Abnormal Changes of Brain Cortical Anatomy and the Association with Plasma MicroRNA107 Level in Amnestic Mild Cognitive Impairment

Wang, Tao; Shi, Feng; Jin, Yan; Jiang, Weixiong; Shen, Dinggang; Xiao, Shifu

doi:10.3389/fnagi.2016.00112

Cited by 19 publications

(22 citation statements)

References 53 publications

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“…Although their results are not directly comparable to ours, they found significantly greater percent change of thickness in the AD group compared with HC in similar bilateral regions, where we found significant differences between HC and AD: the temporal pole, lateral and medial temporal lobe, and PCC. Moreover, small parietal and temporal regions with significant differences between HC and amnestic MCI were found in Wang et al [] in both thickness and LGI. Julkunen et al [] found that MCIc displayed significantly reduced thickness bilaterally in the superior and middle frontal, superior, middle and inferior temporal, fusiform, and parahippocampal regions and the cingulate and retrosplenial cortices and also in the right precuneal and paracentral regions compared to MCIn subjects.…”

Section: Discussionmentioning

confidence: 97%

“…Results shown in Blanc et al [] and Fjell et al [] revealed significant correlations between thickness and MMSE in an AD group in distributed areas including parietal and temporal entorhinal cortices. In Wang et al [], the aMCI group shows small regions with significant correlation between thickness and MMSE (left post central, left inferior parietal, left precuneus, right supra marginal, and right fusiform), although in Yao et al [], only a region on the left‐middle and superior temporal gyrus presented a positive correlation between thickness and MMSE in aMCI. In Wang et al [], LGI and MMSE presented a positive correlation for the aMCI group in a small zone of the right superior temporal gyrus.…”

Section: Discussionmentioning

confidence: 99%

“…In Wang et al [], the aMCI group shows small regions with significant correlation between thickness and MMSE (left post central, left inferior parietal, left precuneus, right supra marginal, and right fusiform), although in Yao et al [], only a region on the left‐middle and superior temporal gyrus presented a positive correlation between thickness and MMSE in aMCI. In Wang et al [], LGI and MMSE presented a positive correlation for the aMCI group in a small zone of the right superior temporal gyrus. Similar to our previous comparison results, white‐matter SHFD, thickness, and LGI display patterns of cortical associations in which different cortical systems correlate with MMSE.…”

Section: Discussionmentioning

confidence: 99%

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Complexity analysis of cortical surface detects changes in future Alzheimer's disease converters

Miras

Costumero

Belloch

et al. 2017

Human Brain Mapping

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Alzheimer's disease (AD) is a neurological disorder that creates neurodegenerative changes at several structural and functional levels in human brain tissue. The fractal dimension (FD) is a quantitative parameter that characterizes the morphometric variability of the human brain. In this study we investigate spherical harmonic-based FD (SHFD), thickness and local gyrification index (LGI) to assess whether they identify cortical surface abnormalities toward the conversion to AD. We study 33 AD patients, 122 mild cognitive impairment (MCI) patients (50 MCI-converters and 29 MCI-non converters) and 32 healthy controls (HC). SHFD, thickness and LGI methodology allowed us to perform not only global but also local level assessments in each cortical surface vertex. First, we found that global SHFD decreased in AD and future MCI-converters compared to HC, and in MCI-converters compared to MCI-non-converters. Second, we found that local white matter SHFD was reduced in AD compared to HC and MCI mainly in medial temporal lobe. Third, local white matter SHFD was significantly reduced in MCI-converters compared to MCI-non-converters in distributed areas, including the medial frontal lobe. Thickness and LGI metrics presented a reduction in AD compared to HC. Thickness was significantly reduced in MCI-converters compared to healthy controls in entorhinal cortex and lateral temporal. In summary, SHFD was the only surface measure showing differences between MCI individuals that will convert or remain stable in the next four years. We suggest that SHFD may be an optimal complement to thickness loss analysis in monitoring longitudinal changes in preclinical and clinical stages of AD.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Complexity analysis of cortical surface detects changes in future Alzheimer's disease converters

Miras

Costumero

Belloch

et al. 2017

Human Brain Mapping

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“…Recently, genetic information has been increasingly explored to predict changes in cognitive examination performance (Allen et al, 2016). Therefore, the potential gene candidates closely associated to AD can also be incorporated into our algorithm to form a multimodality framework for better accuracy (Peng et al, 2016; Wang et al, 2016a). …”

Section: Discussionmentioning

confidence: 99%

Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest

Huang

Jin

Gao

et al. 2016

Neurobiology of Aging

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103

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Alzheimer’s disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies, machine learning techniques have shown promising results on the prediction of AD clinical scores. However, there are multiple limitations in the current models such as linearity assumption and missing data exclusion. Here, we present a nonlinear supervised sparse regression–based random forest (RF) framework to predict a variety of longitudinal AD clinical scores. Furthermore, we propose a soft-split technique to assign probabilistic paths to a test sample in RF for more accurate predictions. In order to benefit from the longitudinal scores in the study, unlike the previous studies that often removed the subjects with missing scores, we first estimate those missing scores with our proposed soft-split sparse regression–based RF and then utilize those estimated longitudinal scores at all the previous time points to predict the scores at the next time point. The experiment results demonstrate that our proposed method is superior to the traditional RF and outperforms other state-of-art regression models. Our method can also be extended to be a general regression framework to predict other disease scores.

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“…Recently, a variety of imaging modalities have been used for AD studies, such as structural MRI [1,2], diffusion MRI [3,4], and resting-state functional MRI (rs-fMRI) [5]. Different from structural and diffusion MRI that reveals brain morphological changes, rs-fMRI can examine both functional integration and segregation of brain networks that are undermined by MCI [6].…”

Section: Introductionmentioning

confidence: 99%

Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis

Yang

Jin

Chen

et al. 2016

Machine Learning in Medical Imaging

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The resting-state functional MRI (rs-fMRI) has been demonstrated as a valuable neuroimaging tool to identify mild cognitive impairment (MCI) patients. Previous studies showed network breakdown in MCI patients with thresholded rs-fMRI connectivity networks. Recently, machine learning techniques have assisted MCI diagnosis by integrating information from multiple networks constructed with a range of thresholds. However, due to the difficulty of searching optimal thresholds, they are often predetermined and uniformly applied to the entire network. Here, we propose an element-wise thresholding strategy to dynamically construct multiple functional networks, i.e., using possibly different thresholds for different elements in the connectivity matrix. These dynamically generated networks are then integrated with a network fusion scheme to capture their common and complementary information. Finally, the features extracted from the fused network are fed into support vector machine (SVM) for MCI diagnosis. Compared to the previous methods, our proposed framework can greatly improve MCI classification performance.

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Abnormal Changes of Brain Cortical Anatomy and the Association with Plasma MicroRNA107 Level in Amnestic Mild Cognitive Impairment

Cited by 19 publications

References 53 publications

Complexity analysis of cortical surface detects changes in future Alzheimer's disease converters

Complexity analysis of cortical surface detects changes in future Alzheimer's disease converters

Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest

Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis

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