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Stam CJ. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw Neurosci 2024; 8:1-23. [PMID: 38562292 PMCID: PMC10861166 DOI: 10.1162/netn_a_00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.
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Affiliation(s)
- Cornelis Jan Stam
- Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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2
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Punzi M, Sestieri C, Picerni E, Chiarelli AM, Padulo C, Delli Pizzi A, Tullo MG, Tosoni A, Granzotto A, Della Penna S, Onofrj M, Ferretti A, Delli Pizzi S, Sensi SL. Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease. Heliyon 2024; 10:e27429. [PMID: 38509925 PMCID: PMC10951508 DOI: 10.1016/j.heliyon.2024.e27429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.
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Affiliation(s)
- Miriam Punzi
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Eleonora Picerni
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Caterina Padulo
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Department of Humanities, University of Naples Federico II, Naples, 80133, Italy
| | - Andrea Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Maria Giulia Tullo
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Annalisa Tosoni
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Alberto Granzotto
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Antonio Ferretti
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- UdA-TechLab, Research Center, University “G. D’Annunzio” of Chieti-Pescara, 66100, Chieti, Italy
| | - Stefano Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Stefano L. Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
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Huang L, Li Q, Lu Y, Pan F, Cui L, Wang Y, Miao Y, Chen T, Li Y, Wu J, Chen X, Jia J, Guo Q. Consensus on rapid screening for prodromal Alzheimer's disease in China. Gen Psychiatr 2024; 37:e101310. [PMID: 38313393 PMCID: PMC10836380 DOI: 10.1136/gpsych-2023-101310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/19/2023] [Indexed: 02/06/2024] Open
Abstract
Alzheimer's disease (AD) is a common cause of dementia, characterised by cerebral amyloid-β deposition, pathological tau and neurodegeneration. The prodromal stage of AD (pAD) refers to patients with mild cognitive impairment (MCI) and evidence of AD's pathology. At this stage, disease-modifying interventions should be used to prevent the progression to dementia. Given the inherent heterogeneity of MCI, more specific biomarkers are needed to elucidate the underlying AD's pathology. Although the uses of cerebrospinal fluid and positron emission tomography are widely accepted methods for detecting AD's pathology, their clinical applications are limited by their high costs and invasiveness, particularly in low-income areas in China. Therefore, to improve the early detection of Alzheimer's disease (AD) pathology through cost-effective screening methods, a panel of 45 neurologists, psychiatrists and gerontologists was invited to establish a formal consensus on the screening of pAD in China. The supportive evidence and grades of recommendations are based on a systematic literature review and focus group discussion. National meetings were held to allow participants to review, vote and provide their expert opinions to reach a consensus. A majority (two-thirds) decision was used for questions for which consensus could not be reached. Recommended screening methods are presented in this publication, including neuropsychological assessment, peripheral biomarkers and brain imaging. In addition, a general workflow for screening pAD in China is established, which will help clinicians identify individuals at high risk and determine therapeutic targets.
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Affiliation(s)
- Lin Huang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinjie Li
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yao Lu
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fengfeng Pan
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Wang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ya Miao
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianlu Chen
- Center for Translational Medicine and Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yatian Li
- Shanghai BestCovered, Shanghai, China
| | | | - Xiaochun Chen
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianping Jia
- Department of Neurology, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Chen Z, Chen K, Li Y, Geng D, Li X, Liang X, Lu H, Ding S, Xiao Z, Ma X, Zheng L, Ding D, Zhao Q, Yang L. Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI. Hum Brain Mapp 2024; 45:e26529. [PMID: 37991144 PMCID: PMC10789213 DOI: 10.1002/hbm.26529] [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: 09/30/2022] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023] Open
Abstract
Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.
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Affiliation(s)
- Zhihan Chen
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
| | - Keliang Chen
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Yuxin Li
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Daoying Geng
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Xiantao Li
- Department of Critical Care MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Xiaoniu Liang
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Huimeng Lu
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Saineng Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Zhenxu Xiao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Xiaoxi Ma
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Li Zheng
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Ding Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Qianhua Zhao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
- MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Liqin Yang
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
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5
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Therrien S, Anthony M, Turnbull A, Lin FV. Risk-Taking Behavior Differs Between Older Adults with and without Mild Cognitive Impairment. J Alzheimers Dis 2024; 100:1227-1235. [PMID: 39031355 DOI: 10.3233/jad-231448] [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] [Indexed: 07/22/2024]
Abstract
Background Adequately evaluating risk and making decisions is vital but understudied for older adults living independently but with compromised cognition, as seen in those with mild cognitive impairment (MCI), specifically those with amnestic MCI (aMCI) which is associated with higher risk of conversion to Alzheimer's disease. Objective We propose to comprehensively evaluate risk-taking behaviors across domains important for everyday activities between an aMCI group and their cognitively healthy counterparts (HC). Methods A case-control study design. Data on risk-taking behaviors via the Domain-Specific Risk-Taking Scale (DOSPERT), and candidate confounding mental health factors (i.e., neurodegeneration, depression, and fatigue) were collected. Analyses on group difference and interaction between group and confounding factors on risk-taking behaviors were conducted. Results The aMCI group showed a higher likelihood of risk-taking than HC (t = 4.38, df = 73, p < 0.001). Moderation analysis showed fatigue (F = 5.91, p = 0.018) and presence of depression (F = 4.52, p = 0.037), but not neurodegeneration, as significant moderators for group and DOSPERT total score, controlling for sex. In post-hoc analyses, there was a significant relationship between both fatigue (B = -7.83, SE = 3.65, t = -2.14, p = 0.036), and presence of depression (B = -20.80, SE = 9.97, t = -2.09, p = 0.041), with DOSPERT total score for HC but not for aMCI. There were no significant relationships between neurodegeneration, fatigue, or depression with any specific risk-taking domains after correction for multiple comparisons. Conclusions Our results show differences in risk-taking behavior between older adults with and without intact cognition, and overall decision-making is affected by fatigue and depression in HC but not aMCI, together suggesting the importance of cognition in the ability to adjust risk-taking behaviors.
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Affiliation(s)
- Sarah Therrien
- Department of Psychiatry and Behavioral Sciences, CogT Lab, Stanford University, Palo Alto, CA, USA
| | - Mia Anthony
- Department of Psychiatry and Behavioral Sciences, CogT Lab, Stanford University, Palo Alto, CA, USA
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
| | - Adam Turnbull
- Department of Psychiatry and Behavioral Sciences, CogT Lab, Stanford University, Palo Alto, CA, USA
| | - F Vankee Lin
- Department of Psychiatry and Behavioral Sciences, CogT Lab, Stanford University, Palo Alto, CA, USA
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Ren X, Dong B, Luan Y, Wu Y, Huang Y. Alterations via inter-regional connective relationships in Alzheimer's disease. Front Hum Neurosci 2023; 17:1276994. [PMID: 38021241 PMCID: PMC10672243 DOI: 10.3389/fnhum.2023.1276994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.
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Affiliation(s)
- Xiaomei Ren
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Bowen Dong
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Ying Luan
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Yunzhi Huang
- Institute for AI in Medicine, School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing, China
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Padulo C, Sestieri C, Punzi M, Picerni E, Chiacchiaretta P, Tullo MG, Granzotto A, Baldassarre A, Onofrj M, Ferretti A, Delli Pizzi S, Sensi SL. Atrophy of specific amygdala subfields in subjects converting to mild cognitive impairment. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2023; 9:e12436. [PMID: 38053753 PMCID: PMC10694338 DOI: 10.1002/trc2.12436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 12/07/2023]
Abstract
Introduction Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI). Methods Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow-up: 75 who remained stable (s-HC) and 22 who converted to MCI within 48 months (c-HC). Anatomical magnetic resonance (MR) images were analyzed using a semi-automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD-related pathology, and the whole cortical thickness as a test of spatial specificity. Results Compared with s-HC individuals, c-HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro-structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48-month follow-up), suggesting that amygdala changes shape the cognitive progression to MCI. Discussion Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD. Highlights Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI).Amygdala's was observed within the basolateral and amygdaloid complexes.Macro-structural alterations were associated with cognitive decline.No atrophy was found in the hippocampus and cortex.
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Affiliation(s)
- Caterina Padulo
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Department of HumanitiesUniversity of Naples Federico IINaplesItaly
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)“G. d'Annunzio” University, Chieti‐PescaraChietiItaly
| | - Miriam Punzi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Eleonora Picerni
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Piero Chiacchiaretta
- Department of Innovative Technologies in Medicine and Dentistry“G. d'Annunzio” University of Chieti‐Pescara, ChietiChietiItaly
- Advanced Computing CoreCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Maria Giulia Tullo
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Alberto Granzotto
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Antonio Ferretti
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Stefano Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Stefano L. Sensi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)“G. d'Annunzio” University, Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
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Fu X, Qin M, Liu X, Cheng L, Zhang L, Zhang X, Lei Y, Zhou Q, Sun P, Lin L, Su Y, Wang J. Decreased GABA levels of the anterior and posterior cingulate cortex are associated with executive dysfunction in mild cognitive impairment. Front Neurosci 2023; 17:1220122. [PMID: 37638325 PMCID: PMC10450953 DOI: 10.3389/fnins.2023.1220122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023] Open
Abstract
Background and purpose Executive function impairment, a slight but noticeable cognitive deficit in mild cognitive impairment (MCI) patients, is influenced by gamma-aminobutyric acid (GABA) levels. Reduced cognitive function is accompanied by thinning of the cerebral cortex, which has higher GABA levels than white matter. However, the relationships among GABA levels, cortical thickness, and executive function in MCI patients have not yet been elucidated. We investigated the relationships among GABA levels, cortical thickness, and executive function in MCI patients. Methods In this study, a total of 36 MCI patients and 36 sex-, age-, and education-matched healthy controls (HC) were recruited. But 33 MCI patients and 35 HC were included because of head motion or poor data quality for three MCI patients and one HC. The levels of gamma-aminobutyric acid plus relative to creatine (GABA+/Cr) and glutamate-glutamine relative to creatine (Glx/Cr) in the anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC) were measured using the Meshcher-Garwood point resolved spectroscopy (MEGA-PRESS) sequence. Metabolite ratios, cortical thickness, and executive function and their interrelationships were determined in the MCI and HC groups. Results Patients with MCI showed lower GABA+/Cr levels in the ACC and PCC. Combined levels of GABA+ and Glx in the ACC and GABA+ in the PCC showed good diagnostic efficacy for MCI (AUC: 0.82). But no differences in cortical thickness were found between the two groups. In the MCI group, lower GABA+/Cr level was correlated to worse performance on the digit span test backward, and the shape trail test-B. The cortical thickness was not associated with GABA+ levels and executive function in patients. Conclusion These results implied that decreased GABA levels in the ACC and PCC had a critical role in the early diagnosis of impaired executive function of MCI. Therefore, GABA in the ACC and PCC could be a potential diagnostic marker of the executive function decline of MCI.
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Affiliation(s)
- Xiaona Fu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Mengting Qin
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Lan Cheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Lan Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xinli Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yu Lei
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Qidong Zhou
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Sun
- Clinical & Technical Solutions, Philips Healthcare, Beijing, China
| | - Liangjie Lin
- Clinical & Technical Solutions, Philips Healthcare, Beijing, China
| | - Ying Su
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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9
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Franciotti R, Nardini D, Russo M, Onofrj M, Sensi SL. Comparison of Machine Learning-based Approaches to Predict the Conversion to Alzheimer's Disease from Mild Cognitive Impairment. Neuroscience 2023; 514:143-152. [PMID: 36736612 DOI: 10.1016/j.neuroscience.2023.01.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/05/2023]
Abstract
In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer's Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms -when applied to different biomarkers and other conditions- is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed -with the voting procedure- to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms' performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.
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Affiliation(s)
- Raffaella Franciotti
- Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy.
| | | | - Mirella Russo
- Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy; Center for Advanced Studies and Technology - CAST, G. d'Annunzio University of Chieti-Pescara, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy; Center for Advanced Studies and Technology - CAST, G. d'Annunzio University of Chieti-Pescara, Italy
| | - Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy; Center for Advanced Studies and Technology - CAST, G. d'Annunzio University of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Italy.
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10
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Altered structural and functional homotopic connectivity associated with the progression from mild cognitive impairment to Alzheimer's disease. Psychiatry Res 2023; 319:115000. [PMID: 36502711 DOI: 10.1016/j.psychres.2022.115000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/28/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022]
Abstract
The progressive mild cognitive impairment (pMCI) is associated with an increased risk of Alzheimer's disease (AD). Many studies have reported the disrupted brain alteration during the imminent conversion from pMCI to AD. However, the subtle difference of structural and functional of inter-hemispheric between pMCI and stable mild cognitive impairment (sMCI) remains unknown. In the present study, we scanned the multimodal magnetic resonance imaging of 38 sMCI, 26 pMCI, and 50 healthy controls (HC) and assessed the cognitive function. The voxel-mirrored homotopic connectivity (VMHC) and volume of corpus callosum were calculated. A structural equation modeling (SEM) was established to determine the relationships between the corpus callosum, the inter-hemispheric connectivity, and cognitive assessment. Compared to sMCI, pMCI exhibited decreased VMHC in insular and thalamus, and reduced volume of corpus callosum. SEM results showed that decreased inter-hemispheric connectivity was directly associated with cognitive impairment and corpus callosum atrophy, and corpus callosum atrophy indirectly caused cognitive impairment by mediating inter-hemispheric connectivity in pMCI. In conclusion, the destruction of homotopic connectivity is related to cognitive impairment, and the corpus callosum atrophy partially mediates the association between the homotopic connectivity and cognitive impairment in pMCI.
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11
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Effects of apolipoprotein E4 genotype on cerebro-cerebellar connectivity, brain atrophy, and cognition in patients with Alzheimer's disease. J Neurol Sci 2022; 442:120435. [PMID: 36201963 DOI: 10.1016/j.jns.2022.120435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 08/28/2022] [Accepted: 09/18/2022] [Indexed: 11/20/2022]
Abstract
INTRODUCTION While several studies have substantially revealed the influence of the apolipoprotein E4 genotype (APOE4) on the vulnerability of Alzheimer's disease (AD), there are still far fewer studies investigating whether and how APOE4, in the absence of the amyloid-β (Aβ), alters regional brain atrophy, cerebro-cerebellar connectivity and cognitive performance in AD patients. METHODS We employed MRI and neuropsychological data from 234 old adults with AD dementia, including 143 APOE4-positive (with ε2/ε4, ε3/ε4, or ε4/ε4 alleles) and 91 APOE4-negative (with ε2/ε2, ε2/ε3 or ε3/ε3), to investigate the cerebro-cerebellar connectivity in three cerebro-cerebellar brain networks: default mode network, motor network and affective-limbic network. Amyloid PET images were used to evaluate individual Aβ burdens, later used as covariates. Regional volumetric and cortical thickness measures were quantified in both the cerebellum and the cerebrum using the cerebellum segmentation algorithm and Freesurfer5.3, respectively. RESULTS Our corrected functional connectivity (FC) results showed that APOE4 carriers (APOE4+) had lower FC within the cerebro-cerebellar motor network. In addition, significant group differences in regional cortical thickness were observed in the left Crus I, the right VIIB, left superior frontal, and right middle temporal gyri. Group differences in regional brain volumes were observed in the left lobule V and right parstriangularis. Furthermore, multiple linear regression analysis indicated that APOE4+ AD patients show greater episodic memory impairment. CONCLUSION Since amyloid-β, age, education, and gender were included as confounds in the statistical models, our findings suggest that APOE4 independently contributes to brain atrophy, disrupted FC, and associated memory declines in AD patients.
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12
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Hsu CL, Falck RS, Backhouse D, Chan P, Dao E, Ten Brinke LF, Manor B, Liu-Ambrose T. Objective Sleep Quality and the Underlying Functional Neural Correlates Among Older Adults with Possible Mild Cognitive Impairment. J Alzheimers Dis 2022; 89:1473-1482. [PMID: 36057822 DOI: 10.3233/jad-220457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Poor sleep quality is common among older individuals with mild cognitive impairment (MCI) and may be a consequence of functional alterations in the brain; yet few studies have investigated the underlying neural correlates of actigraphy-measured sleep quality in this cohort. OBJECTIVE The objective of this study was to examine the relationship between brain networks and sleep quality measured by actigraphy. METHODS In this cross-sectional analysis, sleep efficiency and sleep fragmentation were estimated using Motionwatch8 (MW8) over a period of 14 days in 36 community-dwelling older adults with possible MCI aged 65-85 years. All 36 participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Independent associations between network connectivity and MW8 measures of sleep quality were determined using general linear modeling via FSL. Networks examined included the somatosensory network (SMN), frontoparietal network (FPN), and default mode network (DMN). RESULTS Across the 36 participants (mean age 71.8 years; SD = 5.2 years), mean Montreal Cognitive Assessment score was 22.5 (SD = 2.7) and Mini-Mental State Examination score was 28.3 (SD = 1.5). Mean sleep efficiency and fragmentation index was 80.1% (SD = 10.0) and 31.8 (SD = 10.4) respectively. Higher sleep fragmentation was significantly correlated with increased connectivity between the SMN and insula, the SMN and posterior cingulate, as well as FPN and primary motor area (FDR-corrected, p < 0.004). CONCLUSION Functional connectivity between brain regions involved in attentional and somatosensory processes may be associated with disrupted sleep in older adults with MCI.
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Affiliation(s)
- Chun Liang Hsu
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Roslindale, MA, USA.,Harvard Medical School, Harvard University, Boston, MA, USA.,Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Ryan S Falck
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Daniel Backhouse
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Patrick Chan
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Elizabeth Dao
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Lisanne F Ten Brinke
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Roslindale, MA, USA.,Harvard Medical School, Harvard University, Boston, MA, USA
| | - Teresa Liu-Ambrose
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
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13
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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: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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14
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Zhao C, Huang WJ, Feng F, Zhou B, Yao HX, Guo YE, Wang P, Wang LN, Shu N, Zhang X. Abnormal characterization of dynamic functional connectivity in Alzheimer's disease. Neural Regen Res 2022; 17:2014-2021. [PMID: 35142691 PMCID: PMC8848607 DOI: 10.4103/1673-5374.332161] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer's disease (AD) or amnestic mild cognitive impairment (aMCI). However, most studies examined traditional resting state functional connections, ignoring the instantaneous connection mode of the whole brain. In this case-control study, we used a new method called dynamic functional connectivity (DFC) to look for abnormalities in patients with AD and aMCI. We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant, and then used a support vector machine to classify AD patients and normal controls. Finally, we highlighted brain regions and brain networks that made the largest contributions to the classification. We found differences in dynamic function connectivity strength in the left precuneus, default mode network, and dorsal attention network among normal controls, aMCI patients, and AD patients. These abnormalities are potential imaging markers for the early diagnosis of AD.
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Affiliation(s)
- Cui Zhao
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing; Department of Geriatrics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei Province, China
| | - Wei-Jie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning; Center for Collaboration and Innovation in Brain and Learning Sciences; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Feng Feng
- Department of Neurology, First Medical Center, Chinese PLA General Hospital; Department of Neurology, PLA Rocket Force Characteristic Medical Center, Beijing, China
| | - Bo Zhou
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Hong-Xiang Yao
- Department of Radiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yan-E Guo
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Lu-Ning Wang
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning; Center for Collaboration and Innovation in Brain and Learning Sciences; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
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15
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Um YH, Wang SM, Kang DW, Kim NY, Lim HK. Subcortical and Cerebellar Neural Correlates of Prodromal Alzheimer’s Disease with Prolonged Sleep Latency. J Alzheimers Dis 2022; 86:565-578. [PMID: 35068468 PMCID: PMC9028620 DOI: 10.3233/jad-215460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background: Despite the important associations among sleep, Alzheimer’s disease (AD), subcortical structures, and the cerebellum, structural and functional magnetic resonance imaging (MRI) with regard to these regions and sleep on patients in AD trajectory are scarce. Objective: This study aimed to evaluate the influence of prolonged sleep latency on the structural and functional alterations in the subcortical and cerebellar neural correlates in amyloid-β positive amnestic mild cognitive impairment patients (Aβ+aMCI). Methods: A total of 60 patients with aMCI who were identified as amyloid positive ([18F] flutemetamol+) were recruited in the study, 24 patients with normal sleep latency (aMCI-n) and 36 patients prolonged sleep latency (aMCI-p). Cortical thickness and volumes between the two groups were compared. Volumetric analyses were implemented on the brainstem, thalamus, and hippocampus. Subcortical and cerebellar resting state functional connectivity (FC) differences were measured between the both groups through seed-to-voxel analysis. Additionally, group x Aβ interactive effects on FC values were tested with a general linear model. Result: There was a significantly decreased brainstem volume in aMCI-p subjects. We observed a significant reduction of the locus coeruleus (LC) FC with frontal, temporal, insular cortices, hippocampus, and left thalamic FC with occipital cortex. Moreover, the LC FC with occipital cortex and left hippocampal FC with frontal cortex were increased in aMCI-p subjects. In addition, there was a statistically significant group by regional standardized uptake value ratio interactions discovered in cerebro-cerebellar networks. Conclusion: The aforementioned findings suggest that prolonged sleep latency may be a detrimental factor in compromising structural and functional correlates of subcortical structures and the cerebellum, which may accelerate AD pathophysiology.
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Affiliation(s)
- Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Nak-Young Kim
- Department of Psychiatry, Keyo Hospital, Keyo Medical Foundation, Uiwang, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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16
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Massetti N, Russo M, Franciotti R, Nardini D, Mandolini G, Granzotto A, Bomba M, Delli Pizzi S, Mosca A, Scherer R, Onofrj M, Sensi SL. A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum. J Alzheimers Dis 2021; 85:1639-1655. [PMID: 34958014 DOI: 10.3233/jad-210573] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD. OBJECTIVE To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion. METHODS We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables. RESULTS The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects. CONCLUSION Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.
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Affiliation(s)
- Noemi Massetti
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Mirella Russo
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Raffaella Franciotti
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | | | | | - Alberto Granzotto
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy.,Sue and Bill Gross Stem Cell Research Center, University of California - Irvine, Irvine, CA, USA
| | - Manuela Bomba
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Stefano Delli Pizzi
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Alessandra Mosca
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Reinhold Scherer
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Marco Onofrj
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Stefano L Sensi
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy.,Institute for Mind Impairments and Neurological Disorders - iMIND, University of California - Irvine, Irvine, CA, USA
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17
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Mondragón JD, Marapin R, De Deyn PP, Maurits N. Short- and Long-Term Functional Connectivity Differences Associated with Alzheimer's Disease Progression. Dement Geriatr Cogn Dis Extra 2021; 11:235-249. [PMID: 34721501 PMCID: PMC8543355 DOI: 10.1159/000518233] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 06/30/2021] [Indexed: 01/27/2023] Open
Abstract
Introduction Progression of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) is a clinical event with highly variable progression rates varying from 10–15% up to 30–34%. Functional connectivity (FC), the temporal similarity between spatially remote neurophysiological events, has previously been reported to differ between aMCI patients who progress to AD (pMCI) and those who do not (i.e., remain stable; sMCI). However, these reports had a short-term follow-up and do not provide insight into long-term AD progression. Methods Seventy-nine participants with a baseline and 78 with a 12-month, 51 with a 24-month, and 22 with a +48-month follow-up resting-state fMRI with aMCI diagnosis from the Alzheimer's Disease Neuroimaging Initiative database were included. FC was assessed using the CONN toolbox. Local correlation and group independent component analysis were utilized to compare regional functional coupling and between-network FC, respectively, between sMCI and pMCI groups. Two-sample t tests were used to test for statistically significant differences between groups, and paired t-tests were used to assess cognitive changes over time. Results All participants (i.e., 66 sMCI and 19 pMCI) had a baseline and a year follow-up fMRI scan. Progression from aMCI to AD occurred in 19 patients (10 at 12 months, 5 at 24 months, and 4 at >48 months), while 73 MCI patients remained cognitively stable (sMCI). The pMCI and sMCI cognitive profiles were different. More between-network FC than regional functional coupling differences were present between sMCI and pMCI patients. Activation in the salience network (SN) and the default mode network (DMN) was consistently different between sMCI and pMCI patients across time. Discussion sMCI and pMCI patients have different cognitive and FC profiles. Only pMCI patients showed cognitive differences across time. The DMN and SN showed local correlation and between-network FC differences between the sMCI and pMCI patient groups at multiple moments in time.
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Affiliation(s)
- Jaime D Mondragón
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Alzheimer Center Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ramesh Marapin
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Peter Paul De Deyn
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Alzheimer Center Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Natasha Maurits
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Alzheimer Center Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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18
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Chen S, Song Y, Xu W, Hu G, Ge H, Xue C, Gao J, Qi W, Lin X, Chen J. Impaired Memory Awareness and Loss Integration in Self-Referential Network Across the Progression of Alzheimer's Disease Spectrum. J Alzheimers Dis 2021; 83:111-126. [PMID: 34250942 DOI: 10.3233/jad-210541] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
BACKGROUND Anosognosia, or unawareness of memory deficits, is a common manifestation of Alzheimer's disease (AD), but greatly variable in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) subjects. Self-referential network (SRN) is responsible for self-referential processing and considered to be related to AD progression. OBJECTIVE Our aim is to explore connectivity changes of SRN and its interaction with memory-related network and primary sensorimotor network (SMN) in the AD spectrum. METHODS About 444 Alzheimer's Disease Neuroimaging Initiative subjects (86 cognitively normal [CN]; 156 SCD; 146 aMCI; 56 AD) were enrolled in our study. The independent component analysis (ICA) method was used to extract the SRN, SMN, and memory-related network from all subjects. The alteration of functional connectivity (FC) within SRN and its connectivity with memory-related network/SMN were compared among four groups and further correlation analysis between altered FC and memory awareness index as well as episodic memory score were performed. RESULTS Compared with CN group, individuals with SCD exhibited hyperconnectivity within SRN, while aMCI and AD patients showed hypoconnectivity. Furthermore, aMCI patients and AD patients both showed the interruption of the FC between the SRN and memory-related network compared to CN group. Pearson correlation analysis showed that disruptive FC within SRN and its interaction with memory-related network were related to memory awareness index and episodic memory scores. CONCLUSION In conclusion, impaired memory awareness and episodic memory in the AD spectrum are correlated to the disconnection within SRN and its interaction with memory-related network.
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Affiliation(s)
- Shanshan Chen
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yu Song
- 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
| | - 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
| | - Honglin Ge
- 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
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ju Gao
- Department of Geriatric Psychiatry, 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
| | - 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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19
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Tang F, Zhu D, Ma W, Yao Q, Li Q, Shi J. Differences Changes in Cerebellar Functional Connectivity Between Mild Cognitive Impairment and Alzheimer's Disease: A Seed-Based Approach. Front Neurol 2021; 12:645171. [PMID: 34220669 PMCID: PMC8248670 DOI: 10.3389/fneur.2021.645171] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Recent studies have discovered that functional connections are impaired among patients with Alzheimer's disease (AD), even at the preclinical stage. The cerebellum has been implicated as playing a role in cognitive processes. However, functional connectivity (FC) among cognitive sub-regions of the cerebellum in patients with AD and mild cognitive impairment (MCI) remains to be further elucidated. Objective: Our study aims to investigate the FC changes of the cerebellum among patients with AD and MCI, compared to healthy controls (HC). Additionally, we explored the role of cerebellum FC changes in the cognitive performance of all subjects. Materials: Resting-state functional magnetic resonance imaging (rs-fMRI) data from three different groups (28 AD patients, 26 MCI patients, and 30 HC) was collected. We defined cerebellar crus II and lobule IX as seed regions to assess the intragroup differences of cortico-cerebellar connectivity. Bias correlational analysis was performed to investigate the relationship between changes in FC and neuropsychological performance. Results: Compared to HC, AD patients had decreased FC within the caudate, limbic lobe, medial frontal gyrus (MFG), middle temporal gyrus, superior frontal gyrus, parietal lobe/precuneus, inferior temporal gyrus, and posterior cingulate gyrus. Interestingly, MCI patients demonstrated increased FC within inferior parietal lobe, and MFG, while they had decreased FC in the thalamus, inferior frontal gyrus, and superior frontal gyrus. Further analysis indicated that FC changes between the left crus II and the right thalamus, as well as between left lobule IX and the right parietal lobe, were both associated with cognitive decline in AD. Disrupted FC between left crus II and right thalamus, as well as between left lobule IX and right parietal lobe, was associated with attention deficit among subjects with MCI. Conclusion: These findings indicate that cortico-cerebellar FC in MCI and AD patients was significantly disrupted with different distributions, particularly in the default mode networks (DMN) and fronto-parietal networks (FPN) region. Increased activity within the fronto-parietal areas of MCI patients indicated a possible compensatory role for the cerebellum in cognitive impairment. Therefore, alterations in the cortico-cerebellar FC represent a novel approach for early diagnosis and a potential therapeutic target for early intervention.
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Affiliation(s)
- Fanyu Tang
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Donglin Zhu
- Department of Neurology, Affiliated to Nanjing Medical University, Nanjing, China
| | - Wenying Ma
- Nanjing Medical University, Nanjing, China
| | - Qun Yao
- Department of Neurology, Affiliated to Nanjing Medical University, Nanjing, China
| | - Qian Li
- Nanjing Medical University, Nanjing, China
| | - Jingping Shi
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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20
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Yao W, Chen H, Luo C, Sheng X, Zhao H, Xu Y, Bai F. Hyperconnectivity of Self-Referential Network as a Predictive Biomarker of the Progression of Alzheimer's Disease. J Alzheimers Dis 2021; 80:577-590. [PMID: 33579849 DOI: 10.3233/jad-201376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Self-referential processing is associated with the progression of Alzheimer's disease (AD), and cerebrospinal fluid (CSF) proteins have become accepted biomarkers of AD. OBJECTIVE Our objective in this study was to focus on the relationships between the self-referential network (SRN) and CSF pathology in AD-spectrum patients. METHODS A total of 80 participants, including 20 cognitively normal, 20 early mild cognitive impairment (EMCI), 20 late MCI (LMCI), and 20 AD, were recruited for this study. Independent component analysis was used to explore the topological SRN patterns, and the abnormalities of this network were identified at different stages of AD. Finally, CSF pathological characteristics (i.e., CSF Aβ, t-tau, and p-tau) that affected the abnormalities of the SRN were further determined during the progression of AD. RESULTS Compared to cognitively normal subjects, AD-spectrum patients (i.e., EMCI, LMCI, and AD) showed a reversing trend toward an association between CSF pathological markers and the abnormal SRN occurring during the progression of AD. However, a certain disease state (i.e., the present LMCI) with a low concentration of CSF tau could evoke more hyperconnectivity of the SRN than other patients with progressively increasing concentrations of CSF tau (i.e., EMCI and AD), and this fluctuation of CSF tau was more sensitive to the hyperconnectivity of the SRN than the dynamic changes of CSF Aβ. CONCLUSION The integrity of the SRN was closely associated with CSF pathological characteristics, and these findings support the view that the hyperconnectivity of the SRN will play an important role in monitoring the progression of the pre-dementia state to AD.
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Affiliation(s)
- Weina Yao
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Caimei Luo
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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21
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Chen Q, Lu J, Zhang X, Sun Y, Chen W, Li X, Zhang W, Qing Z, Zhang B. Alterations in Dynamic Functional Connectivity in Individuals With Subjective Cognitive Decline. Front Aging Neurosci 2021; 13:646017. [PMID: 33613274 PMCID: PMC7886811 DOI: 10.3389/fnagi.2021.646017] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 01/06/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: To investigate the dynamic functional connectivity (DFC) and static parameters of graph theory in individuals with subjective cognitive decline (SCD) and the associations of DFC and topological properties with cognitive performance. Methods: Thirty-three control subjects and 32 SCD individuals were enrolled in this study, and neuropsychological evaluations and resting-state functional magnetic resonance imaging scanning were performed. Thirty-three components were selected by group independent component analysis to construct 7 functional networks. Based on the sliding window approach and k-means clustering, distinct DFC states were identified. We calculated the temporal properties of fractional windows in each state, the mean dwell time in each state, and the number of transitions between each pair of DFC states. The global and local static parameters were assessed by graph theory analysis. The differences in DFC and topological metrics, and the associations of the altered neuroimaging measures with cognitive performance were assessed. Results: The whole cohort demonstrated 4 distinct connectivity states. Compared to the control group, the SCD group showed increased fractional windows and an increased mean dwell time in state 4, characterized by hypoconnectivity both within and between networks. The SCD group also showed decreased fractional windows and a decreased mean dwell time in state 2, dominated by hyperconnectivity within and between the auditory, visual and somatomotor networks. The number of transitions between state 1 and state 2, between state 2 and state 3, and between state 2 and state 4 was significantly reduced in the SCD group compared to the control group. No significant differences in global or local topological metrics were observed. The altered DFC properties showed significant correlations with cognitive performance. Conclusion: Our findings indicated DFC network reconfiguration in the SCD stage, which may underlie the early cognitive decline in SCD subjects and serve as sensitive neuroimaging biomarkers for the preclinical detection of individuals with incipient Alzheimer's disease.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China.,Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yi Sun
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wenqian Chen
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xin Li
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
| | - Wen Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zhao Qing
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China.,Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
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22
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Delli Pizzi S, Granzotto A, Bomba M, Frazzini V, Onofrj M, Sensi SL. Acting Before; A Combined Strategy to Counteract the Onset and Progression of Dementia. Curr Alzheimer Res 2020; 17:790-804. [PMID: 33272186 DOI: 10.2174/1567205017666201203085524] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 09/10/2020] [Accepted: 10/16/2020] [Indexed: 11/22/2022]
Abstract
Brain aging and aging-related neurodegenerative disorders are posing a significant challenge for health systems worldwide. To date, most of the therapeutic efforts aimed at counteracting dementiarelated behavioral and cognitive impairment have been focused on addressing putative determinants of the disease, such as β-amyloid or tau. In contrast, relatively little attention has been paid to pharmacological interventions aimed at restoring or promoting the synaptic plasticity of the aging brain. The review will explore and discuss the most recent molecular, structural/functional, and behavioral evidence that supports the use of non-pharmacological approaches as well as cognitive-enhancing drugs to counteract brain aging and early-stage dementia.
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Affiliation(s)
- Stefano Delli Pizzi
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
| | - Alberto Granzotto
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
| | - Manuela Bomba
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
| | - Valerio Frazzini
- AP-HP, Epilepsy Unit, Pitie-Salpetriere Hospital and Brain and Spine Institute (INSERM UMRS1127, CNRS UMR7225, Sorbonne Universite), Pitie-Salpetriere Hospital, Paris, France
| | - Marco Onofrj
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
| | - Stefano L Sensi
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
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23
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López ME, Turrero A, Cuesta P, Rodríguez-Rojo IC, Barabash A, Marcos A, Maestú F, Fernández A. A multivariate model of time to conversion from mild cognitive impairment to Alzheimer's disease. GeroScience 2020; 42:1715-1732. [PMID: 32886293 PMCID: PMC7732920 DOI: 10.1007/s11357-020-00260-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 08/24/2020] [Indexed: 11/26/2022] Open
Abstract
The present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological, and neuroanatomical factors may predict differences in time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). To this end, a sample of 121 MCIs was followed up during a 5-year period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the "progressive" MCI group (n = 46; mean time to progression 17 ± 9.73 months) and (ii) the "stable" MCI group (n = 75; mean time of follow-up 31.37 ± 14.58 months). Kaplan-Meier survival analyses were applied to explore each variable's relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model to estimate differences in time to progression. The final model included three variables (in order of relevance): left parahippocampal volume (corrected by intracranial volume, LP_ ICV), delayed recall (DR), and left inferior occipital lobe individual alpha peak frequency (LIOL_IAPF). Those MCIs with LP_ICV volume, DR score, and LIOL_IAPF value lower than the defined cutoff had 6 times, 5.5 times, and 3 times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were "unfavorable" (i.e., values below the cutoff), 100% of cases progressed to AD at the end of follow-up. Our results highlighted the relevance of neurophysiological markers as predictors of conversion (LIOL_IAPF) and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.
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Affiliation(s)
- María Eugenia López
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain.
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain.
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain.
| | - Agustín Turrero
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Statistics and Operational Research, Complutense University of Madrid, Madrid, Spain
| | - Pablo Cuesta
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
| | - Inmaculada Concepción Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Psychology Faculty, Centro Universitario Villanueva, Madrid, Spain
- Physiotherapy and Nursing Faculty, University of Castilla-La Mancha, Toledo, Spain
| | - Ana Barabash
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Laboratory of Psychoneuroendocrinology and Genetics, San Carlos University Hospital, Madrid, Spain
| | - Alberto Marcos
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Neurology Department, San Carlos University Hospital, Madrid, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Madrid, Spain
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24
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Tsentidou G, Moraitou D, Tsolaki M. Cognition in Vascular Aging and Mild Cognitive Impairment. J Alzheimers Dis 2020; 72:55-70. [PMID: 31561369 DOI: 10.3233/jad-190638] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cardiovascular health declines with age, due to vascular risk factors, and this leads to an increasing risk of cognitive decline. Mild cognitive impairment (MCI) is defined as the negative cognitive changes beyond what is expected in normal aging. The purpose of the study was to compare older adults with vascular risk factors (VRF), MCI patients, and healthy controls (HC) in main dimensions of cognitive control. The sample comprised a total of 109 adults, aged 50 to 85 (M = 66.09, S.D. = 9.02). They were divided into three groups: 1) older adults with VRF, 2) MCI patients, and 3) healthy controls (HC). VRF and MCI did not differ significantly in age, educational level, or gender as was the case with HC. The tests used mainly examine inhibition, cognitive flexibility, and working memory processing. Results showed that the VRF group had more Set Loss Errors in drawing designs indicating deficits in establishing cognitive set and in cognitive shifting. MCI patients displayed lower performance in processing. Hence, different types of specific impairments emerge in vascular aging and MCI, and this may imply that discrete underlying pathologies may play a role in the development of somewhat different profiles of cognitive decline.
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Affiliation(s)
- Glykeria Tsentidou
- Laboratoty of Psychology, Department of Experimental and Cognitive Psychology, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI), AUTh, Greece
| | - Despina Moraitou
- Laboratoty of Psychology, Department of Experimental and Cognitive Psychology, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki (GAADRD), Greece.,Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI), AUTh, Greece
| | - Magda Tsolaki
- 1st Department of Neurology, Medical School, Aristotle University of Thessaloniki (AUTh), Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki (GAADRD), Greece.,Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI), AUTh, Greece
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25
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Bonanni L, Moretti D, Benussi A, Ferri L, Russo M, Carrarini C, Barbone F, Arnaldi D, Falasca NW, Koch G, Cagnin A, Nobili F, Babiloni C, Borroni B, Padovani A, Onofrj M, Franciotti R. Hyperconnectivity in Dementia Is Early and Focal and Wanes with Progression. Cereb Cortex 2020; 31:97-105. [DOI: 10.1093/cercor/bhaa209] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 06/19/2020] [Accepted: 07/08/2020] [Indexed: 12/14/2022] Open
Abstract
Abstract
We investigated in a longitudinal multicenter cohort study functional cortical connectivity changes along the course of frontotemporal dementia (FTD) and Alzheimer’s disease (AD) from the prodromal stage of the diseases.
Electroencephalography (EEG) was recorded in 18 FTD and 18 AD patients at the prodromal stage of dementia, at dementia onset, and 3 years after dementia onset. Twenty healthy controls (HC) underwent EEG recordings at the same time interval as the patients.
Mutual information (MI) analysis measured the strength of functional network connectivity.
FTD and AD patients showed greater MI at the prodromal stage of dementia (FTD vs. HC P = 2 × 10−8; AD vs. HC P = 4 × 10–3). Local connectivity was higher in left and right frontal areas of FTD (P = 7 × 10−5 and 0.03) and in left and right posterior areas in AD (P = 3 × 10−5 and 5 × 10−5) versus HC.
We showed cortical hyperconnectivity at the prodromal stage of dementia in areas involved in the specific pathological process of FTD (frontal regions) and AD (posterior regions). Hyperconnectivity disappeared during follow-up, thus suggesting that it is an early electrophysiological feature of dementia, potentially useful to identify prodromal FTD and AD.
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Affiliation(s)
- Laura Bonanni
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Davide Moretti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Alzheimer Unit, 25125 Brescia, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, 25040 Brescia, Italy
| | - Laura Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Mirella Russo
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Claudia Carrarini
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Filomena Barbone
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Dario Arnaldi
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, 16132 Genoa, Italy
- U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Nicola Walter Falasca
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Giacomo Koch
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, 00179 Rome, Italy
| | - Annachiara Cagnin
- Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, 00184 Rome, Italy
- Department of Neurosciences, University of Padua, 35128 Padova, Italy
| | - Flavio Nobili
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, 16132 Genoa, Italy
- U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, 00185 Rome, Italy
- Hospital San Raffaele Cassino (FR), 03043 Cassino, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, 25040 Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, 25040 Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Raffaella Franciotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
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26
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Zinc Therapy in Early Alzheimer's Disease: Safety and Potential Therapeutic Efficacy. Biomolecules 2020; 10:biom10081164. [PMID: 32784855 PMCID: PMC7466035 DOI: 10.3390/biom10081164] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 07/30/2020] [Accepted: 08/05/2020] [Indexed: 12/17/2022] Open
Abstract
Zinc therapy is normally utilized for treatment of Wilson disease (WD), an inherited condition that is characterized by increased levels of non-ceruloplasmin bound ('free') copper in serum and urine. A subset of patients with Alzheimer's disease (AD) or its prodromal form, known as Mild Cognitive Impairment (MCI), fail to maintain a normal copper metabolic balance and exhibit higher than normal values of non-ceruloplasmin copper. Zinc's action mechanism involves the induction of intestinal cell metallothionein, which blocks copper absorption from the intestinal tract, thus restoring physiological levels of non-ceruloplasmin copper in the body. On this basis, it is employed in WD. Zinc therapy has shown potential beneficial effects in preliminary AD clinical trials, even though the studies have missed their primary endpoints, since they have study design and other important weaknesses. Nevertheless, in the studied AD patients, zinc effectively decreased non-ceruloplasmin copper levels and showed potential for improved cognitive performances with no major side effects. This review discusses zinc therapy safety and the potential therapeutic effects that might be expected on a subset of individuals showing both cognitive complaints and signs of copper imbalance.
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Delli Pizzi S, Franciotti R, Ferretti A, Edden RA, Zöllner HJ, Esposito R, Bubbico G, Aiello C, Calvanese F, Sensi SL, Tartaro A, Onofrj M, Bonanni L. High
γ‐Aminobutyric
Acid Content Within the Medial Prefrontal Cortex Is a Functional Signature of Somatic Symptoms Disorder in Patients With Parkinson's Disease. Mov Disord 2020; 35:2184-2192. [DOI: 10.1002/mds.28221] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 06/20/2020] [Accepted: 06/29/2020] [Indexed: 01/20/2023] Open
Affiliation(s)
- Stefano Delli Pizzi
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d'Annunzio” University Chieti‐Pescara Italy
- Center of Aging Sciences and Translational Medicine University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
| | - Raffaella Franciotti
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d'Annunzio” University Chieti‐Pescara Italy
- Center of Aging Sciences and Translational Medicine University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
| | - Antonio Ferretti
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d'Annunzio” University Chieti‐Pescara Italy
| | - Richard A.E. Edden
- Russell H. Morgan Department of Radiology The Johns Hopkins University School of Medicine Baltimore Maryland USA
- F.M. Kirby Center for Functional MRI Kennedy Krieger Institute Baltimore Maryland USA
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology The Johns Hopkins University School of Medicine Baltimore Maryland USA
- F.M. Kirby Center for Functional MRI Kennedy Krieger Institute Baltimore Maryland USA
| | | | - Giovanna Bubbico
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d'Annunzio” University Chieti‐Pescara Italy
| | - Claudia Aiello
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
| | - Francesco Calvanese
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
| | - Stefano L. Sensi
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
- Center of Aging Sciences and Translational Medicine University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
| | - Armando Tartaro
- Department of Medical Sciences, Oral and Biotechnology University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
- Center of Aging Sciences and Translational Medicine University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
| | - Laura Bonanni
- Department of Neuroscience, Imaging and Clinical Sciences University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
- Center of Aging Sciences and Translational Medicine University “G. d'Annunzio” of Chieti‐Pescara Chieti Italy
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28
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Qi Z, An Y, Zhang M, Li HJ, Lu J. Altered Cerebro-Cerebellar Limbic Network in AD Spectrum: A Resting-State fMRI Study. Front Neural Circuits 2019; 13:72. [PMID: 31780903 PMCID: PMC6851020 DOI: 10.3389/fncir.2019.00072] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 10/17/2019] [Indexed: 12/05/2022] Open
Abstract
Recent evidence suggests that the cerebellum is related to motor and non-motor cognitive functions, and that several coupled cerebro-cerebellar networks exist, including links with the limbic network. Since several limbic structures are affected by Alzheimer pathology, even in the preclinical stages of Alzheimer’s disease (AD), we aimed to investigate the cerebral limbic network activity from the perspective of the cerebellum. Twenty patients with mild cognitive impairment (MCI), 18 patients with AD, and 26 healthy controls (HC) were recruited to acquire Resting-state functional MRI (rs-fMRI). We used seed-based approach to construct the cerebro-cerebellar limbic network. Two-sample t-tests were carried out to explore the differences of the cerebellar limbic network connectivity. The first result, a sub-scale network including the bilateral posterior part of the orbitofrontal cortex (POFC) extending to the anterior insular cortex (AIC) and left inferior parietal lobule (L-IPL), showed greater functional connectivity in MCI than in HC and less functional connectivity in AD than in MCI. The location of this sub-scale network was in accordance with components of the ventral attention network. Second, there was decreased functional connectivity to the right mid-cingulate cortex (MCC) in the AD and MCI patient groups relative to the HC group. As the cerebellum is not compromised by Alzheimer pathology in the prodromal stage of AD, this pattern indicates that the sub-scale ventral attention network may play a pivotal role in functional compensation through the coupled cerebro-cerebellar limbic network in MCI, and the cerebellum may be a key node in the modulation of social cognition.
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Affiliation(s)
- Zhigang Qi
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Yanhong An
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Mo Zhang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Hui-Jie Li
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
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29
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Granzotto A, Bomba M, Castelli V, Navarra R, Massetti N, d'Aurora M, Onofrj M, Cicalini I, Del Boccio P, Gatta V, Cimini A, Piomelli D, Sensi SL. Inhibition of de novo ceramide biosynthesis affects aging phenotype in an in vitro model of neuronal senescence. Aging (Albany NY) 2019; 11:6336-6357. [PMID: 31467258 PMCID: PMC6738398 DOI: 10.18632/aging.102191] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 08/10/2019] [Indexed: 12/13/2022]
Abstract
Although aging is considered to be an unavoidable event, recent experimental evidence suggests that the process can be counteracted. Intracellular calcium (Ca2+i) dyshomeostasis, mitochondrial dysfunction, oxidative stress, and lipid dysregulation are critical factors that contribute to senescence-related processes. Ceramides, a pleiotropic class of sphingolipids, are important mediators of cellular senescence, but their role in neuronal aging is still largely unexplored. In this study, we investigated the effects of L-cycloserine (L-CS), an inhibitor of thede novoceramide biosynthesis, on the aging phenotype of cortical neurons cultured for 22 days, a setting employed as anin vitromodel of senescence. Our findings indicate that, compared to control cultures, ‘aged’ neurons display dysregulation of [Ca2+]ilevels, mitochondrial dysfunction, increased generation of reactive oxygen species (ROS), altered synaptic activity as well as the activation of neuronal death-related molecules. Treatment with L-CS positively affected the senescent phenotype, a result associated with recovery of neuronal [Ca2+]isignaling and reduction of mitochondrial dysfunction and ROS generation. The results suggest that thede novoceramide biosynthesis represents a critical intermediate in the molecular and functional cascade leading to neuronal senescence and identify ceramide biosynthesis inhibitors as promising pharmacological tools to decrease age-related neuronal dysfunctions.
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Affiliation(s)
- Alberto Granzotto
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Manuela Bomba
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Vanessa Castelli
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Riccardo Navarra
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Noemi Massetti
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Marco d'Aurora
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Psychological, Health and Territorial Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Marco Onofrj
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Ilaria Cicalini
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Pharmacy, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Piero Del Boccio
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Pharmacy, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Valentina Gatta
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Psychological, Health and Territorial Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Annamaria Cimini
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy.,Sbarro Institute for Cancer Research and Molecular Medicine and Center for Biotechnology, Temple University, Philadelphia, PA 19122, USA.,National Institute for Nuclear Physics (INFN), Gran Sasso National Laboratory (LNGS), Assergi, Italy
| | - Daniele Piomelli
- Departments of Anatomy and Neurobiology, Biochemistry and Pharmacology, University of California Irvine, Irvine, CA 92697, USA
| | - Stefano L Sensi
- Center of Excellence on Aging and Translational Medicine (CeSI-MeT), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.,Departments of Neurology and Pharmacology, Institute for Mind Impairments and Neurological Disorders (iMIND), University of California Irvine, Irvine, CA 92697, USA
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30
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A quantitative model of human neurodegenerative diseases involving protein aggregation. Neurobiol Aging 2019; 80:46-55. [DOI: 10.1016/j.neurobiolaging.2019.04.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 04/02/2019] [Accepted: 04/02/2019] [Indexed: 12/12/2022]
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31
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Zhang B, Hua R, Qing Z, Ni L, Zhang X, Zhao H, Liu R, Lu J, Wu S, Xu Y, Zhu B, Wan S, Sun Y. Abnormal brain functional connectivity coupled with hypoperfusion measured by Resting-State fMRI: An additional contributing factor for cognitive impairment in patients with Alzheimer's disease. Psychiatry Res Neuroimaging 2019; 289:18-25. [PMID: 31125938 DOI: 10.1016/j.pscychresns.2019.04.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/20/2019] [Accepted: 04/28/2019] [Indexed: 12/11/2022]
Abstract
The contribution of hypoperfusion to abnormal functional connectivity in Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains unclear. In this study, we investigated the potential association between brain perfusion and functional connectivity (FC), and its effects on the cognitive impairment among AD, MCI, and normal controls (NC). One-time acquisition of resting-state functional magnetic resonance imaging (rs-fMRI) was used to study brain perfusion and FC. Compared to the NC, the perfusion in the left temporal lobe showed significantly lower in AD, and bilateral hypoperfusion in the frontal lobe showed in MCI. Using these hypoperfusion areas as seed regions, we found that FC between the left inferior temporal gyrus and medial frontal-cingulate regions in AD patients was significantly lower than that in NCs. The FC between the right medial superior frontal gyrus and left parietal lobe in MCI patients was significantly higher than that in NCs. Additionally, the FC between the right medial superior frontal gyrus and the left superior parietal gyrus were found to be correlated significantly and negatively with mini-mental state examination (MMSE) scores in MCI patients. In conclusion, hypoperfusion may affect cognitive states via abnormal FC as an additional factor contributing to cognitive impairment.
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Affiliation(s)
- Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Rui Hua
- The Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - Zhao Qing
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Ling Ni
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Renyuan Liu
- Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Sichu Wu
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yun Xu
- Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Bin Zhu
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
| | - Suiren Wan
- The Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.
| | - Yu Sun
- International Laboratory for Children's Medical Imaging Research, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China; Institute of Cancer and Genomic Sciences, University of Birmingham, UK.
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32
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Maestú F, Cuesta P, Hasan O, Fernandéz A, Funke M, Schulz PE. The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease. Front Hum Neurosci 2019; 13:17. [PMID: 30792632 PMCID: PMC6374629 DOI: 10.3389/fnhum.2019.00017] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 01/15/2019] [Indexed: 12/22/2022] Open
Abstract
Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers.
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Affiliation(s)
- Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Magnetic Source Imaging Unit, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering & IUNE Universidad de La Laguna, Tenerife, Spain
| | - Omar Hasan
- McGovern Medical School University of Texas Health Science Center, Houston, TX, United States
| | - Alberto Fernandéz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Legal Medicine, Psychiatry, and Pathology, Universidad Complutense de Madrid, Madrid, Spain
| | - Michael Funke
- Magnetic Source Imaging Unit, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
| | - Paul E. Schulz
- McGovern Medical School University of Texas Health Science Center, Houston, TX, United States
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