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Amini S, Hao B, Yang J, Karjadi C, Kolachalama VB, Au R, Paschalidis IC. Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models. Alzheimers Dement 2024. [PMID: 38924662 DOI: 10.1002/alz.13886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 03/01/2024] [Accepted: 04/19/2024] [Indexed: 06/28/2024]
Abstract
INTRODUCTION Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODS We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTS Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. DISCUSSION The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. HIGHLIGHTS Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.
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Affiliation(s)
- Samad Amini
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Boran Hao
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Jingmei Yang
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Cody Karjadi
- Framingham Heart Study, Boston University, Framingham, Massachusetts, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA
- Department of Computer Science, Boston University, Boston, Massachusetts, USA
| | - Rhoda Au
- Framingham Heart Study, Boston University, Framingham, Massachusetts, USA
- Departments of Anatomy & Neurobiology, Neurology, and Epidemiology, Boston University School of Medicine and School of Public Health, Boston, Massachusetts, USA
| | - Ioannis C Paschalidis
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA
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Wang X, Zhou S, Ye N, Li Y, Zhou P, Chen G, Hu H. Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal. BMC Geriatr 2024; 24:531. [PMID: 38898411 PMCID: PMC11188292 DOI: 10.1186/s12877-024-05044-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 05/06/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Mild cognitive impairment has received widespread attention as a high-risk population for Alzheimer's disease, and many studies have developed or validated predictive models to assess it. However, the performance of the model development remains unknown. OBJECTIVE The objective of this review was to provide an overview of prediction models for the risk of Alzheimer's disease dementia in older adults with mild cognitive impairment. METHOD PubMed, EMBASE, Web of Science, and MEDLINE were systematically searched up to October 19, 2023. We included cohort studies in which risk prediction models for Alzheimer's disease dementia in older adults with mild cognitive impairment were developed or validated. The Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed to assess model bias and applicability. Random-effects models combined model AUCs and calculated (approximate) 95% prediction intervals for estimations. Heterogeneity across studies was evaluated using the I2 statistic, and subgroup analyses were conducted to investigate sources of heterogeneity. Additionally, funnel plot analysis was utilized to identify publication bias. RESULTS The analysis included 16 studies involving 9290 participants. Frequency analysis of predictors showed that 14 appeared at least twice and more, with age, functional activities questionnaire, and Mini-mental State Examination scores of cognitive functioning being the most common predictors. From the studies, only two models were externally validated. Eleven studies ultimately used machine learning, and four used traditional modelling methods. However, we found that in many of the studies, there were problems with insufficient sample sizes, missing important methodological information, lack of model presentation, and all of the models were rated as having a high or unclear risk of bias. The average AUC of the 15 best-developed predictive models was 0.87 (95% CI: 0.83, 0.90). DISCUSSION Most published predictive modelling studies are deficient in rigour, resulting in a high risk of bias. Upcoming research should concentrate on enhancing methodological rigour and conducting external validation of models predicting Alzheimer's disease dementia. We also emphasize the importance of following the scientific method and transparent reporting to improve the accuracy, generalizability and reproducibility of study results. REGISTRATION This systematic review was registered in PROSPERO (Registration ID: CRD42023468780).
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Affiliation(s)
- Xiaotong Wang
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Shi Zhou
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Niansi Ye
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Yucan Li
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Pengjun Zhou
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Gao Chen
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Hui Hu
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China.
- Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Wuhan, China.
- Hubei Shizhen Laboratory, Wuhan, China.
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Zheng D, Ruan Y, Cao X, Guo W, Zhang X, Qi W, Yuan Q, Liang X, Zhang D, Huang Q, Xue C. Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment. Neuroscience 2024; 545:47-58. [PMID: 38490330 DOI: 10.1016/j.neuroscience.2024.03.003] [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: 11/08/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.
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Affiliation(s)
- Darui Zheng
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yiming Ruan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, USA
| | - Wenxuan Guo
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xulian Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuhong Liang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Da Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qingling Huang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
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Romano MF, Zhou X, Balachandra AR, Jadick MF, Qiu S, Nijhawan DA, Joshi PS, Mohammad S, Lee PH, Smith MJ, Paul AB, Mian AZ, Small JE, Chin SP, Au R, Kolachalama VB. Deep learning for risk-based stratification of cognitively impaired individuals. iScience 2023; 26:107522. [PMID: 37646016 PMCID: PMC10460987 DOI: 10.1016/j.isci.2023.107522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 09/01/2023] Open
Abstract
Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-β levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.
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Affiliation(s)
- Michael F. Romano
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Xiao Zhou
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Akshara R. Balachandra
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michalina F. Jadick
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Shangran Qiu
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Diya A. Nijhawan
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Prajakta S. Joshi
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of General Dentistry, Boston University School of Dental Medicine, Boston, MA, USA
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Shariq Mohammad
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Peter H. Lee
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Maximilian J. Smith
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Aaron B. Paul
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Asim Z. Mian
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Juan E. Small
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Sang P. Chin
- Department of Computer Science, Boston University, Boston, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Center of Mathematical Sciences & Applications, Harvard University, Cambridge, MA, USA
| | - Rhoda Au
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Boston University Alzheimer’s Disease Research Center, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Vijaya B. Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Computer Science, Boston University, Boston, MA, USA
- Boston University Alzheimer’s Disease Research Center, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA
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Yuan Q, Liang X, Xue C, Qi W, Chen S, Song Y, Wu H, Zhang X, Xiao C, Chen J. Altered anterior cingulate cortex subregional connectivity associated with cognitions for distinguishing the spectrum of pre-clinical Alzheimer's disease. Front Aging Neurosci 2022; 14:1035746. [PMID: 36570538 PMCID: PMC9768430 DOI: 10.3389/fnagi.2022.1035746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Background Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are considered part of the early progression continuum of Alzheimer's disease (AD). The anterior cingulate cortex (ACC), a hub of information processing and regulation in the brain, plays an essential role in AD pathophysiology. In the present study, we aimed to systematically identify changes in the functional connectivity (FC) of ACC subregions in patients with SCD and aMCI and evaluate the association of these changes with cognition. Materials and methods Functional connectivity (FC) analysis of ACC sub-regions was performed among 66 patients with SCD, 71 patients with aMCI, and 78 healthy controls (HCs). Correlation analyses were performed to examine the relationship between FC of altered ACC subnetworks and cognition. Results Compared to HCs, SCD patients showed increased FC of the bilateral precuneus (PCUN) and caudal ACC, left superior frontal gyrus (SFG) and subgenual ACC, left inferior parietal lobule (IPL) and dorsal ACC, left middle occipital gyrus (MOG) and dorsal ACC, and left middle temporal gyrus (MTG) and subgenual ACC, while aMCI patients showed increased FC of the left inferior frontal gyrus (IFG) and dorsal ACC and left medial frontal gyrus (MFG) and subgenual ACC. Compared to patients with SCD, patients with aMCI showed increased FC of the right MFG and dorsal ACC and left ACC and subgenual ACC, while the left posterior cingulate cortex (PCC) showed decreased FC with the caudal ACC. Moreover, some FC values among the altered ACC subnetworks were significantly correlated with episodic memory and executive function. Conclusion SCD and aMCI, part of the spectrum of pre-clinical AD, share some convergent and divergent altered intrinsic connectivity of ACC subregions. These results may serve as neuroimaging biomarkers of the preclinical phase of AD and provide new insights into the design of preclinical interventions.
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Affiliation(s)
- Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuhong Liang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Huimin Wu
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xulian Zhang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Chaoyong Xiao,
| | - Jiu Chen
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China,Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China,Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China,Jiu Chen,
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA,Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA,Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Effect of Resveratrol Combined with Donepezil Hydrochloride on Inflammatory Factor Level and Cognitive Function Level of Patients with Alzheimer's Disease. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9148650. [PMID: 35368930 PMCID: PMC8975642 DOI: 10.1155/2022/9148650] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/04/2022] [Accepted: 03/10/2022] [Indexed: 02/07/2023]
Abstract
Objective To explore the effect of resveratrol (RES) combined with donepezil hydrochloride on inflammatory factor level and cognitive function level of patients with Alzheimer's disease (AD). Methods A total of 90 AD patients treated in our hospital from June 2019 to June 2020 were selected as the study objects and divided into the control group (CG) and experimental group (EG) by the randomized and double-blind method, with 45 cases each. Patients in CG received donepezil hydrochloride treatment, and on this basis, those in EG received additional RES treatment, so as to compare the clinical indicators between the two groups. Results Compared with CG after treatment, EG obtained significantly higher good rate, MMSE score, and FIM score (P < 0.05) and obviously lower clinical indicators and ADAS-cog score (P < 0.001), and between CG and EG, no obvious difference in total incidence rate of adverse reactions was observed after treatment (P > 0.05). Conclusion Combining RES with donepezil hydrochloride has significant clinical efficacy in treating AD, which can effectively improve patients' inflammatory factor indicators, promote their cognitive function, and facilitate patient prognosis.
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Chen J, Chen R, Xue C, Qi W, Hu G, Xu W, Chen S, Rao J, Zhang F, Zhang X. Hippocampal-Subregion Mechanisms of Repetitive Transcranial Magnetic Stimulation Causally Associated with Amelioration of Episodic Memory in Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2021; 85:1329-1342. [PMID: 34924374 DOI: 10.3233/jad-210661] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background: Altered hippocampal subregions (HIPsub) and their network connectivity relate to episodic memory decline in amnestic mild cognitive impairment (aMCI), which is significantly limited by over-dependence on correlational associations. Objective: To identify whether restoration of HIPsub and its network connectivity using repetitive transcranial magnetic stimulation (rTMS) is causally linked to amelioration of episodic memory in aMCI. Methods: In the first cohort, analysis of HIPsub grey matter (GM) and its functional connectivity was performed to identify an episodic memory-related circuit in aMCI by using a pattern classification approach. In the second cohort, this circuit was experimentally modulated with rTMS. Structural equation modeling was employed to investigate rTMS regulatory mechanism in amelioration of episodic memory. Results: First, in the first cohort, this study identified HIPsub circuit pathology of episodic memory decline in aMCI patients. Second, in the second cohort, restoration of HIPc GM and its connectivity with left middle temporal gyrus (MTG.L) are causally associated with amelioration of episodic memory in aMCI after 4 weeks of rTMS. Especially important, the effects of HIPc GM changes on the improvement of episodic memory were significantly mediated by HIPc connectivity with MTG.L changes in aMCI. Conclusion: This study provides novel experimental evidence about a biological substrate for the treatment of the disabling episodic memory in aMCI patients. Correction of breakdown in HIPc structure and its connectivity with MTG can causally ameliorate episodic memory in aMCI.
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Affiliation(s)
- Jiu Chen
- Institute of Neuropsychiatry>, the Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Rong Chen
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Neuropsychiatry>, the Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Department of rehabilitation, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Fuquan Zhang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangrong Zhang
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Department of Geriatric Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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9
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Xue C, Qi W, Yuan Q, Hu G, Ge H, Rao J, Xiao C, Chen J. Disrupted Dynamic Functional Connectivity in Distinguishing Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment Based on the Triple-Network Model. Front Aging Neurosci 2021; 13:711009. [PMID: 34603006 PMCID: PMC8484524 DOI: 10.3389/fnagi.2021.711009] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/16/2021] [Indexed: 12/20/2022] Open
Abstract
Background: Subjective cognitive decline and amnestic mild cognitive impairment (aMCI) were widely thought to be preclinical AD spectrum disorders, characterized by aberrant functional connectivity (FC) within the triple networks of the default mode network (DMN), the salience network (SN), and the executive control network (ECN). Dynamic FC (DFC) analysis can capture temporal fluctuations in brain FC during the scan, which static FC analysis cannot. The purpose of the current study was to explore the changes in dynamic FC within the triple networks of the preclinical AD spectrum and further reveal their potential diagnostic value in diagnosing preclinical AD spectrum disorders. Methods: We collected resting-state functional magnetic resonance imaging data from 44 patients with subjective cognitive decline (SCD), 49 with aMCI, and 58 healthy controls (HCs). DFC analysis based on the sliding time-window correlation method was used to analyze DFC variability within the triple networks in the three groups. Then, correlation analysis was conducted to reveal the relationship between altered DFC variability within the triple networks and a decline in cognitive function. Furthermore, logistic regression analysis was used to assess the diagnostic accuracy of altered DFC variability within the triple networks in patients with SCD and aMCI. Results: Compared with the HC group, the groups with SCD and aMCI both showed altered DFC variability within the triple networks. DFC variability in the right middle temporal gyrus and left inferior frontal gyrus (IFG) within the ECN were significantly different between patients with SCD and aMCI. Moreover, the altered DFC variability in the left IFG within the ECN was obviously associated with a decline in episodic memory and executive function. The logistic regression analysis showed that multivariable analysis had high sensitivity and specificity for diagnosing SCD and aMCI. Conclusions: Subjective cognitive decline and aMCI showed varying degrees of change in DFC variability within the triple networks and altered DFC variability within the ECN involved episodic memory and executive function. More importantly, altered DFC variability and the triple-network model proved to be important biomarkers for diagnosing and identifying patients with preclinical AD spectrum disorders.
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Affiliation(s)
- Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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10
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Song Y, Xu W, Chen S, Hu G, Ge H, Xue C, Qi W, Lin X, Chen J. Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis. Front Aging Neurosci 2021; 13:695210. [PMID: 34381352 PMCID: PMC8350339 DOI: 10.3389/fnagi.2021.695210] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 06/01/2021] [Indexed: 01/03/2023] Open
Abstract
Background Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Amnestic MCI (aMCI) and non-amnestic MCI are the two subtypes of MCI with the former having a higher risk for progressing to Alzheimer's disease (AD). Compared with healthy elderly adults, individuals with MCI have specific functional alterations in the salience network (SN). However, no consistent results are documenting these changes. This meta-analysis aimed to investigate the specific functional alterations in the SN in MCI and aMCI. Methods: We systematically searched PubMed, Embase, and Web of Science for scientific neuroimaging literature based on three research methods, namely, functional connectivity (FC), regional homogeneity (ReHo), and the amplitude of low-frequency fluctuation or fractional amplitude of low-frequency fluctuation (ALFF/fALFF). Then, we conducted the coordinate-based meta-analysis by using the activation likelihood estimation algorithm. Results: In total, 30 functional neuroimaging studies were included. After extracting the data and analyzing it, we obtained specific changes in some brain regions in the SN including decreased ALFF/fALFF in the left superior temporal gyrus, the insula, the precentral gyrus, and the precuneus in MCI and aMCI; increased FC in the thalamus, the caudate, the superior temporal gyrus, the insula, and the cingulate gyrus in MCI; and decreased ReHo in the anterior cingulate gyrus in aMCI. In addition, as to FC, interactions of the SN with other networks including the default mode network and the executive control network were also observed mainly in the middle frontal gyrus and superior frontal gyrus in MCI and inferior frontal gyrus in aMCI. Conclusions: Specific functional alternations in the SN and interactions of the SN with other networks in MCI could be useful as potential imaging biomarkers for MCI or aMCI. Meanwhile, it provided a new insight in predicting the progression of health to MCI or aMCI and novel targets for proper intervention to delay the progression. Systematic Review Registration: [PROSPERO], identifier [No. CRD42020216259].
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Affiliation(s)
- Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
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11
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Shu ZY, Mao DW, Xu YY, Shao Y, Pang PP, Gong XY. Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model. Ther Adv Neurol Disord 2021; 14:17562864211029551. [PMID: 34349837 PMCID: PMC8290507 DOI: 10.1177/17562864211029551] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 06/07/2021] [Indexed: 11/20/2022] Open
Abstract
Objective: This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Methods: 357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T1WI MR images were automatically segmented into white matter, gray matter and cerebrospinal fluid (CSF), and radiomics features were extracted from each tissue. Based on the data from the training group, a radiomics signature was built using logistic regression after dimensionality reduction. The radiomics signatures, in combination with the apolipoprotein E4 (APOE4) and baseline neuropsychological scales, were used to build an integrated model using machine learning. The receiver operating characteristics (ROC) curve and data of the test group were used to evaluate the diagnostic accuracy and reliability of the model, respectively. In addition, the clinical prognostic efficacy of the model was evaluated based on the time of progression from MCI to AD. Results: Stepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798. Conclusion: The integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.
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Affiliation(s)
- Zhen-Yu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - De-Wang Mao
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yu-Yun Xu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yuan Shao
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | | | - Xiang-Yang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310014, China
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12
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Xu W, Rao J, Song Y, Chen S, Xue C, Hu G, Lin X, Chen J. Altered Functional Connectivity of the Basal Nucleus of Meynert in Subjective Cognitive Impairment, Early Mild Cognitive Impairment, and Late Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:671351. [PMID: 34248603 PMCID: PMC8267913 DOI: 10.3389/fnagi.2021.671351] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/11/2021] [Indexed: 01/10/2023] Open
Abstract
Background: The spectrum of early Alzheimer's disease (AD) is thought to include subjective cognitive impairment, early mild cognitive impairment (eMCI), and late mild cognitive impairment (lMCI). Choline dysfunction affects the early progression of AD, in which the basal nucleus of Meynert (BNM) is primarily responsible for cortical cholinergic innervation. The aims of this study were to determine the abnormal patterns of BNM-functional connectivity (BNM-FC) in the preclinical AD spectrum (SCD, eMCI, and lMCI) and further explore the relationships between these alterations and neuropsychological measures. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate FC based on a seed mask (BNM mask) in 28 healthy controls (HC), 30 SCD, 24 eMCI, and 25 lMCI patients. Furthermore, the relationship between altered FC and neurocognitive performance was examined by a correlation analysis. The receiver operating characteristic (ROC) curve of abnormal BNM-FC was used to specifically determine the classification ability to differentiate the early AD disease spectrum relative to HC (SCD and HC, eMCI and HC, lMCI and HC) and pairs of groups in the AD disease spectrum (eMCI and SCD, lMCI and SCD, eMCI and lMCI). Results: Compared with HC, SCD patients showed increased FC in the bilateral SMA and decreased FC in the bilateral cerebellum and middle frontal gyrus (MFG), eMCI patients showed significantly decreased FC in the bilateral precuneus, and lMCI individuals showed decreased FC in the right lingual gyrus. Compared with the SCD group, the eMCI group showed decreased FC in the right superior frontal gyrus (SFG), while the lMCI group showed decreased FC in the left middle temporal gyrus (MTG). Compared with the eMCI group, the lMCI group showed decreased FC in the right hippocampus. Interestingly, abnormal FC was associated with certain cognitive domains and functions including episodic memory, executive function, information processing speed, and visuospatial function in the disease groups. BNM-FC of SFG in distinguishing eMCI from SCD; BNM-FC of MTG in distinguishing lMCI from SCD; BNM-FC of the MTG, hippocampus, and cerebellum in distinguishing SCD from HC; and BNM-FC of the hippocampus and MFG in distinguishing eMCI from lMCI have high sensitivity and specificity. Conclusions: The abnormal BNM-FC patterns can characterize the early disease spectrum of AD (SCD, eMCI, and lMCI) and are closely related to the cognitive domains. These new and reliable findings will provide a new perspective in identifying the early disease spectrum of AD and further strengthen the role of cholinergic theory in AD.
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Affiliation(s)
- Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
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13
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Xue C, Sun H, Yue Y, Wang S, Qi W, Hu G, Ge H, Yuan Q, Rao J, Tian L, Xiao C, Chen J. Structural and Functional Disruption of Salience Network in Distinguishing Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. ACS Chem Neurosci 2021; 12:1384-1394. [PMID: 33825444 DOI: 10.1021/acschemneuro.1c00051] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Salience network (SN), playing a vital role in advanced cognitive function, is regarded to be impaired in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The purpose of the study was to explore the importance of structural and functional features of SN in the diagnosis of SCD and aMCI. Structural and resting-state functional magnetic resonance imaging were collected from SCD, aMCI, and healthy control (HC). Cortex thickness, gray matter (GM) volume, spontaneous brain activity, functional connectivity (FC) within SN, and its relationship with cognitive function were analyzed. Moreover, the receiver operating characteristic analysis was performed to assess diagnostic efficacy of altered indictors for SCD and aMCI. Compared to HC, both SCD and aMCI showed decreased GM volume, decreased spontaneous brain activity, and increased FC within SN, while aMCI showed additional decreased cortex thickness. Furthermore, the altered FC in SCD and aMCI was significantly correlated with cognitive function. Particularly, the best-fitting classification models of SCD and aMCI were based on the combined multiple indicators. In conclusion, structure and function of SN were disrupted in SCD and aMCI, which involved in cognitive decline. The combined multiple indicators of SN provided powerful biomarkers for the diagnosis of SCD and aMCI.
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Affiliation(s)
- Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
| | - Haiting Sun
- Department of Pediatrics, Xijing Hospital, the Fourth Milit ary Medical University (Air Force Medical University), Xi’an, Shaanxi 710032, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Siyu Wang
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Honglin Ge
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
| | - Jiang Rao
- Department of Rehabilitation, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Lei Tian
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
| | - Chaoyong Xiao
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
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14
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Wang S, Sun H, Hu G, Xue C, Qi W, Rao J, Zhang F, Zhang X, Chen J. Altered Insular Subregional Connectivity Associated With Cognitions for Distinguishing the Spectrum of Pre-clinical Alzheimer's Disease. Front Aging Neurosci 2021; 13:597455. [PMID: 33643021 PMCID: PMC7902797 DOI: 10.3389/fnagi.2021.597455] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/15/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are regarded as part of the pre-clinical Alzheimer's disease (AD) spectrum. The insular subregional networks are thought to have diverse intrinsic connectivity patterns that are involved in cognitive and emotional processing. We set out to investigate convergent and divergent altered connectivity patterns of the insular subregions across the spectrum of pre-clinical AD and evaluated how well these patterns can differentiate the pre-clinical AD spectrum. Method: Functional connectivity (FC) analyses in insular subnetworks were carried out among 38 patients with SCD, 56 patients with aMCI, and 55 normal controls (CNs). Logistic regression analyses were used to construct models for aMCI and CN, as well as SCD and CN classification. Finally, we conducted correlation analyses to measure the relationship between FCs of altered insular subnetworks and cognition. Results: Patients with SCD presented with reduced FC in the bilateral cerebellum posterior lobe and increased FC in the medial frontal gyrus and the middle temporal gyrus. On the other hand, patients with aMCI largely presented with decreased FC in the bilateral inferior parietal lobule, the cerebellum posterior lobe, and the anterior cingulate cortex, as well as increased FC in the medial and inferior frontal gyrus, and the middle and superior temporal gyrus. Logistic regression analyses indicated that a model composed of FCs among altered insular subnetworks in patients with SCD was able to appropriately classify 83.9% of patients with SCD and CN, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.876, 81.6% sensitivity, and 81.8% specificity. A model consisting of altered insular subnetwork FCs in patients with aMCI was able to appropriately classify 86.5% of the patients with aMCI and CNs, with an AUC of 0.887, 80.4% sensitivity, and 83.6% specificity. Furthermore, some of the FCs among altered insular subnetworks were significantly correlated with episodic memory and executive function. Conclusions: Patients with SCD and aMCI are likely to share similar convergent and divergent altered intrinsic FC patterns of insular subnetworks as the pre-clinical AD spectrum, and presented with abnormalities among subnetworks. Based on these abnormalities, individuals can be correctly differentiated in the pre-clinical AD spectrum. These results suggest that alterations in insular subnetworks can be utilized as a potential biomarker to aid in conducting a clinical diagnosis of the spectrum of pre-clinical AD.
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Affiliation(s)
- Siyu Wang
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Haiting Sun
- Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Guanjie Hu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Fuquan Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangrong Zhang
- Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.,Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Fourth Clinical College of Nanjing Medical University, Nanjing, China
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15
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Chen S, Xu W, Xue C, Hu G, Ma W, Qi W, Dong L, Lin X, Chen J. Voxelwise Meta-Analysis of Gray Matter Abnormalities in Mild Cognitive Impairment and Subjective Cognitive Decline Using Activation Likelihood Estimation. J Alzheimers Dis 2020; 77:1495-1512. [PMID: 32925061 DOI: 10.3233/jad-200659] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Background: Voxel-based morphometry studies have not yielded consistent results among patients with mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Objective: Therefore, we aimed to conduct a meta-analysis of gray matter (GM) abnormalities acquired from these studies to determine their respective neuroanatomical changes. Methods: We systematically searched for voxel-based whole-brain morphometry studies that compared MCI or SCD subjects with healthy controls in PubMed, Web of Science, and EMBASE databases. We used the coordinate-based method of activation likelihood estimation to determine GM changes in SCD, MCI, and MCI sub-groups (amnestic MCI and non-amnestic MCI). Results: A total of 45 studies were included in our meta-analysis. In the MCI group, we found structural atrophy of the bilateral hippocampus, parahippocampal gyrus (PHG), amygdala, right lateral globus pallidus, right insula, and left middle temporal gyrus. The aMCI group exhibited GM atrophy in the bilateral hippocampus, PHG, and amygdala. The naMCI group presented with structural atrophy in the right putamen, right insula, right precentral gyrus, left medial/superior frontal gyrus, and left anterior cingulate. The right lingual gyrus, right cuneus, and left medial frontal gyrus were atrophic GM regions in the SCD group. Conclusion: Our meta-analysis identified unique patterns of neuroanatomical alternations in both the MCI and SCD group. Structural changes in SCD patients provide new evidence for the notion that subtle impairment of visual function, perception, and cognition may be related to early signs of cognitive impairment. In addition, our findings provide a foundation for future targeted interventions at different stages of preclinical Alzheimer’s disease.
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Affiliation(s)
- Shanshan Chen
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenwen Xu
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Guanjie Hu
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenying Ma
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Lin Dong
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xingjian Lin
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiu Chen
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
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16
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Lu X, Chen J, Shu H, Wang Z, Shi Y, Yuan Y, Xie C, Liao W, Su F, Shi Y, Zhang Z. Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model. CNS Neurosci Ther 2020; 26:720-729. [PMID: 32243064 PMCID: PMC7298996 DOI: 10.1111/cns.13371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/29/2020] [Accepted: 03/12/2020] [Indexed: 12/17/2022] Open
Abstract
AIMS Both amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population. METHODS The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period. RESULTS AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2 = .17, P < .001) at baseline in the combined high-risk population (N = 72). CONCLUSIONS The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.
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Affiliation(s)
- Xiang Lu
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Jiu Chen
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
- Institute of NeuropsychiatryThe Affiliated Brain Hospital of Nanjing Medical UniversityNanjingChina
| | - Hao Shu
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Zan Wang
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Yong‐mei Shi
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Yong‐gui Yuan
- Department of Psychosomatics and PsychiatryAffiliated ZhongDa Hospital of Southeast UniversityNanjingChina
| | - Chun‐ming Xie
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Wen‐xiang Liao
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Fan Su
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Ya‐chen Shi
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Zhi‐jun Zhang
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
- Department of PsychologyXinxiang Medical UniversityXinxiangChina
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17
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Liu Y, Hu G, Yu Y, Jiang Z, Yang K, Hu X, Li Z, Liu D, Zou Y, Liu H, Chen J. Structural and Functional Reorganization Within Cognitive Control Network Associated With Protection of Executive Function in Patients With Unilateral Frontal Gliomas. Front Oncol 2020; 10:794. [PMID: 32528887 PMCID: PMC7266965 DOI: 10.3389/fonc.2020.00794] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 04/22/2020] [Indexed: 11/18/2022] Open
Abstract
Background: The cognitive control network (CCN) is widely considered to be a frontoparietal circuit that is involved in executive function. This study aimed to investigate the structural and functional plasticity within the CCN in unilateral frontal gliomas, which are associated with the protection of executive functions. Methods: To detect structural and functional changes within the CCN, we measured gray matter (GM) volume, regional homogeneity, the amplitude of low-frequency fluctuation (ALFF), degree centrality, and functional connectivity within the CCN in 37 patients with gliomas invading the left frontal lobe (n = 16) or the right frontal lobe (n = 21) and 40 healthy controls (CNs). Partial correlation analysis was performed to assess the association between the altered structural and functional indices and executive function. Results: When the tumor invaded the left frontal lobe, the patients showed reduced ALFF in the dorsal medial prefrontal cortex (dmPFC) within the CCN and increased ALFF in the right superior parietal cortex (rSP) within the CCN compared to the CNs. When the tumor invaded the right frontal lobe, the patients showed significantly increased GM volume and ALFF in the left superior parietal cortex (lSP) within the CCN compared to the CNs. Furthermore, the patients showed significantly increased functional connectivities between the lSP and the dmPFC and between the lSP and the rSP within the CCN compared to the CNs. Increased ALFF in the lSP within the CCN was positively correlated with executive function. Conclusions: Tumors invading the frontal lobe induced contralesional structural and functional reorganization within the posterior CCN in patients with unilateral frontal gliomas. This further suggests that the contralesional superior parietal cortex acts as a functional compensation hub within the CCN, which may protect it against the detrimental effects of tumor invasion on executive functions.
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Affiliation(s)
- Yong Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Yu
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Zijuan Jiang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Kun Yang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Xinhua Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Zonghong Li
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Dongming Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yuanjie Zou
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Hongyi Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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18
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Curado M, Escolano F, Lozano MA, Hancock ER. Early Detection of Alzheimer's Disease: Detecting Asymmetries with a Return Random Walk Link Predictor. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E465. [PMID: 33286239 PMCID: PMC7516949 DOI: 10.3390/e22040465] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 04/04/2020] [Accepted: 04/17/2020] [Indexed: 11/23/2022]
Abstract
Alzheimer's disease has been extensively studied using undirected graphs to represent the correlations of BOLD signals in different anatomical regions through functional magnetic resonance imaging (fMRI). However, there has been relatively little analysis of this kind of data using directed graphs, which potentially offer the potential to capture asymmetries in the interactions between different anatomical brain regions. The detection of these asymmetries is relevant to detect the disease in an early stage. For this reason, in this paper, we analyze data extracted from fMRI images using the net4Lap algorithm to infer a directed graph from the available BOLD signals, and then seek to determine asymmetries between the left and right hemispheres of the brain using a directed version of the Return Random Walk (RRW). Experimental evaluation of this method reveals that it leads to the identification of anatomical brain regions known to be implicated in the early development of Alzheimer's disease in clinical studies.
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Affiliation(s)
- Manuel Curado
- Polytechnic School, Catholic University of Murcia, 30107 Murcia, Spain
| | - Francisco Escolano
- Department of Computer Science and AI, University of Alicante, 03690 Alicante, Spain; (F.E.); (M.A.L.)
| | - Miguel A. Lozano
- Department of Computer Science and AI, University of Alicante, 03690 Alicante, Spain; (F.E.); (M.A.L.)
| | - Edwin R. Hancock
- Department of Computer Science, University of York, York YO10 5GH, UK;
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19
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Xue C, Yuan B, Yue Y, Xu J, Wang S, Wu M, Ji N, Zhou X, Zhao Y, Rao J, Yang W, Xiao C, Chen J. Distinct Disruptive Patterns of Default Mode Subnetwork Connectivity Across the Spectrum of Preclinical Alzheimer's Disease. Front Aging Neurosci 2019; 11:307. [PMID: 31798440 PMCID: PMC6863958 DOI: 10.3389/fnagi.2019.00307] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 10/25/2019] [Indexed: 12/28/2022] Open
Abstract
Background: The early progression continuum of Alzheimer’s disease (AD) has been considered to advance through subjective cognitive decline (SCD), non-amnestic mild cognitive impairment (naMCI), and amnestic mild cognitive impairment (aMCI). Altered functional connectivity (FC) in the default mode network (DMN) is regarded as a hallmark of AD. Furthermore, the DMN can be divided into two subnetworks, the anterior and posterior subnetworks. However, little is known about distinct disruptive patterns in the subsystems of the DMN across the preclinical AD spectrum. This study investigated the connectivity patterns of anterior DMN (aDMN) and posterior DMN (pDMN) across the preclinical AD spectrum. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in the DMN subnetworks in 20 healthy controls (HC), eight SCD, 11 naMCI, and 28 aMCI patients. Moreover, a correlation analysis was used to examine associations between the altered connectivity of the DMN subnetworks and the neurocognitive performance. Results: Compared to the HC, SCD patients showed increased FC in the bilateral superior frontal gyrus (SFG), naMCI patients showed increased FC in the left inferior parietal lobule (IPL), and aMCI patients showed increased FC in the bilateral IPL in the aDMN; while SCD patients showed decreased FC in the precuneus, naMCI patients showed increased FC in the left middle temporal gyrus (MTG), and aMCI patients also showed increased FC in the right middle frontal gyrus (MFG) in the pDMN. Notably, the FC between the ventromedial prefrontal cortex (vmPFC) and the left MFG and the IPL in the aDMN was associated with episodic memory in the SCD and aMCI groups. Interestingly, the FC between the posterior cingulated cortex (PCC) and several regions in the pDMN was associated with other cognitive functions in the SCD and naMCI groups. Conclusions: This study demonstrates that the three preclinical stages of AD exhibit distinct FC alternations in the DMN subnetworks. Furthermore, the patient group data showed that the altered FC involves cognitive function. These findings can provide novel insights for tailored clinical intervention across the preclinical AD spectrum.
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Affiliation(s)
- Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Baoyu Yuan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jiani Xu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Siyu Wang
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Meilin Wu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Nanxi Ji
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Xingzhi Zhou
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Yilin Zhao
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenjie Yang
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
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