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Wang X, Peng L, Zhan S, Yin X, Huang L, Huang J, Yang J, Zhang Y, Zeng Y, Liang S. Alterations in hippocampus-centered morphological features and function of the progression from normal cognition to mild cognitive impairment. Asian J Psychiatr 2024; 93:103921. [PMID: 38237533 DOI: 10.1016/j.ajp.2024.103921] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/21/2023] [Accepted: 01/06/2024] [Indexed: 03/08/2024]
Abstract
Mild cognitive impairment (MCI) is a significant precursor to dementia, highlighting the critical need for early identification of individuals at high risk of MCI to prevent cognitive decline. The study aimed to investigate the changes in brain structure and function before the onset of MCI. This study enrolled 19 older adults with progressive normal cognition (pNC) to MCI and 19 older adults with stable normal cognition (sNC). The gray matter (GM) volume and functional connectivity (FC) were estimated via magnetic resonance imaging during their normal cognition state 3 years prior. Additionally, spatial associations between FC maps and neurochemical profiles were examined using JuSpace. Compared to the sNC group, the pNC group showed decreased volume in the left hippocampus and left amygdala. The significantly positive correlation was observed between the GM volume of the left hippocampus and the MMSE scores after 3 years in pNC group. Besides, it showed that the pNC group had increased FC between the left hippocampus and the anterior-posterior cingulate gyrus, which was significantly correlated with the spatial distribution of dopamine D2 and noradrenaline transporter. Taken together, the study identified the abnormal brain characteristics before the onset of MCI, which might provide insight into clinical research.
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Affiliation(s)
- Xiuxiu Wang
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Lixin Peng
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Shiqi Zhan
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Xiaolong Yin
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Li Huang
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Jiayang Huang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Junchao Yang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Yusi Zhang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Yi Zeng
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Shengxiang Liang
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Traditional Chinese Medicine Rehabilitation Research Center of State Administration of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Fujian Key Laboratory of Cognitive Rehabilitation, Affiliated Rehabilitation Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fuzhou 350001, China.
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Devignes Q, Ren B, Clancy KJ, Howell K, Pollmann Y, Martinez-Sanchez L, Beard C, Kumar P, Rosso IM. Trauma-related intrusive memories and anterior hippocampus structural covariance: an ecological momentary assessment study in posttraumatic stress disorder. Transl Psychiatry 2024; 14:74. [PMID: 38307849 PMCID: PMC10837434 DOI: 10.1038/s41398-024-02795-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 01/09/2024] [Accepted: 01/17/2024] [Indexed: 02/04/2024] Open
Abstract
Trauma-related intrusive memories (TR-IMs) are hallmark symptoms of posttraumatic stress disorder (PTSD), but their neural correlates remain partly unknown. Given its role in autobiographical memory, the hippocampus may play a critical role in TR-IM neurophysiology. The anterior and posterior hippocampi are known to have partially distinct functions, including during retrieval of autobiographical memories. This study aimed to investigate the relationship between TR-IM frequency and the anterior and posterior hippocampi morphology in PTSD. Ninety-three trauma-exposed adults completed daily ecological momentary assessments for fourteen days to capture their TR-IM frequency. Participants then underwent anatomical magnetic resonance imaging to obtain measures of anterior and posterior hippocampal volumes. Partial least squares analysis was applied to identify a structural covariance network that differentiated the anterior and posterior hippocampi. Poisson regression models examined the relationship of TR-IM frequency with anterior and posterior hippocampal volumes and the resulting structural covariance network. Results revealed no significant relationship of TR-IM frequency with hippocampal volumes. However, TR-IM frequency was significantly negatively correlated with the expression of a structural covariance pattern specifically associated with the anterior hippocampus volume. This association remained significant after accounting for the severity of PTSD symptoms other than intrusion symptoms. The network included the bilateral inferior temporal gyri, superior frontal gyri, precuneus, and fusiform gyri. These novel findings indicate that higher TR-IM frequency in individuals with PTSD is associated with lower structural covariance between the anterior hippocampus and other brain regions involved in autobiographical memory, shedding light on the neural correlates underlying this core symptom of PTSD.
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Affiliation(s)
- Quentin Devignes
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric Biostatistics Laboratory, McLean Hospital, Belmont, MA, USA
| | - Kevin J Clancy
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Kristin Howell
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
| | - Yara Pollmann
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
| | | | - Courtney Beard
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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Zhang Q, Sheng J, Zhang Q, Wang L, Yang Z, Xin Y. Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease. Comput Biol Med 2023; 165:107392. [PMID: 37669585 DOI: 10.1016/j.compbiomed.2023.107392] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/30/2023] [Accepted: 08/25/2023] [Indexed: 09/07/2023]
Abstract
In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A framework for diagnosing AD is presented in this paper, which includes magnetic resonance imaging (MRI) image preprocessing, feature extraction, and the Fuzzy k-nearest neighbor algorithm (FKNN) model. In particular, the framework's novelty lies in the use of an improved Harris Hawks Optimization (HHO) algorithm named SSFSHHO, which integrates the Sobol sequence and Stochastic Fractal Search (SFS) mechanisms for optimizing the parameters of FKNN. The HHO method improves the quality of the initial population overall by incorporating the Sobol sequence, and the SFS mechanism increases the algorithm's capacity to get out of the local optimum solution. Comparisons with other classical meta-heuristic algorithms, state-of-the-art HHO variants in low and high dimensions, and enhanced meta-heuristic algorithms on 30 typical IEEE CEC2014 benchmark test problems show that the overall performance of SSFSHHO is significantly better than other comparative algorithms. Moreover, the created framework based on the SSFSHHO-FKNN model is employed to classify AD and MCI using MRI scans from the ADNI dataset, achieving high classification performance for 6 representative cases. Experimental findings indicate that the proposed algorithm performs better than a number of high-performance optimization algorithms and classical machine learning algorithms, thus offering a promising approach for AD classification. Additionally, the proposed strategy can successfully identify relevant features and enhance classification performance for AD diagnosis.
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Affiliation(s)
- Qian Zhang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China; School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, Zhejiang, 325035, China
| | - Jinhua Sheng
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China.
| | - Qiao Zhang
- Beijing Hospital, Beijing, 100730, China; National Center of Gerontology, Beijing, 100730, China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Luyun Wang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Ze Yang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Yu Xin
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
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Wang W, Peng J, Hou J, Yuan Z, Xie W, Mao G, Pan Y, Shao Y, Shu Z. Predicting mild cognitive impairment progression to Alzheimer's disease based on machine learning analysis of cortical morphological features. Aging Clin Exp Res 2023:10.1007/s40520-023-02456-1. [PMID: 37405620 DOI: 10.1007/s40520-023-02456-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/25/2023] [Indexed: 07/06/2023]
Abstract
PURPOSE To establish a model for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD) using morphological features extracted from a joint analysis of voxel-based morphometry (VBM) and surface-based morphometry (SBM). METHODS We analyzed data from 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, 32 of whom progressed to AD during a 4-year follow-up period and were classified as the progression group, while the remaining 89 were classified as the non-progression group. Patients were divided into a training set (n = 84) and a testing set (n = 37). Morphological features measured by VBM and SBM were extracted from the cortex of the training set and dimensionally reduced to construct morphological biomarkers using machine learning methods, which were combined with clinical data to build a multimodal combinatorial model. The model's performance was evaluated using receiver operating characteristic curves on the testing set. RESULTS The Alzheimer's Disease Assessment Scale (ADAS) score, apolipoprotein E (APOE4), and morphological biomarkers were independent predictors of MCI progression to AD. The combinatorial model based on the independent predictors had an area under the curve (AUC) of 0.866 in the training set and 0.828 in the testing set, with sensitivities of 0.773 and 0.900 and specificities of 0.903 and 0.747, respectively. The number of MCI patients classified as high-risk for progression to AD was significantly different from those classified as low-risk in the training set, testing set, and entire dataset, according to the combinatorial model (P < 0.05). CONCLUSION The combinatorial model based on cortical morphological features can identify high-risk MCI patients likely to progress to AD, potentially providing an effective tool for clinical screening.
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Affiliation(s)
- Wei Wang
- Jinzhou Medical University Postgraduate Education Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College), Hangzhou, Zhejiang, China
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Jiaxuan Peng
- Jinzhou Medical University Postgraduate Education Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College), Hangzhou, Zhejiang, China
| | - Jie Hou
- Jinzhou Medical University Postgraduate Education Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College), Hangzhou, Zhejiang, China
| | - Zhongyu Yuan
- Jinzhou Medical University Postgraduate Education Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College), Hangzhou, Zhejiang, China
| | - Wutao Xie
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Guohe Mao
- Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Yaling Pan
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Yuan Shao
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Zhenyu Shu
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China.
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