local) default network connectivity were significantly associated with episodic memory performance in patients. These distance-related connectivity profiles illustrated a dysfunctional pattern in aMCI, which extended our knowledge of this pathological aging process.

MACHINE LEARNING DIFFERENTIATES EARLY STAGES OF ALZHEIMER’S DISEASE FROM NORMAL AGING: A BRAIN MORPHOMETRY STUDY

methanol). Interestingly, we identified several metabolites which were disproportionately affected in mild AD cases than severe, the majority of which turned out to be lipids. Conclusions: Combining two complementary analytical techniques offers a more holistic view of the brain metabolome. Brain metabolic responses differ according to disease severity providing clues about how the disease pathology develops. Future studies should investigate the disease mechanisms and the reproducibility of the metabolite biomarkers discovered here.

ABNORMAL BRAIN CONNECTIVITY DYNAMICS AND BRAIN ACTIVITY STATES IN ALZHEIMER’S DISEASE

infarcts were detected (12 patients (13%); 20 cavitated, 1 non-cavitated; mean size 5.2mm), compared to 48 primary care patients not selected on disease status (1 patient (2%); 1 cavitated; p1⁄40.031; Figure 1). Conclusions: We established reliable imaging criteria for the detection of small infarcts in the caudate nucleus on 7t MRI that can be used in future studies to provide new insights into the pathophysiology of CSVD.

HOW DOES WHITE MATTER CONNECTIVITY DIFFER BETWEEN VASCULAR AND DEGENERATIVE PRE-DEMENTIA?

tients (76% in AD, 75% in FTD, 50% in DLB, and 89% in Others). Among 29 patients with negative [C]PiB-PET scan, 11 patients were evaluated as even not having any progressive neurological disorders and the result lead to drastic change of prognostic view. Conclusions: [C]PiB-PET imaging has high clinical impact on the diagnosis of young onset neurodegenerative dementias. Further studies are required to evaluate the total impact of amyloid imaging in young onset dementias not only on the medical diagnosis but also on social issues such as occupational and familial activities.