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Tang QY, Huang BL, Huang X. Altered functional connectivity between the default mode network in primary angle-closure glaucoma patients. Neuroreport 2024; 35:129-135. [PMID: 38251458 DOI: 10.1097/wnr.0000000000001995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Previous studies have recognized glaucoma as a neurodegenerative disease that causes extensive brain damage and is closely associated with cognitive function. In this study, we employed functional MRI to examine the intrinsic functional connectivity patterns of the default mode network (DMN) in patients diagnosed with primary angle-closure glaucoma (PACG), exploring its association with cognitive dysfunction. A total of 34 patients diagnosed with PACG and 34 healthy controls (HC), who were matched in terms of sex, age, and education, were included in the control group. The posterior cingulate cortex (PCC) was selected as the region of interest to examine functional connectivity alterations. Compared with the HC group, functional connectivity was attenuated in left anterior cingulum cortex and left paracentral lobule between with PCC in the PACG group, the results are statistically significant. Our study revealed that patients with PACG exhibit weakened functional connectivity within the DMN. This finding suggests the presence of a neurological mechanism that is associated with both visual dysfunction and cognitive impairments in PACG patients. Furthermore, our study provides neuroimaging evidence that can aid in the exploration of spontaneous neurological alterations and facilitate a deeper investigation of alterations in the visual conduction pathways of PACG patients.
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Affiliation(s)
- Qiu-Yu Tang
- College of Clinical Medicine, Jiangxi University of Chinese Medicine
| | - Bing-Lin Huang
- College of Clinical Medicine, Jiangxi University of Chinese Medicine
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
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Lee EY, Kim J, Prado-Rico JM, Du G, Lewis MM, Kong L, Yanosky JD, Eslinger P, Kim BG, Hong YS, Mailman RB, Huang X. Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.07.18.23292828. [PMID: 37503124 PMCID: PMC10371112 DOI: 10.1101/2023.07.18.23292828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background Environmental exposure to metal mixtures is common and may be associated with increased risk for neurodegenerative disorders including Alzheimer's disease. Objective This study examined associations of mixed metal exposures with medial temporal lobe (MTL) MRI structural metrics and neuropsychological performance. Methods Metal exposure history, whole blood metal, and neuropsychological tests were obtained from subjects with/without a history of mixed metal exposure from welding fumes (42 exposed subjects; 31 controls). MTL structures (hippocampus, entorhinal and parahippocampal cortices) were assessed by morphologic (volume, cortical thickness) and diffusion tensor imaging [mean (MD), axial (AD), radial diffusivity (RD), and fractional anisotropy (FA)] metrics. In exposed subjects, correlation, multiple linear, Bayesian kernel machine regression, and mediation analyses were employed to examine effects of single- or mixed-metal predictor(s) and their interactions on MTL structural and neuropsychological metrics; and on the path from metal exposure to neuropsychological consequences. Results Compared to controls, exposed subjects had higher blood Cu, Fe, K, Mn, Pb, Se, and Zn levels (p's<0.026) and poorer performance in processing/psychomotor speed, executive, and visuospatial domains (p's<0.046). Exposed subjects displayed higher MD, AD, and RD in all MTL ROIs (p's<0.040) and lower FA in entorhinal and parahippocampal cortices (p's<0.033), but not morphological differences. Long-term mixed-metal exposure history indirectly predicted lower processing speed performance via lower parahippocampal FA (p=0.023). Higher whole blood Mn and Cu predicted higher entorhinal diffusivity (p's<0.043) and lower Delayed Story Recall performance (p=0.007) without overall metal mixture or interaction effects. Discussion Mixed metal exposure predicted MTL structural and neuropsychological features that are similar to Alzheimer's disease at-risk populations. These data warrant follow-up as they may illuminate the path for environmental exposure to Alzheimer's disease-related health outcomes.
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Affiliation(s)
- Eun-Young Lee
- Department of Health Care and Science, Dong-A University, Busan, South-Korea
| | - Juhee Kim
- Department of Health Care and Science, Dong-A University, Busan, South-Korea
| | - Janina Manzieri Prado-Rico
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Guangwei Du
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Mechelle M. Lewis
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Pharmacology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Lan Kong
- Department of Public Health Sciences, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Jeff D. Yanosky
- Department of Public Health Sciences, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Paul Eslinger
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Byoung-Gwon Kim
- Department of Preventive Medicine, College of Medicine, Dong-A University, Busan, South Korea
| | - Young-Seoub Hong
- Department of Preventive Medicine, College of Medicine, Dong-A University, Busan, South Korea
| | - Richard B. Mailman
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Pharmacology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Xuemei Huang
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Pharmacology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Radiology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Neurosurgery, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Kinesiology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
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Bergamino M, Keeling E, McElvogue M, Schaefer SY, Burke A, Prigatano G, Stokes AM. White Matter Microstructure Analysis in Subjective Memory Complaints and Cognitive Impairment: Insights from Diffusion Kurtosis Imaging and Free-Water DTI. J Alzheimers Dis 2024; 98:863-884. [PMID: 38461504 DOI: 10.3233/jad-230952] [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: 03/12/2024]
Abstract
Background Dementia is characterized by a cognitive decline in memory and other domains that lead to functional impairments. As people age, subjective memory complaints (SMC) become common, where individuals perceive cognitive decline without objective deficits on assessments. SMC can be an early sign and may precede amnestic mild cognitive impairment (MCI), which frequently advances to Alzheimer's disease (AD). Objective This study aims to investigate white matter microstructure in individuals with SMC, in cognitively impaired (CI) cohorts, and in cognitively normal individuals using diffusion kurtosis imaging (DKI) and free water imaging (FWI). The study also explores voxel-based correlations between DKI/FWI metrics and cognitive scores to understand the relationship between brain microstructure and cognitive function. Methods Twelve healthy controls (HCs), ten individuals with SMC, and eleven CI individuals (MCI or AD) were enrolled in this study. All participants underwent MRI 3T scan and the BNI Screen (BNIS) for Higher Cerebral Functions. Results The mean kurtosis tensor and anisotropy of the kurtosis tensor showed significant differences across the three groups, indicating altered white matter microstructure in CI and SMC individuals. The free water volume fraction (f) also revealed group differences, suggesting changes in extracellular water content. Notably, these metrics effectively discriminated between the CI and HC/SMC groups. Additionally, correlations between imaging metrics and BNIS scores were found for CI and SMC groups. Conclusions These imaging metrics hold promise in discriminating between individuals with CI and SMC. The observed differences indicate their potential as sensitive and specific biomarkers for early detection and differentiation of cognitive decline.
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Affiliation(s)
| | - Elizabeth Keeling
- Barrow Neurological Institute, Phoenix, AZ, USA
- Arizona State University, Phoenix, AZ, USA
| | | | | | - Anna Burke
- Barrow Neurological Institute, Phoenix, AZ, USA
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Yulug B, Ayyildiz S, Sayman D, Karaca R, Ipek L, Cankaya S, Salar AB, Ayyildiz B, Mikuta C, Yagci N, Oktem EO, Ozsimsek A, Velioglu HA, Hanoglu L. The functional role of the pulvinar in discriminating between objective and subjective cognitive impairment in major depressive disorder. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2024; 10:e12450. [PMID: 38356480 PMCID: PMC10865482 DOI: 10.1002/trc2.12450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/04/2023] [Accepted: 11/09/2023] [Indexed: 02/16/2024]
Abstract
INTRODUCTION Emotionally driven cognitive complaints represent a major diagnostic challenge for clinicians and indicate the importance of objective confirmation of the accuracy of depressive patients' descriptions of their cognitive symptoms. METHODS We compared cognitive status and structural and functional brain connectivity changes in the pulvinar and hippocampus between patients with total depression and healthy controls. The depressive group was also classified as "amnestic" or "nonamnestic," based on the members' subjective reports concerning their forgetfulness. We then sought to determine whether these patients would differ in terms of objective neuroimaging and cognitive findings. RESULTS The right pulvinar exhibited altered connectivity in individuals with depression with objective cognitive impairment, a finding which was not apparent in depressive patients with subjective cognitive impairment. DISCUSSION The pulvinar may play a role in depression-related cognitive impairments. Connectivity network changes may differ between objective and subjective cognitive impairment in depression and may play a role in the increased risk of dementia in patients with depression.
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Affiliation(s)
- Burak Yulug
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
- Department of Neurology and NeuroscienceIstanbul Medipol UniversityIstanbulTurkey
| | - Sevilay Ayyildiz
- School of MedicineDepartment of NeuroradiologyTechnical University of MunichMunichGermany
- School of MedicineTUM‐NIC Neuroimaging CenterTechnical University of MunichMunichGermany
- Anatomy PhD ProgramGraduate School of Health SciencesKocaeli UniversityIstanbulTurkey
| | - Dila Sayman
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Ramazan Karaca
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Lutfiye Ipek
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Seyda Cankaya
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Ali Behram Salar
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN)Health Sciences and Technology Research Institute (SABITA)Istanbul Medipol UniversityIstanbulTurkey
| | - Behcet Ayyildiz
- Anatomy PhD ProgramGraduate School of Health SciencesKocaeli UniversityIstanbulTurkey
| | - Christian Mikuta
- Translational Research CenterUniversity Hospital of Psychiatry and PsychotherapyUniversity of BernBernSwitzerland
- Interdisciplinary Biosciences Doctoral Training PartnershipDepartment of PhysiologyAnatomy and GeneticsUniversity of OxfordOxfordUK
| | - Nilay Yagci
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Ece Ozdemir Oktem
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Ahmet Ozsimsek
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Halil Aziz Velioglu
- School of MedicineTUM‐NIC Neuroimaging CenterTechnical University of MunichMunichGermany
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
| | - Lutfu Hanoglu
- Department of Neurology and NeuroscienceIstanbul Medipol UniversityIstanbulTurkey
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Hu R, Gao L, Chen P, Wei X, Wu X, Xu H. Macroscale neurovascular coupling and functional integration in end-stage renal disease patients with cognitive impairment: A multimodal MRI study. J Neurosci Res 2024; 102:e25277. [PMID: 38284834 DOI: 10.1002/jnr.25277] [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: 07/19/2023] [Revised: 10/06/2023] [Accepted: 11/06/2023] [Indexed: 01/30/2024]
Abstract
End-stage renal disease (ESRD) is associated with vascular and neuronal dysfunction, causing neurovascular coupling (NVC) dysfunction, but how NVC dysfunction acts on the mechanism of cognitive impairment in ESRD patients from local to remote is still poorly understood. We recruited 48 ESRD patients and 35 demographically matched healthy controls to scan resting-state functional MRI and arterial spin labeling, then investigated the four types of NVC between amplitude of low-frequency fluctuation (ALFF), fractional ALFF, regional homogeneity, degree centrality, and cerebral blood perfusion (CBF), and associated functional networks. Our results indicated that ESRD patients showed NVC dysfunction in global gray matter and multiple brain regions due to the mismatch between CBF and neural activity, and associated disrupted functional connectivity (FC) within sensorimotor network (SMN), visual network (VN), default mode network (DMN), salience network (SN), and disrupted FC between them with limbic network (LN), while increased FC between SMN and DMN. Anemia may affect the NVC of middle occipital gyrus and precuneus, and increased pulse pressure may result in disrupted FC with SMN. The NVC dysfunction of the right precuneus, middle frontal gyrus, and parahippocampal gyrus and the FC between the right angular gyrus and the right anterior cingulate gyrus may reflect cognitive impairment in ESRD patients. Our study confirmed that ESRD patients may exist NVC dysfunction and disrupted functional integration in SMN, VN, DMN, SN and LN, serving as one of the mechanisms of cognitive impairment. Anemia and increased pulse pressure may be related risk factors.
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Affiliation(s)
- Runyue Hu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Peina Chen
- Department of Nephrology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Nephrology, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou, China
| | - Xiaobao Wei
- Department of Nephrology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Nephrology, Lianyungang No 1 People's Hospital, Lianyungang, China
| | - Xiaoyan Wu
- Department of Nephrology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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56
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Ersözlü E, Rauchmann BS. Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease. Methods Mol Biol 2024; 2785:89-104. [PMID: 38427190 DOI: 10.1007/978-1-0716-3774-6_7] [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: 03/02/2024]
Abstract
Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.
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Affiliation(s)
- Ersin Ersözlü
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Department of Geriatric Psychiatry and Developmental Disorders, kbo-Isar-Amper-Klinikum Munich East, Academic Teaching Hospital of LMU Munich, Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Department of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
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Chang H, Chen E, Zhu T, Liu J, Chen C. Communication Regarding the Myocardial Ischemia/Reperfusion and Cognitive Impairment: A Narrative Literature Review. J Alzheimers Dis 2024; 97:1545-1570. [PMID: 38277294 PMCID: PMC10894588 DOI: 10.3233/jad-230886] [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] [Accepted: 12/07/2023] [Indexed: 01/28/2024]
Abstract
Coronary artery disease is a prevalent ischemic disease that results in insufficient blood supply to the heart muscle due to narrowing or occlusion of the coronary arteries. Various reperfusion strategies, including pharmacological thrombolysis and percutaneous coronary intervention, have been developed to enhance blood flow restoration. However, these interventions can lead to myocardial ischemia/reperfusion injury (MI/RI), which can cause unpredictable complications. Recent research has highlighted a compelling association between MI/RI and cognitive function, revealing pathophysiological mechanisms that may explain altered brain cognition. Manifestations in the brain following MI/RI exhibit pathological features resembling those observed in Alzheimer's disease (AD), implying a potential link between MI/RI and the development of AD. The pro-inflammatory state following MI/RI may induce neuroinflammation via systemic inflammation, while impaired cardiac function can result in cerebral under-perfusion. This review delves into the role of extracellular vesicles in transporting deleterious substances from the heart to the brain during conditions of MI/RI, potentially contributing to impaired cognition. Addressing the cognitive consequence of MI/RI, the review also emphasizes potential neuroprotective interventions and pharmacological treatments within the MI/RI model. In conclusion, the review underscores the significant impact of MI/RI on cognitive function, summarizes potential mechanisms of cardio-cerebral communication in the context of MI/RI, and offers ideas and insights for the prevention and treatment of cognitive dysfunction following MI/RI.
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Affiliation(s)
- Haiqing Chang
- Department of Anesthesiology, West China Hospital, Sichuan University, Sichuan, China
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Erya Chen
- Department of Anesthesiology, West China Hospital, Sichuan University, Sichuan, China
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Tao Zhu
- Department of Anesthesiology, West China Hospital, Sichuan University, Sichuan, China
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jin Liu
- Department of Anesthesiology, West China Hospital, Sichuan University, Sichuan, China
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chan Chen
- Department of Anesthesiology, West China Hospital, Sichuan University, Sichuan, China
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Ruan Y, Zheng D, Guo W, Cao X, Qi W, Yuan Q, Zhang X, Liang X, Zhang D, Xue C, Xiao C. Shared and Specific Changes of Cortico-Striatal Functional Connectivity in Stable Mild Cognitive Impairment and Progressive Mild Cognitive Impairment. J Alzheimers Dis 2024; 98:1301-1317. [PMID: 38517789 DOI: 10.3233/jad-231174] [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] [Indexed: 03/24/2024]
Abstract
Background Mild cognitive impairment (MCI), the prodromal stage of Alzheimer's disease, has two distinct subtypes: stable MCI (sMCI) and progressive MCI (pMCI). Early identification of the two subtypes has important clinical significance. Objective We aimed to compare the cortico-striatal functional connectivity (FC) differences between the two subtypes of MCI and enhance the accuracy of differential diagnosis between sMCI and pMCI. Methods We collected resting-state fMRI data from 31 pMCI patients, 41 sMCI patients, and 81 healthy controls. We chose six pairs of seed regions, including the ventral striatum inferior, ventral striatum superior, dorsal-caudal putamen, dorsal-rostral putamen, dorsal caudate, and ventral-rostral putamen and analyzed the differences in cortico-striatal FC among the three groups, additionally, the relationship between the altered FC within the MCI subtypes and cognitive function was examined. Results Compared to sMCI, the pMCI patients exhibited decreased FC between the left dorsal-rostral putamen and right middle temporal gyrus, the right dorsal caudate and right inferior temporal gyrus, and the left dorsal-rostral putamen and left superior frontal gyrus. Additionally, the altered FC between the right inferior temporal gyrus and right putamen was significantly associated with episodic memory and executive function. Conclusions Our study revealed common and distinct cortico-striatal FC changes in sMCIs and pMCI across different seeds; these changes were associated with cognitive function. These findings can help us understand the underlying pathophysiological mechanisms of MCI and distinguish pMCI and sMCI in the early stage potentially.
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Affiliation(s)
- Yiming Ruan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Darui Zheng
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenxuan Guo
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xuan Cao
- Department of Mathematical Sciences, Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, USA
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xulian Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xuhong Liang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Da Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chaoyong Xiao
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Caminiti SP, De Francesco S, Tondo G, Galli A, Redolfi A, Perani D, the Alzheimer's Disease Neuroimaging Initiative. FDG-PET markers of heterogeneity and different risk of progression in amnestic MCI. Alzheimers Dement 2024; 20:159-172. [PMID: 37505996 PMCID: PMC10962797 DOI: 10.1002/alz.13385] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/19/2023] [Accepted: 06/12/2023] [Indexed: 07/30/2023]
Abstract
INTRODUCTION Amnestic mild cognitive impairment (aMCI) is emerging as a heterogeneous condition. METHODS We looked at a cohort of N = 207 aMCI subjects, with baseline fluorodeoxyglucose positron emission tomography (FDG-PET), T1 magnetic resonance imaging, cerebrospinal fluid (CSF), apolipoprotein E (APOE), and neuropsychological assessment. An algorithm based on FDG-PET hypometabolism classified each subject into subtypes, then compared biomarker measures and clinical progression. RESULTS Three subtypes emerged: hippocampal sparing-cortical hypometabolism, associated with younger age and the highest level of Alzheimer's disease (AD)-CSF pathology; hippocampal/cortical hypometabolism, associated with a high percentage of APOE ε3/ε4 or ε4/ε4 carriers; medial-temporal hypometabolism, characterized by older age, the lowest AD-CSF pathology, the most severe hippocampal atrophy, and a benign course. Within the whole cohort, the severity of temporo-parietal hypometabolism, correlated with AD-CSF pathology and marked the rate of progression of cognitive decline. DISCUSSION FDG-PET can distinguish clinically comparable aMCI at single-subject level with different risk of progression to AD dementia or stability. The obtained results can be useful for the optimization of pharmacological trials and automated-classification models. HIGHLIGHTS Algorithm based on FDG-PET hypometabolism demonstrates distinct subtypes across aMCI; Three different subtypes show heterogeneous biological profiles and risk of progression; The cortical hypometabolism is associated with AD pathology and cognitive decline; MTL hypometabolism is associated with the lowest conversion rate and CSF-AD pathology.
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Affiliation(s)
- Silvia Paola Caminiti
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Silvia De Francesco
- Laboratory of NeuroinformaticsIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
| | - Giacomo Tondo
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Alice Galli
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Alberto Redolfi
- Laboratory of NeuroinformaticsIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
| | - Daniela Perani
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
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Zhu Y, Cheng J, Li Y, Pan D, Li H, Xu Y, Du Z, Lei M, Xiao S, Shen Q, Shi Z, Tang Y. Progression of cognitive dysfunction in NPC survivors with radiation-induced brain necrosis: A prospective cohort. Radiother Oncol 2024; 190:110033. [PMID: 38030079 DOI: 10.1016/j.radonc.2023.110033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 10/31/2023] [Accepted: 11/19/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND AND PURPOSE The evidence of longitudinal changes in cognition in nasopharyngeal carcinoma (NPC) survivors with radiation-induced brain necrosis (RIBN) after radiotherapy (RT) remained insufficient. We aimed to estimate the clinical progression rate of cognitive decline and identify patients with differential decline rates. MATERIALS AND METHODS Based on an ongoing prospective cohort study, NPC patients aged ≥18 years old and diagnosed with RIBN were included in this current analysis if they finished the time frame of 3-year follow-up and had at least twice cognition assessments. The Chinese version of the Montreal Cognitive Assessment (MoCA) was used to assess the cognitive state. Linear mixed-effect models were used to analyze the annual progression rates of MoCA total and seven sub-items scores. RESULTS Among 134 patients in this study, the transition probability from normal to mild/moderate cognitive dysfunction were 14.2 % (19/134) and 1.49 % (2/134) respectively during the median follow-up time of 2.35 years. The total MoCA score declined by -0.569 (SE 0.208) points annually (p = 0.008). Patients with ≤6 years of duration from RT to RIBN have higher annual progression rate of total scores [-0.851 (SE 0.321), p = 0.013; p for interaction = 0.041]. CONCLUSION Our findings of the annual decline rate of cognition in NPC patients with RIBN from a 3-year longitudinal data, particularly for those who developed RIBN rapidly after RT, have important implications for the upcoming clinical trials designed to prevent or decrease cognitive decline in NPC patients with RIBN, regarding the selection of study patients and the calculation of sample size.
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Affiliation(s)
- Yingying Zhu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China; Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Jinping Cheng
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Yi Li
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Dong Pan
- Department of Neurology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 528406, China
| | - Honghong Li
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Yongteng Xu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Zhicheng Du
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Ming Lei
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Songhua Xiao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Qingyu Shen
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Zhongshan Shi
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Yamei Tang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China; Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510120, China.
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Ramsden CE, Zamora D, Horowitz MS, Jahanipour J, Calzada E, Li X, Keyes GS, Murray HC, Curtis MA, Faull RM, Sedlock A, Maric D. ApoER2-Dab1 disruption as the origin of pTau-associated neurodegeneration in sporadic Alzheimer's disease. Acta Neuropathol Commun 2023; 11:197. [PMID: 38093390 PMCID: PMC10720169 DOI: 10.1186/s40478-023-01693-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/16/2023] [Indexed: 12/17/2023] Open
Abstract
In sporadic Alzheimer's disease (sAD) specific regions, layers and neurons accumulate hyperphosphorylated Tau (pTau) and degenerate early while others remain unaffected even in advanced disease. ApoER2-Dab1 signaling suppresses Tau phosphorylation as part of a four-arm pathway that regulates lipoprotein internalization and the integrity of actin, microtubules, and synapses; however, the role of this pathway in sAD pathogenesis is not fully understood. We previously showed that multiple ApoER2-Dab1 pathway components including ApoE, Reelin, ApoER2, Dab1, pP85αTyr607, pLIMK1Thr508, pTauSer202/Thr205 and pPSD95Thr19 accumulate together within entorhinal-hippocampal terminal zones in sAD, and proposed a unifying hypothesis wherein disruption of this pathway underlies multiple aspects of sAD pathogenesis. However, it is not yet known whether ApoER2-Dab1 disruption can help explain the origin(s) and early progression of pTau pathology in sAD. In the present study, we applied in situ hybridization and immunohistochemistry (IHC) to characterize ApoER2 expression and accumulation of ApoER2-Dab1 pathway components in five regions known to develop early pTau pathology in 64 rapidly autopsied cases spanning the clinicopathological spectrum of sAD. We found that (1) these selectively vulnerable neuron populations strongly express ApoER2; and (2) multiple ApoER2-Dab1 components representing all four arms of this pathway accumulate in abnormal neurons and neuritic plaques in mild cognitive impairment (MCI) and sAD cases and correlate with histological progression and cognitive deficits. Multiplex-IHC revealed that Dab1, pP85αTyr607, pLIMK1Thr508, pTauSer202/Thr205 and pPSD95Thr19 accumulate together within many of the same ApoER2-expressing neurons and in the immediate vicinity of ApoE/ApoJ-enriched extracellular plaques. Collective findings reveal that pTau is only one of many ApoER2-Dab1 pathway components that accumulate in multiple neuroanatomical sites in the earliest stages of sAD and provide support for the concept that ApoER2-Dab1 disruption drives pTau-associated neurodegeneration in human sAD.
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Affiliation(s)
- Christopher E Ramsden
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH (NIA/NIH), 251 Bayview Blvd., Baltimore, MD, 21224, USA.
- Intramural Program of the National Institute on Alcohol Abuse and Alcoholism, NIH, Bethesda, MD, 20892, USA.
| | - Daisy Zamora
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH (NIA/NIH), 251 Bayview Blvd., Baltimore, MD, 21224, USA
| | - Mark S Horowitz
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH (NIA/NIH), 251 Bayview Blvd., Baltimore, MD, 21224, USA
| | - Jahandar Jahanipour
- Flow and Imaging Cytometry Core Facility, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, 20892, USA
| | - Elizabeth Calzada
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH (NIA/NIH), 251 Bayview Blvd., Baltimore, MD, 21224, USA
| | - Xiufeng Li
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH (NIA/NIH), 251 Bayview Blvd., Baltimore, MD, 21224, USA
| | - Gregory S Keyes
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH (NIA/NIH), 251 Bayview Blvd., Baltimore, MD, 21224, USA
| | - Helen C Murray
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, University of Auckland, Private Bag, Auckland, 92019, New Zealand
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, 20892, USA
| | - Maurice A Curtis
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, University of Auckland, Private Bag, Auckland, 92019, New Zealand
| | - Richard M Faull
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, University of Auckland, Private Bag, Auckland, 92019, New Zealand
| | - Andrea Sedlock
- Flow and Imaging Cytometry Core Facility, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, 20892, USA
| | - Dragan Maric
- Flow and Imaging Cytometry Core Facility, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, 20892, USA
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Zhong S, Lou J, Ma K, Shu Z, Chen L, Li C, Ye Q, Zhou L, Shen Y, Ye X, Zhang J. Disentangling in-vivo microstructural changes of white and gray matter in mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis. Brain Imaging Behav 2023; 17:764-777. [PMID: 37752311 DOI: 10.1007/s11682-023-00805-2] [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] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
The microstructural characteristics of white and gray matter in mild cognitive impairment (MCI) and the early-stage of Alzheimer's disease (AD) remain unclear. This study aimed to systematically identify the microstructural damages of MCI/AD in studies using neurite orientation dispersion and density imaging (NODDI), and explore their correlations with cognitive performance. Multiple databases were searched for eligible studies. The 10 eligible NODDI studies were finally included. Patients with MCI/AD showed overall significant reductions in neurite density index (NDI) of specific white matter structures in bilateral hemispheres (left hemisphere: -0.40 [-0.53, -0.27], P < 0.001; right: -0.33 [-0.47, -0.19], P < 0.001), involving the bilateral superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), the left posterior thalamic radiation (PTR), and the left cingulum. White matter regions exhibited significant increased orientation dispersion index (ODI) (left: 0.25 [0.02, 0.48], P < 0.05; right: 0.27 [0.07, 0.46], P < 0.05), including the left cingulum, the right UF, and the bilateral parahippocampal cingulum (PHC), and PTR. Additionally, the ODI of gray matter showed significant reduction in bilateral hippocampi (left: -0.97 [-1.42, -0.51], P < 0.001; right: -0.90 [-1.35, -0.45], P < 0.001). The cognitive performance in MCI/AD was significantly associated with NDI (r = 0.50, P < 0.001). Our findings highlight the microstructural changes in MCI/AD were characterized by decreased fiber orientation dispersion in the hippocampus, and decreased neurite density and increased fiber orientation dispersion in specific white matter tracts, including the cingulum, UF, and PTR. Moreover, the decreased NDI may indicate the declined cognitive level of MCI/AD patients.
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Affiliation(s)
- Shuchang Zhong
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jingjing Lou
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ke Ma
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Lin Chen
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Chao Li
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qing Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liang Zhou
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ye Shen
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiangming Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jie Zhang
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
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63
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Mahady L, Perez SE, Malek-Ahmadi M, Mufson EJ. Oligomeric, phosphorylated, and truncated tau and spliceosome pathology within the entorhinal-hippocampal connectome across stages of Alzheimer's disease. J Comp Neurol 2023; 531:2080-2108. [PMID: 36989381 PMCID: PMC10539478 DOI: 10.1002/cne.25466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/27/2023] [Accepted: 02/06/2023] [Indexed: 03/31/2023]
Abstract
Neurofibrillary tangles (NFTs) contain abnormally phosphorylated tau proteins, which spread within components of the medial temporal lobe (MTL) memory circuit in Alzheimer's disease (AD). Here, we used quantitative immunohistochemistry to determine the density of posttranslational oligomeric (TOC1 and TNT1), phosphorylated (AT8), and late truncated (TauC3) tau epitopes within the MTL subfields including entorhinal cortex (EC) layer II, subiculum, Cornu Ammonis (CA) subfields, and dentate gyrus (DG) in subjects who died with a clinical diagnosis of no cognitive impairment (NCI), mild cognitive impairment (MCI), and AD. We also examined whether alterations of the nuclear alternative splicing protein, SRSF2, are associated with tau pathology. Although a significant increase in TOC1, TNT1, and AT8 neuron density occurred in the EC in MCI and AD, subicular, DG granule cell, and CA1 and CA3 densities were only significantly higher in AD. TauC3 counts were not different between connectome regions and clinical groups. SRSF2 intensity in AT8-positive cells decreased significantly in all regions independent of the clinical groups examined. CA1 and subicular AT8, TauC3, and oligomeric densities correlated across clinical groups. EC AT8 counts correlated with CA subfields and subicular and DG values across clinical groups. Oligomeric and AT8 CA1, EC, and subicular density correlated with Braak stage. Decreased nuclear SRSF2 in the presence of cytoplasmic phosphorylated tau suggests a dual-hit process in NFT formation within the entorhinal hippocampal connectome during the onset of AD. Although oligomeric and phosphorylated tau follow a stereotypical pattern, clinical disease stage determined density of tau deposition and not anatomic location within the entorhinal-hippocampal connectome.
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Affiliation(s)
- Laura Mahady
- Dept. of Translational Neuroscience, Phoenix, AZ
| | | | | | - Elliott J. Mufson
- Dept. of Translational Neuroscience, Phoenix, AZ
- Dept. of Neurology, Barrow Neurological Institute, Phoenix, AZ 85013
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64
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Xu X, Lin L, Wu S, Sun S. Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics. Brain Sci 2023; 13:1651. [PMID: 38137099 PMCID: PMC10741933 DOI: 10.3390/brainsci13121651] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.
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Affiliation(s)
- Xinze Xu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
| | - Lan Lin
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shen Sun
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
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65
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Zuo Q, Shen Y, Zhong N, Chen CLP, Lei B, Wang S. Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4601-4612. [PMID: 37971911 DOI: 10.1109/tnsre.2023.3333952] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively fuse the correlated and complementary information from multimodal neuroimages. In this work, a novel model termed cross-modal transformer generative adversarial network (CT-GAN) is proposed to effectively fuse the functional and structural information contained in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner. Moreover, the swapping bi-attention mechanism is designed to gradually align common features and effectively enhance the complementary features between modalities. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively. The proposed model also provides new insights into detecting AD-related abnormal neural circuits.
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66
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Ren X, Dong B, Luan Y, Wu Y, Huang Y, the Alzheimer's Disease Neuroimaging Initiative. 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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67
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Zuo Q, Zhong N, Pan Y, Wu H, Lei B, Wang S. Brain Structure-Function Fusing Representation Learning Using Adversarial Decomposed-VAE for Analyzing MCI. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4017-4028. [PMID: 37815971 DOI: 10.1109/tnsre.2023.3323432] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse structural and functional features in exploring the complex brain network. In this paper, a novel brain structure-function fusing-representation learning (BSFL) model is proposed to effectively learn fused representation from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the decomposition-fusion framework is developed to first decompose the feature space into the union of the uniform and unique spaces for each modality, and then adaptively fuse the decomposed features to learn MCI-related representation. Moreover, a knowledge-aware transformer module is designed to automatically capture local and global connectivity features throughout the brain. Also, a uniform-unique contrastive loss is further devised to make the decomposition more effective and enhance the complementarity of structural and functional features. The extensive experiments demonstrate that the proposed model achieves better performance than other competitive methods in predicting and analyzing MCI. More importantly, the proposed model could be a potential tool for reconstructing unified brain networks and predicting abnormal connections during the degenerative processes in MCI.
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68
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He Y, Li Q, Fu Z, Zeng D, Han Y, Li S. Functional gradients reveal altered functional segregation in patients with amnestic mild cognitive impairment and Alzheimer's disease. Cereb Cortex 2023; 33:10836-10847. [PMID: 37718155 DOI: 10.1093/cercor/bhad328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/26/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
Alzheimer's disease and amnestic mild cognitive impairment are associated with disrupted functional organization in brain networks, involved with alteration of functional segregation. Connectome gradients are a new tool representing brain functional topological organization to smoothly capture the human macroscale hierarchy. Here, we examined altered topological organization in amnestic mild cognitive impairment and Alzheimer's disease by connectome gradient mapping. We further quantified functional segregation by gradient dispersion. Then, we systematically compared the alterations observed in amnestic mild cognitive impairment and Alzheimer's disease patients with those in normal controls in a two-dimensional functional gradient space from both the whole-brain level and module level. Compared with normal controls, the first gradient, which described the neocortical hierarchy from unimodal to transmodal regions, showed a more distributed and significant suppression in Alzheimer's disease than amnestic mild cognitive impairment patients. Furthermore, gradient dispersion showed significant decreases in Alzheimer's disease at both the global level and module level, whereas this alteration was limited only to limbic areas in amnestic mild cognitive impairment. Notably, we demonstrated that suppressed gradient dispersion in amnestic mild cognitive impairment and Alzheimer's disease was associated with cognitive scores. These findings provide new evidence for altered brain hierarchy in amnestic mild cognitive impairment and Alzheimer's disease, which strengthens our understanding of the progressive mechanism of cognitive decline.
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Affiliation(s)
- Yirong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhenrong Fu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- Biomedical Engineering Institute, Hainan University, Haikou 570228, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100050, China
- National Clinical Research Center for Geriatric Disorders, Beijing 100053, China
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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Cui L, Zhang Z, Huang YL, Xie F, Guan YH, Lo CYZ, Guo YH, Jiang JH, Guo QH. Brain amyloid-β deposition associated functional connectivity changes of ultra-large structural scale in mild cognitive impairment. Brain Imaging Behav 2023; 17:494-506. [PMID: 37188840 DOI: 10.1007/s11682-023-00780-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
In preclinical Alzheimer's disease, neuro-functional changes due to amyloid-β (Aβ) deposition are not synchronized in different brain lobes and subcortical nuclei. This study aimed to explore the correlation between brain Aβ burden, connectivity changes in an ultra-large structural scale, and cognitive function in mild cognitive impairment. Participants with mild cognitive impairment were recruited and underwent florbetapir (F18-AV45) PET, resting-state functional MRI, and multidomain neuropsychological tests. AV-45 standardized uptake value ratio (SUVR) and functional connectivity of all participants were calculated. Of the total 144 participants, 72 were put in the low Aβ burden group and 72 in the high Aβ burden group. In the low Aβ burden group, all connectivities between lobes and nuclei had no correlation with SUVR. In the high Aβ burden group, SUVR showed negative correlations with the Subcortical-Occipital connectivity (r=-0.36, P = 0.02) and Subcortical-Parietal connectivity (r=-0.26, P = 0.026). Meanwhile, in the high Aβ burden group, SUVR showed positive correlations with the Temporal-Prefrontal connectivity (r = 0.27, P = 0.023), Temporal-Occipital connectivity (r = 0.24, P = 0.038), and Temporal-Parietal connectivity (r = 0.32, P = 0.006). Subcortical to Occipital and Parietal connectivities had positive correlations with general cognition, language, memory, and executive function. Temporal to Prefrontal, Occipital, and Parietal connectivities had negative correlations with memory function, executive function, and visuospatial function, and a positive correlation with language function. In conclusion, Individuals with mild cognitive impairment with high Aβ burden have Aβ-related bidirectional functional connectivity changes between lobes and subcortical nuclei that are associated with cognitive decline in multiple domains. These connectivity changes reflect neurological impairment and failed compensation.
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Affiliation(s)
- Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Zhen Zhang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Yan-Lu Huang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200040, China
| | - Yi-Hui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200040, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yi-Han Guo
- Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Jie-Hui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China.
| | - Qi-Hao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
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Coughlan G, DeSouza B, Zhukovsky P, Hornberger M, Grady C, Buckley RF. Spatial cognition is associated with levels of phosphorylated-tau and β-amyloid in clinically normal older adults. Neurobiol Aging 2023; 130:124-134. [PMID: 37506550 DOI: 10.1016/j.neurobiolaging.2023.06.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 06/23/2023] [Accepted: 06/24/2023] [Indexed: 07/30/2023]
Abstract
Spatial cognition is associated with Alzheimer's disease (AD) biomarkers in the symptomatic stages of the disease. We investigated whether cerebrospinal fluid (CSF) biomarkers (phosphorylated-tau [p-tau] and β-amyloid) are associated with poorer spatial cognition in clinically normal older adults. Participants were 1875 clinically normal adults (age 67.8 [8.5] years) from the European Prevention of Alzheimer's Dementia Consortium. Mixed effect models assessed the cross-sectional association between p-tau181, β-amyloid1-42 (Aβ1-42) and p-tau181/Aβ1-42 ratio and spatial cognition measured using semi-automated Supermarket Task and the 4 Mountains Task. Levels of p-tau181, Aβ1-42, and p-tau181/Aβ1-42 ratio were significantly associated with spatial cognition scores on both tasks. The p-tau181/Aβ1-42 ratio showed the largest effect sizes (β = -0.04/0.05, p < 0.001). Lower entorhinal cortical volume was associated with poorer outcomes on both tasks (β = 0.06, p < 0.002) and accounted for 18%-22% of the direct association between p-tau181 and spatial cognition scores. In conclusion, degeneration of the entorhinal cortex mediates a significant proportion of the association between p-tau181 and spatial assessments in cognitively normal adults. Future studies should focus on increasing the sensitivity of digital spatial assessments.
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Affiliation(s)
- Gillian Coughlan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Brennan DeSouza
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Mental Health and Addiction, Toronto, Ontario, Canada
| | - Michael Hornberger
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Cheryl Grady
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada; Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA; Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia.
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71
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Gao X, Liu H, Shi F, Shen D, Liu M. Brain Status Transferring Generative Adversarial Network for Decoding Individualized Atrophy in Alzheimer's Disease. IEEE J Biomed Health Inform 2023; 27:4961-4970. [PMID: 37607152 DOI: 10.1109/jbhi.2023.3304388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Deep learning has been widely investigated in brain image computational analysis for diagnosing brain diseases such as Alzheimer's disease (AD). Most of the existing methods built end-to-end models to learn discriminative features by group-wise analysis. However, these methods cannot detect pathological changes in each subject, which is essential for the individualized interpretation of disease variances and precision medicine. In this article, we propose a brain status transferring generative adversarial network (BrainStatTrans-GAN) to generate corresponding healthy images of patients, which are further used to decode individualized brain atrophy. The BrainStatTrans-GAN consists of generator, discriminator, and status discriminator. First, a normative GAN is built to generate healthy brain images from normal controls. However, it cannot generate healthy images from diseased ones due to the lack of paired healthy and diseased images. To address this problem, a status discriminator with adversarial learning is designed in the training process to produce healthy brain images for patients. Then, the residual between the generated and input images can be computed to quantify pathological brain changes. Finally, a residual-based multi-level fusion network (RMFN) is built for more accurate disease diagnosis. Compared to the existing methods, our method can model individualized brain atrophy for facilitating disease diagnosis and interpretation. Experimental results on T1-weighted magnetic resonance imaging (MRI) data of 1,739 subjects from three datasets demonstrate the effectiveness of our method.
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72
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Aleksandrova Y, Neganova M. Deciphering the Mysterious Relationship between the Cross-Pathogenetic Mechanisms of Neurodegenerative and Oncological Diseases. Int J Mol Sci 2023; 24:14766. [PMID: 37834214 PMCID: PMC10573395 DOI: 10.3390/ijms241914766] [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: 08/10/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The relationship between oncological pathologies and neurodegenerative disorders is extremely complex and is a topic of concern among a growing number of researchers around the world. In recent years, convincing scientific evidence has accumulated that indicates the contribution of a number of etiological factors and pathophysiological processes to the pathogenesis of these two fundamentally different diseases, thus demonstrating an intriguing relationship between oncology and neurodegeneration. In this review, we establish the general links between three intersecting aspects of oncological pathologies and neurodegenerative disorders, i.e., oxidative stress, epigenetic dysregulation, and metabolic dysfunction, examining each process in detail to establish an unusual epidemiological relationship. We also focus on reviewing the current trends in the research and the clinical application of the most promising chemical structures and therapeutic platforms that have a modulating effect on the above processes. Thus, our comprehensive analysis of the set of molecular determinants that have obvious cross-functional pathways in the pathogenesis of oncological and neurodegenerative diseases can help in the creation of advanced diagnostic tools and in the development of innovative pharmacological strategies.
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Affiliation(s)
- Yulia Aleksandrova
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, 142432 Chernogolovka, Russia;
| | - Margarita Neganova
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, 142432 Chernogolovka, Russia;
- Arbuzov Institute of Organic and Physical Chemistry, FRC Kazan Scientific Center, Russian Academy of Sciences, 420088 Kazan, Russia
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73
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Cassady KE, Chen X, Adams JN, Harrison TM, Zhuang K, Maass A, Baker S, Jagust W. Effect of Alzheimer's Pathology on Task-Related Brain Network Reconfiguration in Aging. J Neurosci 2023; 43:6553-6563. [PMID: 37604690 PMCID: PMC10513069 DOI: 10.1523/jneurosci.0023-23.2023] [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: 01/04/2023] [Revised: 08/02/2023] [Accepted: 08/06/2023] [Indexed: 08/23/2023] Open
Abstract
Large-scale brain networks undergo widespread changes with older age and in neurodegenerative diseases such as Alzheimer's disease (AD). Research in young adults (YA) suggest that the underlying functional architecture of brain networks remains relatively consistent between rest and task states. However, it remains unclear whether the same is true in aging and to what extent any changes may be related to accumulation of AD pathology such as β-amyloid (Aβ) and tau. Here, we examined age-related differences in functional connectivity (FC) between rest and an object-scene mnemonic discrimination task using fMRI in young and older adults (OA; both females and males). We used an a priori episodic memory network (EMN) parcellation scheme associated with object and scene processing, that included anterior-temporal regions and posterior-medial regions. We also used positron emission topography to measure Aβ and tau in older adults. The correlation between rest and task FC (i.e., FC similarity) was reduced in older compared with younger adults. Older adults with lower FC similarity in EMN had higher levels of tau in the same EMN regions and performed worse during object, but not scene, trials during the fMRI task. These findings link AD pathology, particularly tau, to a less stable functional architecture in memory networks. They also suggest that smaller changes in FC organization between rest and task states may facilitate better performance in older age. Interpretations are limited by methodological factors related to different acquisition directions and durations between rest and task scans.SIGNIFICANCE STATEMENT The brain's large-scale network organization is relatively consistent between rest and task states in young adults (YA). We found that memory networks in older adults (OA) were less correlated between rest and (memory) task states compared with young adults. Older adults with less correlated brain networks also had higher levels of Alzheimer's disease (AD) pathology in the same regions, suggesting that a less stable network architecture may reflect the early evolution of AD. Older adults with less correlated brain networks also performed worse during the memory task suggesting that more similar network organization between rest and task states may facilitate better performance in older age.
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Affiliation(s)
- Kaitlin E Cassady
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Xi Chen
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Jenna N Adams
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Kailin Zhuang
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Anne Maass
- German Center for Neurodegenerative Disease, 39120 Magdeburg, Germany
| | - Suzanne Baker
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - William Jagust
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
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74
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Hrybouski S, Das SR, Xie L, Wisse LEM, Kelley M, Lane J, Sherin M, DiCalogero M, Nasrallah I, Detre J, Yushkevich PA, Wolk DA. Aging and Alzheimer's disease have dissociable effects on local and regional medial temporal lobe connectivity. Brain Commun 2023; 5:fcad245. [PMID: 37767219 PMCID: PMC10521906 DOI: 10.1093/braincomms/fcad245] [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: 03/21/2023] [Revised: 08/06/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Functional disruption of the medial temporal lobe-dependent networks is thought to underlie episodic memory deficits in aging and Alzheimer's disease. Previous studies revealed that the anterior medial temporal lobe is more vulnerable to pathological and neurodegenerative processes in Alzheimer's disease. In contrast, cognitive and structural imaging literature indicates posterior, as opposed to anterior, medial temporal lobe vulnerability in normal aging. However, the extent to which Alzheimer's and aging-related pathological processes relate to functional disruption of the medial temporal lobe-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity alterations in the medial temporal lobe and its immediate functional neighbourhood-the Anterior-Temporal and Posterior-Medial brain networks-in normal agers, individuals with preclinical Alzheimer's disease and patients with Mild Cognitive Impairment or mild dementia due to Alzheimer's disease. In the Anterior-Temporal network and in the perirhinal cortex, in particular, we observed an inverted 'U-shaped' relationship between functional connectivity and Alzheimer's stage. According to our results, the preclinical phase of Alzheimer's disease is characterized by increased functional connectivity between the perirhinal cortex and other regions of the medial temporal lobe, as well as between the anterior medial temporal lobe and its one-hop neighbours in the Anterior-Temporal system. This effect is no longer present in symptomatic Alzheimer's disease. Instead, patients with symptomatic Alzheimer's disease displayed reduced hippocampal connectivity within the medial temporal lobe as well as hypoconnectivity within the Posterior-Medial system. For normal aging, our results led to three main conclusions: (i) intra-network connectivity of both the Anterior-Temporal and Posterior-Medial networks declines with age; (ii) the anterior and posterior segments of the medial temporal lobe become increasingly decoupled from each other with advancing age; and (iii) the posterior subregions of the medial temporal lobe, especially the parahippocampal cortex, are more vulnerable to age-associated loss of function than their anterior counterparts. Together, the current results highlight evolving medial temporal lobe dysfunction in Alzheimer's disease and indicate different neurobiological mechanisms of the medial temporal lobe network disruption in aging versus Alzheimer's disease.
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Affiliation(s)
- Stanislau Hrybouski
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Diagnostic Radiology, Lund University, 221 00 Lund, Sweden
| | - Melissa Kelley
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jacqueline Lane
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica Sherin
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael DiCalogero
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
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75
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Lespinasse J, Dufouil C, Proust-Lima C. Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer's disease and related dementia. BMC Med Res Methodol 2023; 23:199. [PMID: 37670234 PMCID: PMC10478286 DOI: 10.1186/s12874-023-02009-0] [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: 01/27/2023] [Accepted: 08/04/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Alzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable. METHODS We developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed. RESULTS The estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of A[Formula: see text]. CONCLUSION By leveraging the available clinical diagnosis timing information, our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events. TRIAL REGISTRATION clinicaltrials.gov, NCT01926249. Registered on 16 August 2013.
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Affiliation(s)
- Jérémie Lespinasse
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, BPH, U1219, 33000, Bordeaux, France
- Inserm, CIC1401-EC, 33000, Bordeaux, France
- Pôle de santé publique, Centre Hospitalier Universitaire (CHU) de Bordeaux, 33000, Bordeaux, France
| | - Carole Dufouil
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, BPH, U1219, 33000, Bordeaux, France
- Inserm, CIC1401-EC, 33000, Bordeaux, France
- Pôle de santé publique, Centre Hospitalier Universitaire (CHU) de Bordeaux, 33000, Bordeaux, France
| | - Cécile Proust-Lima
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, BPH, U1219, 33000, Bordeaux, France.
- Inserm, CIC1401-EC, 33000, Bordeaux, France.
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76
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Stricker JL, Corriveau-Lecavalier N, Wiepert DA, Botha H, Jones DT, Stricker NH. Neural network process simulations support a distributed memory system and aid design of a novel computer adaptive digital memory test for preclinical and prodromal Alzheimer's disease. Neuropsychology 2023; 37:698-715. [PMID: 36037486 PMCID: PMC9971333 DOI: 10.1037/neu0000847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE Growing evidence supports the importance of learning as a central deficit in preclinical/prodromal Alzheimer's disease. The aims of this study were to conduct a series of neural network simulations to develop a functional understanding of a distributed, nonmodular memory system that can learn efficiently without interference. This understanding is applied to the development of a novel digital memory test. METHOD Simulations using traditional feed forward neural network architectures to learn simple logic problems are presented. The simulations demonstrate three limitations: (a) inefficiency, (b) an inability to learn problems consistently, and (c) catastrophic interference when given multiple problems. A new mirrored cascaded architecture is introduced to address these limitations, with support provided by a series of simulations. RESULTS The mirrored cascaded architecture demonstrates efficient and consistent learning relative to feed forward networks but also suffers from catastrophic interference. Addition of context values to add the capability of distinguishing features as part of learning eliminates the problem of interference in the mirrored cascaded, but not the feed forward, architectures. CONCLUSIONS A mirrored cascaded architecture addresses the limitations of traditional feed forward neural networks, provides support for a distributed memory system, and emphasizes the importance of context to avoid interference. These process models contributed to the design of a digital computer-adaptive word list learning test that places maximum stress on the capability to distinguish specific episodes of learning. Process simulations provide a useful method of testing models of brain function and contribute to new approaches to neuropsychological assessment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- John L. Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Nikki H. Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
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Yulug B, Ayyıldız B, Ayyıldız S, Sayman D, Salar AB, Cankaya S, Ozdemir Oktem E, Ozsimsek A, Kurt CC, Lakadamyalı H, Akturk A, Altay Ö, Hanoglu L, Velioglu HA, Mardinoglu A. Infection with COVID-19 is no longer a public emergency: But what about degenerative dementia? J Med Virol 2023; 95:e29072. [PMID: 37724347 DOI: 10.1002/jmv.29072] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/16/2023] [Accepted: 08/23/2023] [Indexed: 09/20/2023]
Abstract
Although no longer considered a public health threat, post-COVID cognitive syndrome continues to impact on a considerable proportion of individuals who were infected with COVID-19. Recent studies have also suggested that COVID may be represent a critical risk factor for the development of Alzheimer's disease (AD). We compared 17 COVID patients with 20 controls and evaluated the effects of COVID-19 on general cognitive performance, hippocampal volume, and connections using structural and seed-based connectivity analysis. We showed that COVID patients exhibited considerably worse cognitive functioning and increased hippocampal connectivity supported by the strong correlation between hippocampal connectivity and cognitive scores. Our findings of higher hippocampal connectivity with no observable hippocampal morphological changes even in mild COVID cases may be represent evidence of a prestructural compensatory mechanism for stimulating additional neuronal resources to combat cognitive dysfunction as recently shown for the prodromal stages of degenerative cognitive disorders. Our findings may be also important in light of recent data showing that other viral infections as well as COVID may constitute a critical risk factor for the development of AD. To our knowledge, this is the first study that investigated network differences in COVID patients, with a particular focus on compensatory hippocampal connectivity.
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Affiliation(s)
- Burak Yulug
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Behçet Ayyıldız
- Anatomy PhD Program, Graduate School of Health Sciences, Kocaeli University, Istanbul, Turkey
| | - Sevilay Ayyıldız
- Anatomy PhD Program, Graduate School of Health Sciences, Kocaeli University, Istanbul, Turkey
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dila Sayman
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Ali Behram Salar
- Department of Neuroscience, Faculty of Medicine, Istanbul Medipol University, Istanbul, Türkiye
| | - Seyda Cankaya
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Ece Ozdemir Oktem
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Ahmet Ozsimsek
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Cagla Ceren Kurt
- Department of Physiotherapy, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Hatice Lakadamyalı
- Department of Radiology, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Aynur Akturk
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Özlem Altay
- KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Lutfu Hanoglu
- Department of Neurology, Istanbul Medipol University, Istanbul, Turkey
| | - Halil Aziz Velioglu
- Department of Neuroscience, Faculty of Medicine, Istanbul Medipol University, Istanbul, Türkiye
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Adil Mardinoglu
- KTH-Royal Institute of Technology, Stockholm, Sweden
- King's College London, Faculty of Dentistry, London, UK
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78
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Hari E, Kizilates-Evin G, Kurt E, Bayram A, Ulasoglu-Yildiz C, Gurvit H, Demiralp T. Functional and structural connectivity in the Papez circuit in different stages of Alzheimer's disease. Clin Neurophysiol 2023; 153:33-45. [PMID: 37451080 DOI: 10.1016/j.clinph.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/12/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVE Alzheimer's disease (AD) is a progressive neurodegenerative continuum with memory impairment. We aimed to examine the detailed functional (FC) and structural connectivity (SC) pattern of the Papez circuit, known as the memory circuit, along the AD. METHODS MRI data of 15 patients diagnosed with AD dementia (ADD), 15 patients with the amnestic mild cognitive impairment (MCI), and 15 patients with subjective cognitive impairment were analyzed. The FC analyses were performed between main nodes of the Papez circuit, and the SC was quantified as fractional anisotropy (FA) of the main white matter pathways of the Papez circuit. RESULTS The FC between the retrosplenial (RSC) and parahippocampal cortices (PHC) was the earliest affected FC, while a manifest SC change in the ventral cingulum and fornix was observed in the later ADD stage. The RSC-PHC FC and the ventral cingulum FA efficiently predicted the memory performance of the non-demented participants. CONCLUSIONS Our findings revealed the importance of the Papez circuit as target regions along the AD. SIGNIFICANCE The ventral cingulum connecting the RSC and PHC, a critical overlap area between the Papez circuit and the default mode network, seems to be a target region associated with the earliest objective memory findings in AD.
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Affiliation(s)
- Emre Hari
- Graduate School of Health Sciences, Istanbul University, 34216 Istanbul, Turkey; Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Gozde Kizilates-Evin
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Elif Kurt
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Ali Bayram
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Cigdem Ulasoglu-Yildiz
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Hakan Gurvit
- Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey; Department of Neurology, Behavioral Neurology and Movement Disorders Unit, Istanbul Faculty of Medicine, Istanbul University, 34093 Istanbul, Turkey.
| | - Tamer Demiralp
- Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey; Department of Physiology, Istanbul Faculty of Medicine, Istanbul University, 34093 Istanbul, Turkey.
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79
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Karaca O, Tepe N, Ozcan E. Evaluation of volumetric asymmetry of the dorsolateral prefrontal cortex and medial temporal lobe in Alzheimer's disease using the atlas-based method. Neuroreport 2023; 34:592-597. [PMID: 37384935 DOI: 10.1097/wnr.0000000000001930] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Brain areas affected during neurodegenerative disease progression are considered anatomically connected to the first affected areas. The dorsolateral prefrontal cortex (DLPFC) has connections with the medial temporal lobe (MTL), which includes regions that become atrophic in Alzheimer's disease. In this study, we aimed to investigate the degree of volumetric asymmetry of DLPFC and MTL structures. This is a cross-sectional volumetric study involving 25 Alzheimer's disease patients and 25 healthy adults who underwent MRI with a 3D turbo spin echo sequence at 1.5 Tesla. The atlas-based method incorporated MRIStudio software to automatically measure the volume of brain structures. We compared the asymmetry index and volumetric changes across study groups and correlated them with Mini-Mental State Examination scores. We observed significant volumetric rightward lateralization in the DLPFC and superior frontal gyrus in Alzheimer's disease patients compared to the healthy controls. There was a significant volume loss in the MTL structures of Alzheimer's disease patients. Atrophy of MTL structures was positively correlated with right DLPFC volume changes in Alzheimer's disease patients. Volumetric asymmetry of the DLPFC may be a characteristic for determining disease progression in Alzheimer's disease patients. Future studies are recommended to evaluate whether these volumetric asymmetrical changes are specific to Alzheimer's disease and whether asymmetry measurements can serve as diagnostic markers.
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Affiliation(s)
| | - Nermin Tepe
- Department of Neurology, Faculty of Medicine, Balikesir University, Balikesir, Turkey
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Cheng Y, Zhai Y, Yuan Y, Li H, Zhao W, Fan Z, Zhou L, Gao X, Zhan Y, Sun H. Xenon inhalation attenuates neuronal injury and prevents epilepsy in febrile seizure Sprague-Dawley pups. Front Cell Neurosci 2023; 17:1155303. [PMID: 37645594 PMCID: PMC10461106 DOI: 10.3389/fncel.2023.1155303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 07/27/2023] [Indexed: 08/31/2023] Open
Abstract
Background Febrile seizures (FS) usually occur in childhood and may cause irreversible neuronal damage, cognitive functional defects, and an increase in the risk of epilepsy later in life. Anti-epileptic drugs (AEDs), currently used to treat FS in children, can relieve seizures. However, their effects in preventing the risk of developing epilepsy in later life are unsatisfactory. Moreover, AEDs may damage child brain development. Here, we evaluated the efficiency of xenon in treating prolonged FS (PFS) and preventing epilepsy in Sprague-Dawley pups. Methods Prolonged FS was induced by hyperthermic treatment. After 90 min of PFS, the pups in the xenon treatment group were immediately treated with 70% xenon/21% oxygen/9% nitrogen for 60 min. The levels of glutamate, mitochondrial oxidative stress, mitophagy, and neuronal injury, seizures, learning, and memory functions were measured at specific time points. Results Neonatal period PFS led to spontaneous seizure, learning and memory dysfunction, accompanied by increased levels of glutamate, mitochondrial oxidative stress, mitophagy, and neuronal injury. Xenon treatment alleviated the changes caused by PFS and reduced the risk of PFS developing into epilepsy later. Conclusion Our results suggest that xenon inhalation could be a potential therapeutic strategy to attenuate neuronal injury and prevent epilepsy in patients with FS.
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Affiliation(s)
- Yao Cheng
- School of Pharmaceutical Sciences, Binzhou Medical University, Yantai, China
| | - Yujie Zhai
- School of Pharmaceutical Sciences, Binzhou Medical University, Yantai, China
| | - Yi Yuan
- School of Pharmaceutical Sciences, Binzhou Medical University, Yantai, China
| | - Hao Li
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Wenke Zhao
- School of Pharmaceutical Sciences, Binzhou Medical University, Yantai, China
| | - Zhenhai Fan
- School of Pharmaceutical Sciences, Binzhou Medical University, Yantai, China
| | - Ling Zhou
- School of Pharmaceutical Sciences, Binzhou Medical University, Yantai, China
| | - Xue Gao
- School of Pharmaceutical Sciences, Binzhou Medical University, Yantai, China
| | - Yan Zhan
- Department of Neurology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Hongliu Sun
- School of Pharmaceutical Sciences, Binzhou Medical University, Yantai, China
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Yao S, Zhu Q, Zhang Q, Cai Y, Liu S, Pang L, Jing Y, Yin X, Cheng H. Managing Cancer and Living Meaningfully (CALM) alleviates chemotherapy related cognitive impairment (CRCI) in breast cancer survivors: A pilot study based on resting-state fMRI. Cancer Med 2023; 12:16231-16242. [PMID: 37409628 PMCID: PMC10469649 DOI: 10.1002/cam4.6285] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 06/12/2023] [Accepted: 06/14/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Chemotherapy related cognitive impairment (CRCI) is a type of memory and cognitive impairment induced by chemotherapy and has become a growing clinical problem. Breast cancer survivors (BCs) refer to patients from the moment of breast cancer diagnosis to the end of their lives. Managing Cancer and Living Meaningfully (CALM) is a convenient and easy-to-apply psychological intervention that has been proven to improve quality of life and alleviate CRCI in BCs. However, the underlying neurobiological mechanisms remain unclear. Resting-state functional magnetic resonance imaging (rs-fMRI) has become an effective method for understanding the neurobiological mechanisms of brain networks in CRCI. The fractional amplitude of low-frequency fluctuations (fALFF) and ALFF have often been used in analyzing the power and intensity of spontaneous regional resting state neural activity. METHODS The recruited BCs were randomly divided into the CALM group and the care as usual (CAU) group. All BCs were evaluated by the Functional Assessment of Cancer Therapy Cognitive Function (FACT-Cog) before and after CALM or CAU. The rs-fMRI imaging was acquired before and after CALM intervention in CALM group BCs. The BCs were defined as before CALM intervention (BCI) group and after CALM intervention (ACI) group. RESULTS There were 32 BCs in CALM group and 35 BCs in CAU group completed the overall study. There were significant differences between the BCI group and the ACI group in the FACT-Cog-PCI scores. Compared with the BCI group, the ACI group showed lower fALFF signal in the left medial frontal gyrus and right sub-gyral and higher fALFF in the left occipital_sup and middle occipital gyrus. There was a significant positive correlation between hippocampal ALFF value and FACT-Cog-PCI scores. CONCLUSIONS CALM intervention may have an effective function in alleviating CRCI of BCs. The altered local synchronization and regional brain activity may be correlated with the improved cognitive function of BCs who received the CALM intervention. The ALFF value of hippocampus seems to be an important factor in reflect cognitive function in BCs with CRCI and the neural network mechanism of CALM intervention deserves further exploration to promote its application.
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Affiliation(s)
- Senbang Yao
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Qinqin Zhu
- Department of RadiologyQuzhou People's HospitalQuzhouChina
| | - Qianqian Zhang
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Yinlian Cai
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Shaochun Liu
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Lulian Pang
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Yanyan Jing
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Xiangxiang Yin
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Huaidong Cheng
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Shenzhen Clinical Medical School of Southern Medical UniversityShenzhenChina
- Department of OncologyShenzhen Hospital of Southern Medical UniversityShenzhenChina
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Liu Z, Shu K, Geng Y, Cai C, Kang H. Deep brain stimulation of fornix in Alzheimer's disease: From basic research to clinical practice. Eur J Clin Invest 2023; 53:e13995. [PMID: 37004153 DOI: 10.1111/eci.13995] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/13/2023] [Accepted: 03/23/2023] [Indexed: 04/03/2023]
Abstract
Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases associated with the degradation of memory and cognitive ability. Current pharmacotherapies show little therapeutic effect in AD treatment and still cannot prevent the pathological progression of AD. Deep brain stimulation (DBS) has shown to enhance memory in morbid obese, epilepsy and traumatic brain injury patients, and cognition in Parkinson's disease (PD) patients deteriorates during DBS off. Some relevant animal studies and clinical trials have been carried out to discuss the DBS treatment for AD. Reviewing the fornix trials, no unified conclusion has been reached about the clinical benefits of DBS in AD, and the dementia ratings scale has not been effectively improved in the long term. However, some patients have presented promising results, such as improved glucose metabolism, increased connectivity in cognition-related brain regions and even elevated cognitive function rating scale scores. The fornix plays an important regulatory role in memory, attention, and emotion through its complex fibre projection to cognition-related structures, making it a promising target for DBS for AD treatment. Moreover, the current stereotaxic technique and various evaluation methods have provided references for the operator to select accurate stimulation points. Related adverse events and relatively higher costs in DBS have been emphasized. In this article, we summarize and update the research progression on fornix DBS in AD and seek to provide a reliable reference for subsequent experimental studies on DBS treatment of AD.
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Affiliation(s)
- Zhikun Liu
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Kai Shu
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Yumei Geng
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Chang Cai
- National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei Province, China
| | - Huicong Kang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
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Ramsden CE, Zamora D, Horowitz M, Jahanipour J, Keyes G, Li X, Murray HC, Curtis MA, Faull RM, Sedlock A, Maric D. ApoER2-Dab1 disruption as the origin of pTau-related neurodegeneration in sporadic Alzheimer's disease. RESEARCH SQUARE 2023:rs.3.rs-2968020. [PMID: 37461602 PMCID: PMC10350181 DOI: 10.21203/rs.3.rs-2968020/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
BACKGROUND Sporadic Alzheimer's disease (sAD) is not a global brain disease. Specific regions, layers and neurons degenerate early while others remain untouched even in advanced disease. The prevailing model used to explain this selective neurodegeneration-prion-like Tau spread-has key limitations and is not easily integrated with other defining sAD features. Instead, we propose that in humans Tau hyperphosphorylation occurs locally via disruption in ApoER2-Dab1 signaling and thus the presence of ApoER2 in neuronal membranes confers vulnerability to degeneration. Further, we propose that disruption of the Reelin/ApoE/ApoJ-ApoER2-Dab1-P85α-LIMK1-Tau-PSD95 (RAAAD-P-LTP) pathway induces deficits in memory and cognition by impeding neuronal lipoprotein internalization and destabilizing actin, microtubules, and synapses. This new model is based in part on our recent finding that ApoER2-Dab1 disruption is evident in entorhinal-hippocampal terminal zones in sAD. Here, we hypothesized that neurons that degenerate in the earliest stages of sAD (1) strongly express ApoER2 and (2) show evidence of ApoER2-Dab1 disruption through co-accumulation of multiple RAAAD-P-LTP components. METHODS We applied in situ hybridization and immunohistochemistry to characterize ApoER2 expression and accumulation of RAAAD-P-LTP components in five regions that are prone to early pTau pathology in 64 rapidly autopsied cases spanning the clinicopathological spectrum of sAD. RESULTS We found that: (1) selectively vulnerable neuron populations strongly express ApoER2; (2) numerous RAAAD-P-LTP pathway components accumulate in neuritic plaques and abnormal neurons; and (3) RAAAD-P-LTP components were higher in MCI and sAD cases and correlated with histological progression and cognitive deficits. Multiplex-IHC revealed that Dab1, pP85αTyr607, pLIMK1Thr508, pTau and pPSD95Thr19 accumulated together within dystrophic dendrites and soma of ApoER2-expressing neurons in the vicinity of ApoE/ApoJ-enriched extracellular plaques. These observations provide evidence for molecular derangements that can be traced back to ApoER2-Dab1 disruption, in each of the sampled regions, layers, and neuron populations that are prone to early pTau pathology. CONCLUSION Findings support the RAAAD-P-LTP hypothesis, a unifying model that implicates dendritic ApoER2-Dab1 disruption as the major driver of both pTau accumulation and neurodegeneration in sAD. This model provides a new conceptual framework to explain why specific neurons degenerate and identifies RAAAD-P-LTP pathway components as potential mechanism-based biomarkers and therapeutic targets for sAD.
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Affiliation(s)
| | - Daisy Zamora
- National Institute on Aging Laboratory of Clinical Investigation
| | - Mark Horowitz
- National Institute on Aging Intramural Research Program
| | | | - Gregory Keyes
- National Institute on Aging Laboratory of Clinical Investigation
| | - Xiufeng Li
- National Institute on Aging Laboratory of Clinical Investigation
| | - Helen C Murray
- The University of Auckland Faculty of Medical and Health Sciences
| | - Maurice A Curtis
- The University of Auckland Faculty of Medical and Health Sciences
| | - Richard M Faull
- The University of Auckland Faculty of Medical and Health Sciences
| | - Andrea Sedlock
- NINDS: National Institute of Neurological Disorders and Stroke
| | - Dragan Maric
- NINDS: National Institute of Neurological Disorders and Stroke
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Papay RS, Stauffer SR, Perez DM. A PAM of the α 1A-Adrenergic receptor rescues biomarker, long-term potentiation, and cognitive deficits in Alzheimer's disease mouse models without effects on blood pressure. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2023; 5:100160. [PMID: 37448695 PMCID: PMC10336260 DOI: 10.1016/j.crphar.2023.100160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 05/30/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
α1-Adrenergic Receptors (ARs) regulate the sympathetic nervous system by the binding of norepinephrine (NE) and epinephrine (Epi) through different subtypes (α1A, α1B, α1D). α1A-AR activation is hypothesized to be memory forming and cognitive enhancing but drug development has been stagnant due to unwanted side effects on blood pressure. We recently reported the pharmacological characterization of the first positive allosteric modulator (PAM) for the α1A-AR with predictive pro-cognitive and memory properties. In this report, we now demonstrate the in vivo characteristics of Compound 3 (Cmpd-3) in two genetically-different Alzheimer's Disease (AD) mouse models. Drug metabolism and pharmacokinetic studies indicate sufficient brain penetrance and rapid uptake into the brain with low to moderate clearance, and a favorable inhibition profile against the major cytochrome p450 enzymes. Oral administration of Cmpd-3 (3-9 mg/kg QD) can fully rescue long-term potentiation defects and AD biomarker profile (amyloid β-40, 42) within 3 months of dosing to levels that were non-significant from WT controls and which outperformed donepezil (1 mg/kg QD). There were also significant effects on paired pulse facilitation and cognitive behavior. Long-term and high-dose in vivo studies with Cmpd-3 revealed no effects on blood pressure. Our results suggest that Cmpd-3 can maintain lasting therapeutic levels and efficacy with disease modifying effects with a once per day dosing regimen in AD mouse models with no observed side effects.
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Affiliation(s)
- Robert S. Papay
- The Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, The Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH, 44195, USA
| | - Shaun R. Stauffer
- Center of Therapeutics Discovery, Lerner Research Institute, The Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, Ohio, 44195, USA
| | - Dianne M. Perez
- The Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, The Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH, 44195, USA
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Martinez Villar G, Daneault V, Martineau-Dussault MÈ, Baril AA, Gagnon K, Lafond C, Gilbert D, Thompson C, Marchi NA, Lina JM, Montplaisir J, Carrier J, Gosselin N, André C. Altered resting-state functional connectivity patterns in late middle-aged and older adults with obstructive sleep apnea. Front Neurol 2023; 14:1215882. [PMID: 37470008 PMCID: PMC10353887 DOI: 10.3389/fneur.2023.1215882] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/05/2023] [Indexed: 07/21/2023] Open
Abstract
Introduction Obstructive sleep apnea (OSA) is increasingly recognized as a risk factor for cognitive decline, and has been associated with structural brain alterations in regions relevant to memory processes and Alzheimer's disease. However, it is unclear whether OSA is associated with disrupted functional connectivity (FC) patterns between these regions in late middle-aged and older populations. Thus, we characterized the associations between OSA severity and resting-state FC between the default mode network (DMN) and medial temporal lobe (MTL) regions. Second, we explored whether significant FC changes differed depending on cognitive status and were associated with cognitive performance. Methods Ninety-four participants [24 women, 65.7 ± 6.9 years old, 41% with Mild Cognitive Impairment (MCI)] underwent a polysomnography, a comprehensive neuropsychological assessment and a resting-state functional magnetic resonance imaging (MRI). General linear models were conducted between OSA severity markers (i.e., the apnea-hypopnea, oxygen desaturation and microarousal indices) and FC values between DMN and MTL regions using CONN toolbox. Partial correlations were then performed between OSA-related FC patterns and (i) OSA severity markers in subgroups stratified by cognitive status (i.e., cognitively unimpaired versus MCI) and (ii) cognitive scores in the whole sample. All analyzes were controlled for age, sex and education, and considered significant at a p < 0.05 threshold corrected for false discovery rate. Results In the whole sample, a higher apnea-hypopnea index was significantly associated with lower FC between (i) the medial prefrontal cortex and bilateral hippocampi, and (ii) the left hippocampus and both the posterior cingulate cortex and precuneus. FC patterns were not associated with the oxygen desaturation index, or micro-arousal index. When stratifying the sample according to cognitive status, all associations remained significant in cognitively unimpaired individuals but not in the MCI group. No significant associations were observed between cognition and OSA severity or OSA-related FC patterns. Discussion OSA severity was associated with patterns of lower FC in regions relevant to memory processes and Alzheimer's disease. Since no associations were found with cognitive performance, these FC changes could precede detectable cognitive deficits. Whether these FC patterns predict future cognitive decline over the long-term needs to be investigated.
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Affiliation(s)
- Guillermo Martinez Villar
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Véronique Daneault
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
| | - Marie-Ève Martineau-Dussault
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Andrée-Ann Baril
- Douglas Mental Health Institute, McGill University, Montréal, QC, Canada
| | - Katia Gagnon
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Laboratory and Sleep Clinic, Hôpital en Santé Mentale Rivière-des-Prairies, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
| | - Chantal Lafond
- Department of Pulmonology, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Danielle Gilbert
- Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, QC, Canada
- Department of Radiology, Hopital du Sacré-Coeur de Montréal, CIUSSS du Nord-de-l'Ile-de, Montréal, QC, Canada
| | - Cynthia Thompson
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
| | - Nicola Andrea Marchi
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
- Center for Investigation and Research in Sleep, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Marc Lina
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Département de Génie Electrique, École de Technologie Supérieure, Montréal, QC, Canada
| | - Jacques Montplaisir
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychiatry, Université de Montréal, Montréal, QC, Canada
| | - Julie Carrier
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Nadia Gosselin
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Claire André
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
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Xue K, Chen J, Wei Y, Chen Y, Han S, Wang C, Zhang Y, Song X, Cheng J. Impaired large-scale cortico-hippocampal network connectivity, including the anterior temporal and posterior medial systems, and its associations with cognition in patients with first-episode schizophrenia. Front Neurosci 2023; 17:1167942. [PMID: 37342466 PMCID: PMC10277613 DOI: 10.3389/fnins.2023.1167942] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 06/23/2023] Open
Abstract
Background and objective The cortico-hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico-hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition. Methods A total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico-hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores. Results Compared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico-hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico-hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC). Conclusion Schizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico-hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia.
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Affiliation(s)
- Kangkang Xue
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
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Eslami M, Tabarestani S, Adjouadi M. A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease. Artif Intell Med 2023; 140:102543. [PMID: 37210151 PMCID: PMC10204620 DOI: 10.1016/j.artmed.2023.102543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/28/2023] [Accepted: 04/02/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to resolve in the last decade. This study introduces a first-of-its-kind color-coded visualization mechanism driven by an integrated ML model to predict disease trajectory in a 2-year longitudinal study. The main aim of this study is to help capture visually in 2D and 3D renderings the diagnosis and prognosis of AD, therefore augmenting our understanding of the processes of multiclass classification and regression analysis. METHOD The proposed method, Machine Learning for Visualizing AD (ML4VisAD), is designed to predict disease progression through a visual output. This newly developed model takes baseline measurements as input to generate a color-coded visual image that reflects disease progression at different time points. The architecture of the network relies on convolutional neural networks. With 1123 subjects selected from the ADNI QT-PAD dataset, we use a 10-fold cross-validation process to evaluate the method. Multimodal inputs* include neuroimaging data (MRI, PET), neuropsychological test scores (excluding MMSE, CDR-SB, and ADAS to avoid bias), cerebrospinal fluid (CSF) biomarkers with measures of amyloid beta (ABETA), phosphorylated tau protein (PTAU), total tau protein (TAU), and risk factors that include age, gender, years of education, and ApoE4 gene. FINDINGS/RESULTS Based on subjective scores reached by three raters, the results showed an accuracy of 0.82 ± 0.03 for a 3-way classification and 0.68 ± 0.05 for a 5-way classification. The visual renderings were generated in 0.08 msec for a 23 × 23 output image and in 0.17 ms for a 45 × 45 output image. Through visualization, this study (1) demonstrates that the ML visual output augments the prospects for a more accurate diagnosis and (2) highlights why multiclass classification and regression analysis are incredibly challenging. An online survey was conducted to gauge this visualization platform's merits and obtain valuable feedback from users. All implementation codes are shared online on GitHub. CONCLUSION This approach makes it possible to visualize the many nuances that lead to a specific classification or prediction in the disease trajectory, all in context to multimodal measurements taken at baseline. This ML model can serve as a multiclass classification and prediction model while reinforcing the diagnosis and prognosis capabilities by including a visualization platform.
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Affiliation(s)
- Mohammad Eslami
- Harvard Ophthalmology AI lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA; Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
| | - Solale Tabarestani
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
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Ramsden CE, Zamora D, Horowitz MS, Jahanipour J, Keyes GS, Li X, Murray HC, Curtis MA, Faull RM, Sedlock A, Maric D. ApoER2-Dab1 disruption as the origin of pTau-related neurodegeneration in sporadic Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.19.23290250. [PMID: 37333406 PMCID: PMC10274982 DOI: 10.1101/2023.05.19.23290250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
BACKGROUND Sporadic Alzheimer's disease (sAD) is not a global brain disease. Specific regions, layers and neurons degenerate early while others remain untouched even in advanced disease. The prevailing model used to explain this selective neurodegeneration-prion-like Tau spread-has key limitations and is not easily integrated with other defining sAD features. Instead, we propose that in humans Tau hyperphosphorylation occurs locally via disruption in ApoER2-Dab1 signaling and thus the presence of ApoER2 in neuronal membranes confers vulnerability to degeneration. Further, we propose that disruption of the Reelin/ApoE/ApoJ-ApoER2-Dab1-P85α-LIMK1-Tau-PSD95 (RAAAD-P-LTP) pathway induces deficits in memory and cognition by impeding neuronal lipoprotein internalization and destabilizing actin, microtubules, and synapses. This new model is based in part on our recent finding that ApoER2-Dab1 disruption is evident in entorhinal-hippocampal terminal zones in sAD. Here, we hypothesized that neurons that degenerate in the earliest stages of sAD (1) strongly express ApoER2 and (2) show evidence of ApoER2-Dab1 disruption through co-accumulation of multiple RAAAD-P-LTP components. METHODS We applied in situ hybridization and immunohistochemistry to characterize ApoER2 expression and accumulation of RAAAD-P-LTP components in five regions that are prone to early pTau pathology in 64 rapidly autopsied cases spanning the clinicopathological spectrum of sAD. RESULTS We found that: (1) selectively vulnerable neuron populations strongly express ApoER2; (2) numerous RAAAD-P-LTP pathway components accumulate in neuritic plaques and abnormal neurons; and (3) RAAAD-P-LTP components were higher in MCI and sAD cases and correlated with histological progression and cognitive deficits. Multiplex-IHC revealed that Dab1, pP85αTyr607, pLIMK1Thr508, pTau and pPSD95Thr19 accumulated together within dystrophic dendrites and soma of ApoER2-expressing neurons in the vicinity of ApoE/ApoJ-enriched extracellular plaques. These observations provide evidence for molecular derangements that can be traced back to ApoER2-Dab1 disruption, in each of the sampled regions, layers, and neuron populations that are prone to early pTau pathology. CONCLUSION Findings support the RAAAD-P-LTP hypothesis, a unifying model that implicates dendritic ApoER2-Dab1 disruption as the major driver of both pTau accumulation and neurodegeneration in sAD. This model provides a new conceptual framework to explain why specific neurons degenerate and identifies RAAAD-P-LTP pathway components as potential mechanism-based biomarkers and therapeutic targets for sAD.
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Affiliation(s)
- Christopher E. Ramsden
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH 251 Bayview Blvd., Baltimore, MD, 21224, USA
- Intramural Program of the National Institute on Alcohol Abuse and Alcoholism, NIH, Bethesda, MD, 20892, USA
| | - Daisy Zamora
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH 251 Bayview Blvd., Baltimore, MD, 21224, USA
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Mark S. Horowitz
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH 251 Bayview Blvd., Baltimore, MD, 21224, USA
| | - Jahandar Jahanipour
- Flow and Imaging Cytometry Core Facility, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, 20892, USA
| | - Gregory S. Keyes
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH 251 Bayview Blvd., Baltimore, MD, 21224, USA
| | - Xiufeng Li
- Lipid Peroxidation Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH 251 Bayview Blvd., Baltimore, MD, 21224, USA
| | - Helen C. Murray
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, University of Auckland, Private Bag, Auckland, 92019, New Zealand
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, 20892, USA
| | - Maurice A. Curtis
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, University of Auckland, Private Bag, Auckland, 92019, New Zealand
| | - Richard M. Faull
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, University of Auckland, Private Bag, Auckland, 92019, New Zealand
| | - Andrea Sedlock
- Flow and Imaging Cytometry Core Facility, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, 20892, USA
| | - Dragan Maric
- Flow and Imaging Cytometry Core Facility, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, 20892, USA
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Miranda M, Giachero M, Weisstaub NV, Morici JF. Editorial: Updates on memory modulation in health and disease. Front Behav Neurosci 2023; 17:1205371. [PMID: 37214642 PMCID: PMC10193039 DOI: 10.3389/fnbeh.2023.1205371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Affiliation(s)
- Magdalena Miranda
- Institute of Functional Genomics, University of Montpellier, CNRS, Inserm, Montpellier, France
| | - Marcelo Giachero
- Laboratorio de Memoria y Cognición Molecular, Instituto de Neurociencia Cognitiva y Traslacional, Consejo Nacional de Investigaciones Científicas y Técnicas-Fundación INECO-Universidad Favaloro, Buenos Aires, Argentina
| | - Noelia V. Weisstaub
- Laboratorio de Memoria y Cognición Molecular, Instituto de Neurociencia Cognitiva y Traslacional, Consejo Nacional de Investigaciones Científicas y Técnicas-Fundación INECO-Universidad Favaloro, Buenos Aires, Argentina
| | - Juan Facundo Morici
- Institut du Fer a Moulin, UMR-S 1270, INSERM and Sorbonne Univerité, Paris, France
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90
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, et alBao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Show More Authors] [Citation(s) in RCA: 167] [Impact Index Per Article: 83.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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91
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Lin YR, Chi CH, Chang YL. Differential decay of gist and detail memory in older adults with amnestic mild cognitive impairment. Cortex 2023; 164:112-128. [PMID: 37207409 DOI: 10.1016/j.cortex.2023.04.002] [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: 09/20/2022] [Revised: 02/19/2023] [Accepted: 04/11/2023] [Indexed: 05/21/2023]
Abstract
Amnestic mild cognitive impairment (aMCI) has been identified as a risk factor for dementia due to Alzheimer's disease. The medial temporal structures, which are crucial for memory processing, are the earliest affected regions in the brains of patients with aMCI, and episodic memory performance has been identified as a reliable way to discriminate between patients with aMCI and cognitively normal older adults. However, whether the detail and gist memory of patients with aMCI and cognitively normal older adults decay differently remains unclear. In this study, we hypothesized that detail and gist memory would be retrieved differentially, with a larger group performance gap in detail memory than in gist memory. In addition, we explored whether an increasing group performance gap between detail memory and gist memory groups would be observed over a 14-day period. Furthermore, we hypothesized that unisensory (audio-only) and multisensory (audiovisual) encoding would lead to differences in retrievals, with the multisensory condition reducing between and within-group performance gaps observed under the unisensory condition. The analyses conducted were analyses of covariance controlling for age, sex, and education and correlational analyses to examine behavioral performance and the association between behavioral data and brain variables. Compared with cognitively normal older adults, the patients with aMCI performed poorly on both detail and gist memory tests, and this performance gap persisted over time. Moreover, the memory performance of the patients with aMCI was enhanced by the provision of multisensory information, and bimodal input was significantly associated with medial temporal structure variables. Overall, our findings suggest that detail and gist memory decay differently, with a longer lasting group gap in gist memory than in detail memory. Multisensory encoding effectively reduced or overcame the between- and within-group gaps between time intervals, especially for gist memory, compared with unisensory encoding.
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Affiliation(s)
- Yu-Ruei Lin
- Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan
| | - Chia-Hsing Chi
- Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan
| | - Yu-Ling Chang
- Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan; Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan; Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan; Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan.
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92
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Veréb D, Mijalkov M, Chang YW, Canal-Garcia A, Gomez-Ruis E, Maass A, Villeneuve S, Volpe G, Pereira JB. Functional gradients of the medial parietal cortex in a healthy cohort with family history of sporadic Alzheimer's disease. Alzheimers Res Ther 2023; 15:82. [PMID: 37076873 PMCID: PMC10114342 DOI: 10.1186/s13195-023-01228-3] [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: 09/05/2022] [Accepted: 04/05/2023] [Indexed: 04/21/2023]
Abstract
BACKGROUND The medial parietal cortex is an early site of pathological protein deposition in Alzheimer's disease (AD). Previous studies have identified different subregions within this area; however, these subregions are often heterogeneous and disregard individual differences or subtle pathological alterations in the underlying functional architecture. To address this limitation, here we measured the continuous connectivity gradients of the medial parietal cortex and assessed their relationship with cerebrospinal fluid (CSF) biomarkers, ApoE ε4 carriership and memory in asymptomatic individuals at risk to develop AD. METHODS Two hundred sixty-three cognitively normal participants with a family history of sporadic AD who underwent resting-state and task-based functional MRI using encoding and retrieval tasks were included from the PREVENT-AD cohort. A novel method for characterizing spatially continuous patterns of functional connectivity was applied to estimate functional gradients in the medial parietal cortex during the resting-state and task-based conditions. This resulted in a set of nine parameters that described the appearance of the gradient across different spatial directions. We performed correlation analyses to assess whether these parameters were associated with CSF biomarkers of phosphorylated tau181 (p-tau), total tau (t-tau), and amyloid-ß1-42 (Aß). Then, we compared the spatial parameters between ApoE ε4 carriers and noncarriers, and evaluated the relationship between these parameters and memory. RESULTS Alterations involving the superior part of the medial parietal cortex, which was connected to regions of the default mode network, were associated with higher p-tau, t-tau levels as well as lower Aß/p-tau levels during the resting-state condition (p < 0.01). Similar alterations were found in ApoE ε4 carriers compared to non-carriers (p < 0.003). In contrast, lower immediate memory scores were associated with changes in the middle part of the medial parietal cortex, which was connected to inferior temporal and posterior parietal regions, during the encoding task (p = 0.001). No results were found when using conventional connectivity measures. CONCLUSIONS Functional alterations in the medial parietal gradients are associated with CSF AD biomarkers, ApoE ε4 carriership, and lower memory in an asymptomatic cohort with a family history of sporadic AD, suggesting that functional gradients are sensitive to subtle changes associated with early AD stages.
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Affiliation(s)
- Dániel Veréb
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden.
| | - Mite Mijalkov
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, Goteborg University, Goteborg, Sweden
| | - Anna Canal-Garcia
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden
| | | | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
| | - Sylvia Villeneuve
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg, Sweden
| | - Joana B Pereira
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden.
- Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
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93
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Zhang YY, Li XS, Ren KD, Peng J, Luo XJ. Restoration of metal homeostasis: a potential strategy against neurodegenerative diseases. Ageing Res Rev 2023; 87:101931. [PMID: 37031723 DOI: 10.1016/j.arr.2023.101931] [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: 01/31/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 04/11/2023]
Abstract
Metal homeostasis is critical to normal neurophysiological activity. Metal ions are involved in the development, metabolism, redox and neurotransmitter transmission of the central nervous system (CNS). Thus, disturbance of homeostasis (such as metal deficiency or excess) can result in serious consequences, including neurooxidative stress, excitotoxicity, neuroinflammation, and nerve cell death. The uptake, transport and metabolism of metal ions are highly regulated by ion channels. There is growing evidence that metal ion disorders and/or the dysfunction of ion channels contribute to the progression of neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS). Therefore, metal homeostasis-related signaling pathways are emerging as promising therapeutic targets for diverse neurological diseases. This review summarizes recent advances in the studies regarding the physiological and pathophysiological functions of metal ions and their channels, as well as their role in neurodegenerative diseases. In addition, currently available metal ion modulators and in vivo quantitative metal ion imaging methods are also discussed. Current work provides certain recommendations based on literatures and in-depth reflections to improve neurodegenerative diseases. Future studies should turn to crosstalk and interactions between different metal ions and their channels. Concomitant pharmacological interventions for two or more metal signaling pathways may offer clinical advantages in treating the neurodegenerative diseases.
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Affiliation(s)
- Yi-Yue Zhang
- Department of Pharmacology, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410078, China
| | - Xi-Sheng Li
- Department of Laboratory Medicine, The Third Xiangya Hospital of Central South University, Changsha 410013,China
| | - Kai-Di Ren
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jun Peng
- Department of Pharmacology, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410078, China; Hunan Provincial Key Laboratory of Cardiovascular Research, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410078, China.
| | - Xiu-Ju Luo
- Department of Laboratory Medicine, The Third Xiangya Hospital of Central South University, Changsha 410013,China.
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94
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Imai A, Matsuoka T, Narumoto J. Emotional Dysregulation in Mild Behavioral Impairment Is Associated with Reduced Cortical Thickness in the Right Supramarginal Gyrus. J Alzheimers Dis 2023; 93:521-532. [PMID: 37038811 DOI: 10.3233/jad-220948] [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: 04/12/2023]
Abstract
BACKGROUND Mild behavioral impairment (MBI) has attracted attention as a possible precursor symptom of dementia, but its neural basis has not been fully investigated. OBJECTIVE We aimed to investigate the relationship between MBI and surface area, cortical thickness, and volume in the temporal and parietal lobes, which are strongly associated with dementia and emotional disorders. METHODS This retrospective study evaluated 123 participants: 90 with mild cognitive impairment (MCI), 13 with subjective cognitive decline (SCD), and 20 cognitively healthy (CH). Using analysis of covariance (ANCOVA) with sex, age, and MMSE score as covariates, cortical thickness, surface area, and volume in 10 regions were compared between groups with and without MBI. Groups with MBI emotional dysregulation were also compared with groups without MBI. RESULTS ANCOVA revealed significantly smaller cortical thickness in the MBI group's right parahippocampal (p = 0.01) and supramarginal gyri (p = 0.002). After multiple comparison correction, only the right supramarginal gyrus was significantly smaller (p = 0.02). When considering only MBI emotional dysregulation, the right parahippocampal and supramarginal gyrus' cortical thicknesses were significantly smaller in this MBI group (p = 0.03, 0.01). However, multiple comparison correction identified no significant differences (p = 0.14, 0.11). CONCLUSION Overall MBI and the emotional dysregulation domains were associated with reduced cortical thickness in the right parahippocampal and supramarginal gyri. Since neurodegeneration in the medial temporal and parietal lobe precedes early Alzheimer's disease (AD), MBI, particularly emotion dysregulation, may predict early AD below the diagnostic threshold.
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Affiliation(s)
- Ayu Imai
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Teruyuki Matsuoka
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jin Narumoto
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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95
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Nabizadeh F, Balabandian M, Rostami MR, Mehrabi S, Sedighi M, Alzheimer’s Disease Neuroimaging Initiative. Regional cerebral blood flow and brain atrophy in mild cognitive impairment and Alzheimer's disease. NEUROLOGY LETTERS 2023; 2:16-24. [PMID: 38327487 PMCID: PMC10849084 DOI: 10.52547/nl.2.1.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objectives A decline in the regional cerebral blood flow (CBF) is proposed to be one of the initial changes in the Alzheimer's disease process. To date, there are limited data on the correlation between CBF decline and gray matter atrophy in mild cognitive impairment (MCI) and AD patients. to investigate the association between CBF with the gray matter structural parameters such as cortical volume, surface area, and thickness in AD, MCI, and healthy controls (HC). Methods Data from three groups of participants including 39 HC, 82 MCI, and 28 AD subjects were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI). One-way ANOVA and linear regression were used to compare data and find a correlation between structural parameters such as cortical volume, surface area, and thickness and CBF which measured by arterial spin labeling (ASL)-MRI. Results Our findings revealed a widespread significant correlation between the CBF and structural parameters in temporal, frontal, parietal, occipital, precentral gyrus, pericalcarine cortex, entorhinal cortex, supramarginal gyrus, fusiform, precuneus, and pallidum. Conclusion CBF decline may be a useful biomarker for MCI and AD and accurately reflect the structural changes related to AD. According to the present results, CBF decline, as measured by ASL-MRI, is correlated with lower measures of structural parameters in AD responsible regions. It means that CBF decline may reflect AD-associated atrophy across disease progression and is also used as an early biomarker for AD and MCI diagnosis.
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Affiliation(s)
- Fardin Nabizadeh
- Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Balabandian
- Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Rostami
- Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Soraya Mehrabi
- Department of Physiology, Faculty of Medicine, Iran University of Medical Science, Tehran, Iran
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Science, Tehran, Iran
| | - Mohsen Sedighi
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Science, Tehran, Iran
- Neuroscience Research Center (NRC), Iran University of Medical Sciences, Tehran, Iran
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96
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Ahmadi K, Pereira JB, Berron D, Vogel J, Ingala S, Strandberg OT, Janelidze S, Barkhof F, Pfeuffer J, Knutsson L, van Westen D, Palmqvist S, Mutsaerts HJ, Hansson O. Gray matter hypoperfusion is a late pathological event in the course of Alzheimer's disease. J Cereb Blood Flow Metab 2023; 43:565-580. [PMID: 36412244 PMCID: PMC10063832 DOI: 10.1177/0271678x221141139] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Several studies have shown decreased cerebral blood flow (CBF) in Alzheimer's disease (AD). However, the role of hypoperfusion in the disease pathogenesis remains unclear. Combining arterial spin labeling MRI, PET, and CSF biomarkers, we investigated the associations between gray matter (GM)-CBF and the key mechanisms in AD including amyloid-β (Aβ) and tau pathology, synaptic and axonal degeneration. Further, we applied a disease progression modeling to characterize the temporal sequence of different AD biomarkers. Lower perfusion was observed in temporo-occipito-parietal cortex in the Aβ-positive cognitively impaired compared to both Aβ-negative and Aβ-positive cognitively unimpaired individuals. In participants along the AD spectrum, GM-CBF was associated with tau, synaptic and axonal dysfunction, but not Aβ in similar cortical regions. Axonal degeneration was further associated with hypoperfusion in cognitively unimpaired individuals. Disease progression modeling revealed that GM-CBF disruption Followed the abnormality of biomarkers of Aβ, tau and brain atrophy. These findings indicate that tau tangles and neurodegeneration are more closely connected with GM-CBF changes than Aβ pathology. Although subjected to the sensitivity of the employed neuroimaging techniques and the modeling approach, these findings suggest that hypoperfusion might not be an early event associated with the build-up of Aβ in preclinical phase of AD.
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Affiliation(s)
- Khazar Ahmadi
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jacob Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Olof T Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Queen's Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Josef Pfeuffer
- Application Development, Siemens Healthcare, Erlangen, Germany
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden.,The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Danielle van Westen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Diagnostic Radiology, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Henk Jmm Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Queen's Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
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97
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Sanz Perl Y, Fittipaldi S, Gonzalez Campo C, Moguilner S, Cruzat J, Fraile-Vazquez ME, Herzog R, Kringelbach ML, Deco G, Prado P, Ibanez A, Tagliazucchi E. Model-based whole-brain perturbational landscape of neurodegenerative diseases. eLife 2023; 12:e83970. [PMID: 36995213 PMCID: PMC10063230 DOI: 10.7554/elife.83970] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/15/2023] [Indexed: 03/31/2023] Open
Abstract
The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.
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Affiliation(s)
- Yonatan Sanz Perl
- Department of Physics, University of Buenos AiresBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
| | - Sol Fittipaldi
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
| | - Cecilia Gonzalez Campo
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
| | - Sebastián Moguilner
- Global Brain Health Institute, University of California, San FranciscoSan FranciscoUnited States
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | - Josephine Cruzat
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | | | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | - Morten L Kringelbach
- Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus UniversityÅrhusDenmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBragaPortugal
- Centre for Eudaimonia and Human Flourishing, University of OxfordOxfordUnited Kingdom
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
- Department of Information and Communication Technologies, Universitat Pompeu FabraBarcelonaSpain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA)BarcelonaSpain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- School of Psychological Sciences, Monash UniversityClaytonAustralia
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San SebastiánSantiagoChile
| | - Agustin Ibanez
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Global Brain Health Institute, University of California, San FranciscoSan FranciscoUnited States
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
- Trinity College Institute of Neuroscience (TCIN), Trinity College DublinDublinIreland
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos AiresBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
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98
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Wu H, Song Y, Yang X, Chen S, Ge H, Yan Z, Qi W, Yuan Q, Liang X, Lin X, Chen J. Functional and structural alterations of dorsal attention network in preclinical and early-stage Alzheimer's disease. CNS Neurosci Ther 2023; 29:1512-1524. [PMID: 36942514 PMCID: PMC10173716 DOI: 10.1111/cns.14092] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 12/31/2022] [Accepted: 01/02/2023] [Indexed: 03/23/2023] Open
Abstract
OBJECTIVES Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the "top-down" attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early-stage AD. METHODS Resting-state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver-operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI. RESULTS The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages. CONCLUSIONS Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early-stage AD for their high diagnostic value.
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Affiliation(s)
- Huimin Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyi Yang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuhong Liang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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99
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Cerasuolo M, Papa M, Colangelo AM, Rizzo MR. Alzheimer’s Disease from the Amyloidogenic Theory to the Puzzling Crossroads between Vascular, Metabolic and Energetic Maladaptive Plasticity. Biomedicines 2023; 11:biomedicines11030861. [PMID: 36979840 PMCID: PMC10045635 DOI: 10.3390/biomedicines11030861] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
Alzheimer’s disease (AD) is a progressive and degenerative disease producing the most common type of dementia worldwide. The main pathogenetic hypothesis in recent decades has been the well-known amyloidogenic hypothesis based on the involvement of two proteins in AD pathogenesis: amyloid β (Aβ) and tau. Amyloid deposition reported in all AD patients is nowadays considered an independent risk factor for cognitive decline. Vascular damage and blood–brain barrier (BBB) failure in AD is considered a pivotal mechanism for brain injury, with increased deposition of both immunoglobulins and fibrin. Furthermore, BBB dysfunction could be an early sign of cognitive decline and the early stages of clinical AD. Vascular damage generates hypoperfusion and relative hypoxia in areas with high energy demand. Long-term hypoxia and the accumulation within the brain parenchyma of neurotoxic molecules could be seeds of a self-sustaining pathological progression. Cellular dysfunction comprises all the elements of the neurovascular unit (NVU) and neuronal loss, which could be the result of energy failure and mitochondrial impairment. Brain glucose metabolism is compromised, showing a specific region distribution. This energy deficit worsens throughout aging. Mild cognitive impairment has been reported to be associated with a glucose deficit in the entorhinal cortex and in the parietal lobes. The current aim is to understand the complex interactions between amyloid β (Aβ) and tau and elements of the BBB and NVU in the brain. This new approach aimed at the study of metabolic mechanisms and energy insufficiency due to mitochondrial impairment would allow us to define therapies aimed at predicting and slowing down the progression of AD.
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Affiliation(s)
- Michele Cerasuolo
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Michele Papa
- Laboratory of Neuronal Networks Morphology and System Biology, Department of Mental and Physical Health and Preventive Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
- SYSBIO Centre of Systems Biology ISBE-IT, 20126 Milan, Italy
- Correspondence:
| | - Anna Maria Colangelo
- SYSBIO Centre of Systems Biology ISBE-IT, 20126 Milan, Italy
- Laboratory of Neuroscience “R. Levi-Montalcini”, Department of Biotechnology and Biosciences, NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, 20126 Milano, Italy
| | - Maria Rosaria Rizzo
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
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100
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Helwegen K, Libedinsky I, van den Heuvel MP. Statistical power in network neuroscience. Trends Cogn Sci 2023; 27:282-301. [PMID: 36725422 DOI: 10.1016/j.tics.2022.12.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/31/2023]
Abstract
Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.
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Affiliation(s)
- Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ilan Libedinsky
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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