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Wang J, Liang X, Lu J, Zhang W, Chen Q, Li X, Chen J, Zhang X, Zhang B. Cortical and subcortical gray matter abnormalities in mild cognitive impairment. Neuroscience 2024; 557:81-88. [PMID: 39067683 DOI: 10.1016/j.neuroscience.2024.07.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 07/06/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024]
Abstract
Gray matter changes are thought to be closely related to cognitive decline in mild cognitive impairment (MCI) patients. The study aimed to explore cortical and subcortical structural alterations in MCI and their association with cognitive assessment. 24 MCI patients and 22 normal controls (NCs) were included. Voxel-based morphometry (VBM), vertex-based shape analysis and surface-based morphometry (SBM) analysis were applied to explore subcortical nuclei volume, shape and cortical morphology. Correlations between structural changes and cognition were explored using spearman correlation analysis. Support vector machine (SVM) classification evaluated MCI identification accuracy. MCI patients showed significant atrophy in the left thalamus, left hippocampus, left amygdala, right pallidum, right hippocampus, along with inward deformation in the left amygdala. SBM analysis revealed that MCI group exhibited shallower sulci depth in the left hemisphere and increased cortical gyrification index (GI) in the right frontal gyrus. Correlation analysis showed the positive correlation between right hippocampus volume and episodic memory, while negative correlation between the altered GI and memory performance in MCI group. SVM analysis demonstrated superior performance of sulci depth and GI derived from SBM in MCI identification. When combined with cortical and subcortical metrics, SVM achieved a peak accuracy of 89 % in distinguishing MCI from NC. The study reveals significant gray matter structural changes in MCI, suggesting their potential role in underlying functional differences and neural mechanisms behind memory impairment in MCI.
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Affiliation(s)
- Junxia Wang
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Xue Liang
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Wen Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Qian Chen
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Xin Li
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Jiu Chen
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Xin Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China; Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing 210008, China.
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Cheng Z, Nie W, Leng J, Yang L, Wang Y, Li X, Guo L. Amygdala and cognitive impairment in cerebral small vessel disease: structural, functional, and metabolic changes. Front Neurol 2024; 15:1398009. [PMID: 39070051 PMCID: PMC11275956 DOI: 10.3389/fneur.2024.1398009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024] Open
Abstract
Cerebral small vessel disease (CSVD) is a prevalent vascular disorder that has been consistently associated with vascular cognitive impairment (VCI). The diagnosis of CSVD continues to rely on magnetic resonance imaging (MRI). Epidemiological data indicate that the characteristic MRI features of CSVD, including white matter hyperintensity (WMH) and lacunar infarction, are very common among individuals over 40 years of age in community studies. This prevalence poses a significant burden on many low- and middle-income families. The amygdala plays a crucial role in integrating sensory and associative information to regulate emotional cognition. Although many previous studies have linked alterations in the amygdala to various diseases, such as depression, there has been little research on CSVD-associated alterations in the amygdala due to the complexity of CSVD. In this paper, we summarize the various imaging features of CSVD and discuss the correlation between amygdala changes and VCI. We also explore how new neuroimaging methods can assess amygdala changes early, laying a foundation for future comprehensive exploration of the pathogenesis of CSVD.
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Affiliation(s)
- Zhenyu Cheng
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, China
| | - Wenying Nie
- Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Junhong Leng
- Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Linfeng Yang
- Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yuanyuan Wang
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, China
| | - Xianglin Li
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, China
| | - Lingfei Guo
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
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Yang M, Meng S, Wu F, Shi F, Xia Y, Feng J, Zhang J, Li C. Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging. Front Med (Lausanne) 2024; 11:1305565. [PMID: 38283620 PMCID: PMC10811129 DOI: 10.3389/fmed.2024.1305565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/02/2024] [Indexed: 01/30/2024] Open
Abstract
Purpose Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). Method This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Result Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.
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Affiliation(s)
- Mingguang Yang
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Shan Meng
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Faqi Wu
- Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Jinan, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Jinrui Zhang
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
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Li P, Quan W, Wang Z, Liu Y, Cai H, Chen Y, Wang Y, Zhang M, Tian Z, Zhang H, Zhou Y. Early-stage differentiation between Alzheimer's disease and frontotemporal lobe degeneration: Clinical, neuropsychology, and neuroimaging features. Front Aging Neurosci 2022; 14:981451. [PMID: 36389060 PMCID: PMC9659748 DOI: 10.3389/fnagi.2022.981451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/10/2022] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) are the two most common forms of neurodegenerative dementia. Although both of them have well-established diagnostic criteria, achieving early diagnosis remains challenging. Here, we aimed to make the differential diagnosis of AD and FTLD from clinical, neuropsychological, and neuroimaging features. MATERIALS AND METHODS In this retrospective study, we selected 95 patients with PET-CT defined AD and 106 patients with PET-CT/biomarker-defined FTLD. We performed structured chart examination to collect clinical data and ascertain clinical features. A series of neuropsychological scales were used to assess the neuropsychological characteristics of patients. Automatic tissue segmentation of brain by Dr. Brain tool was used to collect multi-parameter volumetric measurements from different brain areas. All patients' structural neuroimage data were analyzed to obtain brain structure and white matter hyperintensities (WMH) quantitative data. RESULTS The prevalence of vascular disease associated factors was higher in AD patients than that in FTLD group. 56.84% of patients with AD carried at least one APOE ε4 allele, which is much high than that in FTLD patients. The first symptoms of AD patients were mostly cognitive impairment rather than behavioral abnormalities. In contrast, behavioral abnormalities were the prominent early manifestations of FTLD, and few patients may be accompanied by memory impairment and motor symptoms. In direct comparison, patients with AD had slightly more posterior lesions and less frontal atrophy, whereas patients with FTLD had more frontotemporal atrophy and less posterior lesions. The WMH burden of AD was significantly higher, especially in cortical areas, while the WMH burden of FTLD was higher in periventricular areas. CONCLUSION These results indicate that dynamic evaluation of cognitive function, behavioral and psychological symptoms, and multimodal neuroimaging are helpful for the early diagnosis and differentiation between AD and FTLD.
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Affiliation(s)
- Pan Li
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Wei Quan
- Department of Neurosurgery, General Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Injuries, Variations and Regeneration of Nervous System, Tianjin Neurological Institute, Tianjin, China
| | - Zengguang Wang
- Department of Neurosurgery, General Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Injuries, Variations and Regeneration of Nervous System, Tianjin Neurological Institute, Tianjin, China
| | - Ying Liu
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Hao Cai
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Yuan Chen
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Yan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Miao Zhang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhiyan Tian
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Huihong Zhang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
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Sun M, Wang YL, Li R, Jiang J, Zhang Y, Li W, Zhang Y, Jia Z, Chappell M, Xu J. Potential Diagnostic Applications of Multi-Delay Arterial Spin Labeling in Early Alzheimer’s Disease: The Chinese Imaging, Biomarkers, and Lifestyle Study. Front Neurosci 2022; 16:934471. [PMID: 35937865 PMCID: PMC9353523 DOI: 10.3389/fnins.2022.934471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/21/2022] [Indexed: 11/18/2022] Open
Abstract
Background Cerebral blood flow (CBF) alterations are involved in the onset and progression of Alzheimer’s disease (AD) and can be a potential biomarker. However, CBF measured by single-delay arterial spin labeling (ASL) for discrimination of mild cognitive impairment (MCI, an early stage of AD) was lack of accuracy. Multi-delay ASL can not only provide CBF quantification but also provide arterial transit time (ATT). Unfortunately, the technique was scarcely applied to the diagnosis of AD. Here, we detected the utility of ASL with 1-delay and 7-delay in ten regions of interest (ROIs) to identify MCI and AD. Materials and Methods Pseudocontinuous ASL (pCASL) MRI was acquired on a 3T GE scanner in adults from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) Study of AD cohort, including 26 normal cognition (NC), 37 MCI, and 39 AD. Receiver operating characteristic (ROC) analyses with 1-delay and 7-delay ASL were performed for the identification of MCI and AD. The DeLong test was used to compare ROC curves. Results For CBF of 1-delay or 7-delay the AUCs showed moderate-high performance for the AD/NC and AD/MCI comparisons (AUC = 0.83∼0.96) (p < 0.001). CBF of 1-delay performed poorly in MCI/NC comparison (AUC = 0.69) (p < 0.001), but CBF of 7-delay fared well with an AUC of 0.79 (p < 0.001). The combination of CBF and ATT of 7-delay showed higher performance for AD/NC, AD/MCI, and MCI/NC comparisons with AUCs of 0.96, 0.89, and 0.89, respectively (p < 0.001). Furthermore, combination of CBF, ATT, sex, age, APOE ε4, and education improved further the accuracy (p < 0.001). In subgroups analyses, there were no significant differences in CBF of 7-delay ASL for identification of AD or MCI between age subgroups (p > 0.05). Conclusion The combination of CBF and ATT with 7-delay ASL showed higher performance for identification of MCI than CBF of 1-delay, when adding to sex, age, APOE ε4 carrier status, and education years, the diagnostic performance was further increased, presenting a potential imaging biomarker in early AD.
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Affiliation(s)
- Mengfan Sun
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yan-Li Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Runzhi Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiwei Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yanling Zhang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenyi Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuan Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ziyan Jia
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Michael Chappell
- Mental Health and Clinical Neurosciences and Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham Biomedical Research Centre, Queen’s Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- *Correspondence: Jun Xu,
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OUP accepted manuscript. Arch Clin Neuropsychol 2022; 37:1502-1514. [DOI: 10.1093/arclin/acac018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
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