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Liu H, Jing J, Jiang J, Wen W, Zhu W, Li Z, Pan Y, Cai X, Liu C, Zhou Y, Meng X, Wang Y, Li H, Jiang Y, Zheng H, Wang S, Niu H, Kochan N, Brodaty H, Wei T, Sachdev PS, Fan Y, Liu T, Wang Y. Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study. Sci Bull (Beijing) 2024; 69:2735-2744. [PMID: 38664095 DOI: 10.1016/j.scib.2024.04.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 02/08/2024] [Accepted: 04/07/2024] [Indexed: 09/09/2024]
Abstract
Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.
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Affiliation(s)
- Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Jing Jing
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
| | - Jiyang Jiang
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wei Wen
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Zixiao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yuesong Pan
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Xueli Cai
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Xia Meng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yilong Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Hao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Huaguang Zheng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Nicole Kochan
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Henry Brodaty
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Tiemin Wei
- Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Perminder S Sachdev
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Yubo Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China.
| | - Yongjun Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
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Ni H, Xue J, Qin J, Zhang Y. Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108281. [PMID: 38924798 DOI: 10.1016/j.cmpb.2024.108281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 05/04/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND AND OBJECTIVE Accurate identification of individuals with subjective cognitive decline (SCD) is crucial for early intervention and prevention of neurodegenerative diseases. Fractal dimensionality (FD) has emerged as a robust and replicable measure, surpassing traditional geometric metrics, in characterizing the intricate fractal geometrical properties of brain structure. Nevertheless, the effectiveness of FD in identifying individuals with SCD remains largely unclear. A 3D regional FD method can be suggested to characterize and quantify the spatial complexity of the precise gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. METHODS This study introduces a novel integer ratio based 3D box-counting fractal analysis (IRBCFA) to quantify regional fractal dimensions (FDs) in structural magnetic resonance imaging (MRI) data. The innovative method overcomes limitations of conventional box-counting techniques by accommodating arbitrary box sizes, thereby enhancing the precision of FD estimation in small, yet neurologically significant, brain regions. RESULTS The application of IRBCFA to two publicly available datasets, OASIS-3 and ADNI, consisting of 520 and 180 subjects, respectively. The method identified discriminative regions of interest (ROIs) predominantly within the limbic system, fronto-parietal region, occipito-temporal region, and basal ganglia-thalamus region. These ROIs exhibited significant correlations with cognitive functions, including executive functioning, memory, social cognition, and sensory perception, suggesting their potential as neuroimaging markers for SCD. The identification model trained on these ROIs demonstrated exceptional performance achieving over 93 % accuracy on the discovery dataset and exceeding 87 % on the independent testing dataset. Furthermore, an exchange experiment between datasets revealed a substantial overlap in discriminative ROIs, highlighting the robustness of our method across diverse populations. CONCLUSION Our findings indicate that IRBCFA can serve as a valuable tool for quantifying the spatial complexity of gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. The demonstrated generalizability and robustness of this method position it as a promising tool for neurodegenerative disease research and offer potential for clinical applications.
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Affiliation(s)
- Huangjing Ni
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
| | - Jing Xue
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
| | - Jiaolong Qin
- Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Yu Zhang
- Department of Clinical Psychology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, 310006, China.
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Mroziak M, Kozłowski G, Kołodziejczyk W, Pszczołowska M, Walczak K, Beszłej JA, Leszek J. Dendrimers-Novel Therapeutic Approaches for Alzheimer's Disease. Biomedicines 2024; 12:1899. [PMID: 39200363 PMCID: PMC11351976 DOI: 10.3390/biomedicines12081899] [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: 07/16/2024] [Revised: 08/10/2024] [Accepted: 08/13/2024] [Indexed: 09/02/2024] Open
Abstract
Dendrimers are covalently bonded globular nanostructures that may be used in the treatment of Alzheimer's disease (AD). Nowadays, AD therapies are focused on improving cognitive functioning and not causal treatment. However, this may change with the use of dendrimers, which are being investigated as a drug-delivery system or as a drug per se. With their ability to inhibit amyloid formation and their anti-tau properties, they are a promising therapeutic option for AD patients. Studies have shown that dendrimers may inhibit amyloid formation in at least two ways: by blocking fibril growth and by breaking already existing fibrils. Neurofibrillary tangles (NFTs) are abnormal filaments built by tau proteins that can be accumulated in the cell, which leads to the loss of cytoskeletal microtubules and tubulin-associated proteins. Cationic phosphorus dendrimers, with their anti-tau properties, can induce the aggregation of tau into amorphous structures. Drug delivery to mitochondria is difficult due to poor transport across biological barriers, such as the inner mitochondrial membrane, which is highly negatively polarized. Dendrimers may be potential nanocarriers and increase mitochondria targeting. Another considered use of dendrimers in AD treatment is as a drug-delivery system, for example, carbamazepine (CBZ) or tacrine. They can also be used to transport siRNA into neuronal tissue and to carry antioxidants and anti-inflammatory drugs to act protectively on the nervous system.
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Affiliation(s)
- Magdalena Mroziak
- Faculty of Medicine, Wrocław Medical University, 50-367 Wrocław, Poland
| | - Gracjan Kozłowski
- Faculty of Medicine, Wrocław Medical University, 50-367 Wrocław, Poland
| | | | | | - Kamil Walczak
- Faculty of Medicine, Wrocław Medical University, 50-367 Wrocław, Poland
| | - Jan Aleksander Beszłej
- Clinic of Psychiatry, Department of Psychiatry, Medical Department, Wrocław Medical University, 50-367 Wrocław, Poland
| | - Jerzy Leszek
- Clinic of Psychiatry, Department of Psychiatry, Medical Department, Wrocław Medical University, 50-367 Wrocław, Poland
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Zhang X, Zhu Y, Lu J, Chen Q, Chen F, Long C, Xu X, Ge D, Bai Y, Liu D, Du S, Zhu Z, Mai X, Yang QX, Zhang B. Altered functional connectivity of primary olfactory cortex-hippocampus-frontal cortex in subjective cognitive decline during odor stimulation. Hum Brain Mapp 2024; 45:e26814. [PMID: 39163575 PMCID: PMC11335137 DOI: 10.1002/hbm.26814] [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: 01/28/2024] [Revised: 07/08/2024] [Accepted: 07/29/2024] [Indexed: 08/22/2024] Open
Abstract
Subjective cognitive decline (SCD) is a high-risk population in the preclinical stage of Alzheimer's disease (AD), and olfactory dysfunction is a risk factor for dementia progression. The present study aimed to explore the patterns of functional connectivity (FC) changes in the olfactory neural circuits during olfactory stimulation in SCD subjects. A total of 56 SCD subjects and 56 normal controls (NCs) were included. All subjects were assessed with a cognitive scale, an olfactory behavior test, and olfactory task-based functional magnetic resonance imaging scanning. The FC differences in olfactory neural circuits between the two groups were analyzed by the generalized psychophysiological interaction. Additionally, we calculated and compared the activation of brain regions within the olfactory neural circuits during odor stimulation, the volumetric differences in brain regions showing FC differences between groups, and the correlations between neuroimaging indicators and olfactory behavioral and cognitive scale scores. During odor stimulation, the FC between the bilateral primary olfactory cortex (bPOC) and the right hippocampus in the SCD group was significantly reduced; while the FC between the right hippocampus and the right frontal cortex was significantly increased in the SCD group. The bPOC of all subjects showed significant activation, but no significant difference in activation between groups was found. No significant differences were observed in the volume of the brain regions within the olfactory neural circuits or in olfactory behavior between groups. The volume of the bPOC and right frontal cortex was significantly positively correlated with olfactory identification, and the volume of the right frontal cortex and right hippocampus was significantly correlated with cognitive functions. Furthermore, a significant correlation between the activation of bPOC and the olfactory threshold was found in the whole cohort. These results suggested that while the structure of the olfactory neural circuits and olfactory behavior in SCD subjects remained stable, there were significant changes observed in the FC of the olfactory neural circuits (specifically, the POC-hippocampus-frontal cortex neural circuits) during odor stimulation. These findings highlight the potential of FC alterations as sensitive imaging markers for identifying high-risk individuals in the early stage of AD.
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Affiliation(s)
- Xin Zhang
- Department of RadiologyNanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese MedicineNanjingChina
| | - Yajing Zhu
- Department of RadiologyNanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityNanjingChina
| | - Jiaming Lu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingChina
| | - Qian Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingChina
| | - Futao Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingChina
| | - Cong Long
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingChina
| | - Xinru Xu
- Department of RadiologyNanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityNanjingChina
| | - Danni Ge
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingChina
| | - Yijun Bai
- Department of PsychologyNanjing UniversityNanjingChina
| | - Dongming Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingChina
| | - Shunshun Du
- Department of PsychologyNanjing UniversityNanjingChina
| | - Zhengyang Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingChina
| | - Xiaoli Mai
- Department of RadiologyNanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityNanjingChina
| | - Qing X. Yang
- Department of RadiologyThe Pennsylvania State University College of MedicineHersheyPennsylvaniaUSA
| | - Bing Zhang
- Department of RadiologyNanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese MedicineNanjingChina
- Department of RadiologyNanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityNanjingChina
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingChina
- Department of PsychologyNanjing UniversityNanjingChina
- Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingChina
- Jiangsu Key Laboratory of Molecular MedicineNanjingChina
- Institute of Brain ScienceNanjing UniversityNanjingChina
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Chavarría-Elizondo P, Del Río-Torné C, Maturana-Quijada P, Martínez-Zalacaín I, Juaneda A, Del Cerro I, Guinea-Izquierdo A, Gascón-Bayarri J, Reñé-Ramírez R, Urretavizcaya M, Ferrer I, Menchón JM, Soria V, Soriano-Mas C. Altered activation of the dorsal anterior cingulate cortex during oddball performance in individuals at risk for Alzheimer's disease. SPANISH JOURNAL OF PSYCHIATRY AND MENTAL HEALTH 2024:S2950-2853(24)00038-3. [PMID: 38992346 DOI: 10.1016/j.sjpmh.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 06/03/2024] [Accepted: 07/02/2024] [Indexed: 07/13/2024]
Abstract
INTRODUCTION The neural mechanisms underlying neurodegenerative disorders in the elderly remain elusive, despite extensive neuroimaging research in recent decades. Amnestic type mild cognitive impairment (aMCI) and late-life major depressive disorder (MDD) are two such conditions characterized by intersecting cognitive and affective symptomatology, and they are at a higher risk for Alzheimer's disease. MATERIALS AND METHODS This study analyzed the neural underpinnings of cognitive and depressive symptoms in a cohort comprising 12 aMCI subjects, 24 late-life MDD patients, and 26 healthy controls (HCs). Participants underwent a detailed neuropsychological assessment and completed a visual attentional oddball task during functional magnetic resonance imaging (fMRI), with evaluations at baseline and at 2-year follow-up. RESULTS Initial findings showed that aMCI subjects had reduced dACC activation during oddball (target) stimulus detection, a pattern that persisted in longitudinal analyses and correlated with cognitive functioning measures. For HCs, subsequent dACC activation was linked to depression scores. Furthermore, in the affective-cognitive altered groups, later dACC activation correlated with oddball and memory performance. CONCLUSIONS These findings enhance our comprehension of the neurobiological basis of cognitive and depressive disturbances in aging, indicating that dACC activation in response to a visual attentional oddball task could serve as a neural marker for assessing cognitive impairment and depression in conditions predisposing to Alzheimer's disease.
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Affiliation(s)
- Pamela Chavarría-Elizondo
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, School of Medicine, University of Barcelona, L'Hospitalet de Llobregat, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain
| | - Carla Del Río-Torné
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, School of Medicine, University of Barcelona, L'Hospitalet de Llobregat, Spain
| | - Pablo Maturana-Quijada
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, School of Medicine, University of Barcelona, L'Hospitalet de Llobregat, Spain
| | | | - Asier Juaneda
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Inés Del Cerro
- Department of Psychology, Medical School, Catholic University of Murcia, Murcia, Spain; Worldwide Clinical Trials, Madrid, Spain
| | - Andrés Guinea-Izquierdo
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Jordi Gascón-Bayarri
- Dementia Diagnostic and Treatment Unit, Department of Neurology, Bellvitge University Hospital, Barcelona, Spain
| | - Ramón Reñé-Ramírez
- Dementia Diagnostic and Treatment Unit, Department of Neurology, Bellvitge University Hospital, Barcelona, Spain
| | - Mikel Urretavizcaya
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, School of Medicine, University of Barcelona, L'Hospitalet de Llobregat, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain
| | - Isidre Ferrer
- Department of Pathology and Experimental Therapeutics, Institute of Neurosciences, University of Barcelona, Barcelona, Spain; Bellvitge Biomedical Research Institute-IDIBELL, Department of Pathologic Anatomy, Bellvitge University Hospital, Barcelona, Spain; Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Barcelona, Spain
| | - José M Menchón
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, School of Medicine, University of Barcelona, L'Hospitalet de Llobregat, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain
| | - Virginia Soria
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain; Department of Mental Health, Parc Taulí University Hospital, Sabadell, Barcelona, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain.
| | - Carles Soriano-Mas
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain; Department of Social Psychology and Quantitative Psychology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain.
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Wang Q, Zhen W, Hu R, Wang Z, Sun Y, Sun W, Huang C, Xu J, Zhang H. Occlusion dysfunction and Alzheimer's disease: Mendelian randomization study. Front Aging Neurosci 2024; 16:1423322. [PMID: 39035234 PMCID: PMC11258003 DOI: 10.3389/fnagi.2024.1423322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 06/20/2024] [Indexed: 07/23/2024] Open
Abstract
Aim Occlusion dysfunction (OD) is increasingly linked to Alzheimer's disease (AD). This study aimed to elucidate the causal relationship between OD and AD using Mendelian randomization (MR) analysis. Materials and methods Genome-wide association study (GWAS) meta-analysis data obtained from FinnGen, IEU Open GWAS, and UK Biobank (UKBB) was represented as instrumental variables. We validated the causal relationship between periodontal disease (PD), loose teeth (PD & occlusion dysfunction), dentures restoration (occlusion recovery), and AD. Results According to the MR analysis, PD and AD have no direct causal relationship (P = 0.395, IVW). However, loose teeth significantly increased the risk of AD progression (P = 0.017, IVW, OR = 187.3567, 95%CI = 2.54E+00-1.38E+04). These findings were further supported by the negative causal relationship between dentures restoration and AD (P = 0.015, IVW, OR = 0.0234, 95%CI = 1.13E-03-0.485). Conclusion The occlusion dysfunction can ultimately induce Alzheimer's disease. Occlusion function was a potentially protective factor for maintaining neurological health.
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Affiliation(s)
- Qing Wang
- Key Laboratory of Oral Diseases Research of Anhui Province, College and Hospital of Stomatology, Anhui Medical University, Hefei, China
| | - Wenyu Zhen
- Key Laboratory of Oral Diseases Research of Anhui Province, College and Hospital of Stomatology, Anhui Medical University, Hefei, China
| | - Rui Hu
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zifei Wang
- Key Laboratory of Oral Diseases Research of Anhui Province, College and Hospital of Stomatology, Anhui Medical University, Hefei, China
| | - Yuqiang Sun
- Key Laboratory of Oral Diseases Research of Anhui Province, College and Hospital of Stomatology, Anhui Medical University, Hefei, China
| | - Wansu Sun
- Department of Stomatology, Anhui Public Health Clinical Center, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chunxia Huang
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jianguang Xu
- Key Laboratory of Oral Diseases Research of Anhui Province, College and Hospital of Stomatology, Anhui Medical University, Hefei, China
| | - Hengguo Zhang
- Key Laboratory of Oral Diseases Research of Anhui Province, College and Hospital of Stomatology, Anhui Medical University, Hefei, China
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Jiang J, Zhuo Z, Wang A, Li W, Jiang S, Duan Y, Ren Q, Zhao M, Wang L, Yang S, Awan MUN, Liu Y, Xu J. Choroid plexus volume as a novel candidate neuroimaging marker of the Alzheimer's continuum. Alzheimers Res Ther 2024; 16:149. [PMID: 38961406 PMCID: PMC11221040 DOI: 10.1186/s13195-024-01520-w] [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: 01/17/2024] [Accepted: 06/25/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Enlarged choroid plexus (ChP) volume has been reported in patients with Alzheimer's disease (AD) and inversely correlated with cognitive performance. However, its clinical diagnostic and predictive value, and mechanisms by which ChP impacts the AD continuum remain unclear. METHODS This prospective cohort study enrolled 607 participants [healthy control (HC): 110, mild cognitive impairment (MCI): 269, AD dementia: 228] from the Chinese Imaging, Biomarkers, and Lifestyle study between January 1, 2021, and December 31, 2022. Of the 497 patients on the AD continuum, 138 underwent lumbar puncture for cerebrospinal fluid (CSF) hallmark testing. The relationships between ChP volume and CSF pathological hallmarks (Aβ42, Aβ40, Aβ42/40, tTau, and pTau181), neuropsychological tests [Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Neuropsychiatric Inventory (NPI), and Activities of Daily Living (ADL) scores], and multimodal neuroimaging measures [gray matter volume, cortical thickness, and corrected cerebral blood flow (cCBF)] were analyzed using partial Spearman's correlation. The mediating effects of four neuroimaging measures [ChP volume, hippocampal volume, lateral ventricular volume (LVV), and entorhinal cortical thickness (ECT)] on the relationship between CSF hallmarks and neuropsychological tests were examined. The ability of the four neuroimaging measures to identify cerebral Aβ42 changes or differentiate among patients with AD dementia, MCI and HCs was determined using receiver operating characteristic analysis, and their associations with neuropsychological test scores at baseline were evaluated by linear regression. Longitudinal associations between the rate of change in the four neuroimaging measures and neuropsychological tests scores were evaluated on the AD continuum using generalized linear mixed-effects models. RESULTS The participants' mean age was 65.99 ± 8.79 years. Patients with AD dementia exhibited the largest baseline ChP volume than the other groups (P < 0.05). ChP volume enlargement correlated with decreased Aβ42 and Aβ40 levels; lower MMSE and MoCA and higher NPI and ADL scores; and lower volume, cortical thickness, and cCBF in other cognition-related regions (all P < 0.05). ChP volume mediated the association of Aβ42 and Aβ40 levels with MMSE scores (19.08% and 36.57%), and Aβ42 levels mediated the association of ChP volume and MMSE or MoCA scores (39.49% and 34.36%). ChP volume alone better identified cerebral Aβ42 changes than LVV alone (AUC = 0.81 vs. 0.67, P = 0.04) and EC thickness alone (AUC = 0.81 vs.0.63, P = 0.01) and better differentiated patients with MCI from HCs than hippocampal volume alone (AUC = 0.85 vs. 0.81, P = 0.01), and LVV alone (AUC = 0.85 vs.0.82, P = 0.03). Combined ChP and hippocampal volumes significantly increased the ability to differentiate cerebral Aβ42 changes and patients among AD dementia, MCI, and HCs groups compared with hippocampal volume alone (all P < 0.05). After correcting for age, sex, years of education, APOE ε4 status, eTIV, and hippocampal volume, ChP volume was associated with MMSE, MoCA, NPI, and ADL score at baseline, and rapid ChP volume enlargement was associated with faster deterioration in NPI scores with an average follow-up of 10.03 ± 4.45 months (all P < 0.05). CONCLUSIONS ChP volume may be a novel neuroimaging marker associated with neurodegenerative changes and clinical AD manifestations. It could better detect the early stages of the AD and predict prognosis, and significantly enhance the differential diagnostic ability of hippocampus on the AD continuum.
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Affiliation(s)
- Jiwei Jiang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Zhizheng Zhuo
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Anxin Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Wenyi Li
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Shirui Jiang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yunyun Duan
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Qiwei Ren
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Min Zhao
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Linlin Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Shiyi Yang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
| | | | - Yaou Liu
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- National Clinical Research Center for Neurological Diseases, Beijing, China.
| | - Jun Xu
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- National Clinical Research Center for Neurological Diseases, Beijing, China.
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8
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Amland R, Selbæk G, Brækhus A, Edwin TH, Engedal K, Knapskog AB, Olsrud ER, Persson K. Clinically feasible automated MRI volumetry of the brain as a prognostic marker in subjective and mild cognitive impairment. Front Neurol 2024; 15:1425502. [PMID: 39011362 PMCID: PMC11248186 DOI: 10.3389/fneur.2024.1425502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/11/2024] [Indexed: 07/17/2024] Open
Abstract
Background/aims The number of patients suffering from cognitive decline and dementia increases, and new possible treatments are being developed. Thus, the need for time efficient and cost-effective methods to facilitate an early diagnosis and prediction of future cognitive decline in patients with early cognitive symptoms is becoming increasingly important. The aim of this study was to evaluate whether an MRI based software, NeuroQuant® (NQ), producing volumetry of the hippocampus and whole brain volume (WBV) could predict: (1) conversion from subjective cognitive decline (SCD) at baseline to mild cognitive impairment (MCI) or dementia at follow-up, and from MCI at baseline to dementia at follow-up and (2) progression of cognitive and functional decline defined as an annual increase in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score. Methods MRI was performed in 156 patients with SCD or MCI from the memory clinic at Oslo University Hospital (OUH) that had been assessed with NQ and had a clinical follow-up examination. Logistic and linear regression analyses were performed with hippocampus volume and WBV as independent variables, and conversion or progression as dependent variables, adjusting for demographic and other relevant covariates including Mini-Mental State Examination-Norwegian Revised Version score (MMSE-NR) and Apolipoprotein E ɛ4 (APOE ɛ4) carrier status. Results Hippocampus volume, but not WBV, was associated with conversion to MCI or dementia, but neither were associated with conversion when adjusting for MMSE-NR. Both hippocampus volume and WBV were associated with progression as measured by the annual change in CDR-SB score in both unadjusted and adjusted analyses. Conclusion The results indicate that automated regional MRI volumetry of the hippocampus and WBV can be useful in predicting further cognitive decline in patients with early cognitive symptoms.
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Affiliation(s)
- Rachel Amland
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Geir Selbæk
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anne Brækhus
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Trine H. Edwin
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Knut Engedal
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | | | - Ellen Regine Olsrud
- Department of Radiography Ullevål, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Karin Persson
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
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9
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Oviedo DC, Tratner AE, Rodríguez-Araña S, Villarreal AE, Rangel G, Carreira MB, Britton GB. Predictors of cognitive change in cognitively healthy older women in Panama: the PARI-HD study. Front Glob Womens Health 2024; 5:1353657. [PMID: 38939750 PMCID: PMC11208464 DOI: 10.3389/fgwh.2024.1353657] [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: 12/11/2023] [Accepted: 05/28/2024] [Indexed: 06/29/2024] Open
Abstract
Background Evidence suggests that a combination of biological and social factors influence risk of dementia differently for women and men. In healthy older women, several factors may contribute to changes in cognition. Objective Describe the characteristics associated with variation in cognition in a sample of cognitively healthy older Panamanian women. Methods The study includes cross-sectional analyses of cognitive domains at baseline (n = 357) and 17-month (SD = 2.0) follow-up (n = 200) for women aged 60 years and older enrolled in the Panama Aging Research Initiative-Health Disparities (PARI-HD) study. Instruments included clinical questionnaires, physiological measures, and a neuropsychological test battery assessing global cognition and seven cognitive domains. Multiple regression analyses examined the associations between demographic and clinical characteristics and cognition at baseline. Repeated measures analyses were used to investigate changes in cognition from baseline to follow-up. Results On average, participants were 68.6 years of age (SD = 5.9) with 16.1 years of education (SD = 4.7). Age, income, and education showed robust associations with baseline cognition. Subjective cognitive impairment was associated with lower performance in global cognition, verbal learning, and memory domains. Only performance in the attention domain decreased at follow-up, and subjective health state and depressive symptoms significantly predicted the change in attention. Discussion Our study findings contribute to the investigation of cognitive health in older Hispanic women and to the understanding of sociodemographic and health-related factors associated with cognitive decline and the progression to cognitive impairment and dementia.
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Affiliation(s)
- Diana C. Oviedo
- Centro de Neurociencias y Unidad de Investigación Clínica, Institiuto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT-AIP), Panama City, Panamá
- Escuela de Psicología, Universidad Santa María la Antigua (USMA), Panama City, Panamá
- Sistema Nacional de Investigación (SNI) SENACYT, Panama City, Panamá
| | - Adam E. Tratner
- Sistema Nacional de Investigación (SNI) SENACYT, Panama City, Panamá
- Florida State University, Panama City, Panamá
| | - Sofía Rodríguez-Araña
- Centro de Neurociencias y Unidad de Investigación Clínica, Institiuto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT-AIP), Panama City, Panamá
| | - Alcibiades E. Villarreal
- Centro de Neurociencias y Unidad de Investigación Clínica, Institiuto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT-AIP), Panama City, Panamá
- Sistema Nacional de Investigación (SNI) SENACYT, Panama City, Panamá
| | - Giselle Rangel
- Centro de Neurociencias y Unidad de Investigación Clínica, Institiuto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT-AIP), Panama City, Panamá
- Sistema Nacional de Investigación (SNI) SENACYT, Panama City, Panamá
| | - María B. Carreira
- Centro de Neurociencias y Unidad de Investigación Clínica, Institiuto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT-AIP), Panama City, Panamá
- Sistema Nacional de Investigación (SNI) SENACYT, Panama City, Panamá
| | - Gabrielle B. Britton
- Centro de Neurociencias y Unidad de Investigación Clínica, Institiuto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT-AIP), Panama City, Panamá
- Sistema Nacional de Investigación (SNI) SENACYT, Panama City, Panamá
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10
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Zhou X, Balachandra AR, Romano MF, Chin SP, Au R, Kolachalama VB. Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2024; 12:83169-83182. [PMID: 39148927 PMCID: PMC11326336 DOI: 10.1109/access.2024.3408840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Game theory-inspired deep learning using a generative adversarial network provides an environment to competitively interact and accomplish a goal. In the context of medical imaging, most work has focused on achieving single tasks such as improving image resolution, segmenting images, and correcting motion artifacts. We developed a dual-objective adversarial learning framework that simultaneously 1) reconstructs higher quality brain magnetic resonance images (MRIs) that 2) retain disease-specific imaging features critical for predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). We obtained 3-Tesla, T1-weighted brain MRIs of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=342) and the National Alzheimer's Coordinating Center (NACC, N = 190) datasets. We simulated MRIs with missing data by removing 50% of sagittal slices from the original scans (i.e., diced scans). The generator was trained to reconstruct brain MRIs using the diced scans as input. We introduced a classifier into the GAN architecture to discriminate between stable (i.e., sMCI) and progressive MCI (i.e., pMCI) based on the generated images to facilitate encoding of disease-related information during reconstruction. The framework was trained using ADNI data and externally validated on NACC data. In the NACC cohort, generated images had better image quality than the diced scans (Structural similarity (SSIM) index: 0.553 ± 0.116 versus 0.348 ± 0.108). Furthermore, a classifier utilizing the generated images distinguished pMCI from sMCI more accurately than with the diced scans (F1-score: 0.634 ± 0.019 versus 0.573 ± 0.028). Competitive deep learning has potential to facilitate disease-oriented image reconstruction in those at risk of developing Alzheimer's disease.
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Affiliation(s)
- Xiao Zhou
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Akshara R Balachandra
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael F Romano
- Department of Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
| | - Sang Peter Chin
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Rhoda Au
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- Framingham Heart Study, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- Boston University Alzheimer's Disease Research Center, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- Boston University Alzheimer's Disease Research Center, Boston, MA 02118, USA
- Department of Computer Science, Faculty of Computing and Data Sciences, Boston University, Boston, MA 02215, USA
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11
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Chen Y, Al-Nusaif M, Li S, Tan X, Yang H, Cai H, Le W. Progress on early diagnosing Alzheimer's disease. Front Med 2024; 18:446-464. [PMID: 38769282 PMCID: PMC11391414 DOI: 10.1007/s11684-023-1047-1] [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: 09/23/2023] [Accepted: 11/15/2023] [Indexed: 05/22/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects both cognition and non-cognition functions. The disease follows a continuum, starting with preclinical stages, progressing to mild cognitive and behavioral impairment, ultimately leading to dementia. Early detection of AD is crucial for better diagnosis and more effective treatment. However, the current AD diagnostic tests of biomarkers using cerebrospinal fluid and/or brain imaging are invasive or expensive, and mostly are still not able to detect early disease state. Consequently, there is an urgent need to develop new diagnostic techniques with higher sensitivity and specificity during the preclinical stages of AD. Various non-cognitive manifestations, including behavioral abnormalities, sleep disturbances, sensory dysfunctions, and physical changes, have been observed in the preclinical AD stage before occurrence of notable cognitive decline. Recent research advances have identified several biofluid biomarkers as early indicators of AD. This review focuses on these non-cognitive changes and newly discovered biomarkers in AD, specifically addressing the preclinical stages of the disease. Furthermore, it is of importance to explore the potential for developing a predictive system or network to forecast disease onset and progression at the early stage of AD.
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Affiliation(s)
- Yixin Chen
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, 116021, China
| | - Murad Al-Nusaif
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, 116021, China
| | - Song Li
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, 116021, China
| | - Xiang Tan
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, 116021, China
| | - Huijia Yang
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, 116021, China
| | - Huaibin Cai
- Transgenic Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Weidong Le
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, 116021, China.
- Institute of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, 610072, China.
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12
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Wei YC, Kung YC, Lin CP, Chen CK, Lin C, Tseng RY, Chen YL, Huang WY, Chen PY, Chong ST, Shyu YC, Chang WC, Yeh CH. White matter alterations and their associations with biomarkers and behavior in subjective cognitive decline individuals: a fixel-based analysis. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2024; 20:12. [PMID: 38778325 PMCID: PMC11110460 DOI: 10.1186/s12993-024-00238-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 05/04/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD. METHODS Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018-2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states, physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured using an immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments. RESULTS The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC (pFWE < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aβ42 level (r = -0.38, p = 0.01) and positively with the AD8 score for subjective cognitive complaints (r = 0.42, p = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r = 0.35, p = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum (pFWE < 0.05) and with fiber-bundle cross-section at the right thalamus (pFWE < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score (pFWE < 0.01) and with fatigue to leisure activities (pFWE < 0.05). CONCLUSION Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aβ42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.
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Affiliation(s)
- Yi-Chia Wei
- Department of Neurology, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Yi-Chia Kung
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Chih-Ken Chen
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
| | - Chemin Lin
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
| | - Rung-Yu Tseng
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 333, Taiwan
| | - Yao-Liang Chen
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Radiology, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
| | - Wen-Yi Huang
- Department of Neurology, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
| | - Pin-Yuan Chen
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
| | - Shin-Tai Chong
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Yu-Chiau Shyu
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- Department of Nursing, Chang Gung University of Science and Technology, Taoyuan, 333, Taiwan
| | - Wei-Chou Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan
| | - Chun-Hung Yeh
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 333, Taiwan.
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, 333, Taiwan.
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13
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Huang Z, Hamblin MR, Zhang Q. Photobiomodulation in experimental models of Alzheimer's disease: state-of-the-art and translational perspectives. Alzheimers Res Ther 2024; 16:114. [PMID: 38773642 PMCID: PMC11106984 DOI: 10.1186/s13195-024-01484-x] [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: 02/07/2024] [Accepted: 05/15/2024] [Indexed: 05/24/2024]
Abstract
Alzheimer's disease (AD) poses a significant public health problem, affecting millions of people across the world. Despite decades of research into therapeutic strategies for AD, effective prevention or treatment for this devastating disorder remains elusive. In this review, we discuss the potential of photobiomodulation (PBM) for preventing and alleviating AD-associated pathologies, with a focus on the biological mechanisms underlying this therapy. Future research directions and guidance for clinical practice for this non-invasive and non-pharmacological therapy are also highlighted. The available evidence indicates that different treatment paradigms, including transcranial and systemic PBM, along with the recently proposed remote PBM, all could be promising for AD. PBM exerts diverse biological effects, such as enhancing mitochondrial function, mitigating the neuroinflammation caused by activated glial cells, increasing cerebral perfusion, improving glymphatic drainage, regulating the gut microbiome, boosting myokine production, and modulating the immune system. We suggest that PBM may serve as a powerful therapeutic intervention for AD.
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Affiliation(s)
- Zhihai Huang
- Department of Neurology, Louisiana State University Health Sciences Center, 1501 Kings Highway, Shreveport, LA, 71103, USA
- Department of Pharmacology, Toxicology & Neuroscience, Louisiana State University Health Sciences Center, 1501 Kings Highway, Shreveport, LA, 71103, USA
| | - Michael R Hamblin
- Laser Research Centre, University of Johannesburg, Doornfontein, 2028, South Africa.
| | - Quanguang Zhang
- Department of Neurology, Louisiana State University Health Sciences Center, 1501 Kings Highway, Shreveport, LA, 71103, USA.
- Department of Pharmacology, Toxicology & Neuroscience, Louisiana State University Health Sciences Center, 1501 Kings Highway, Shreveport, LA, 71103, USA.
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14
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Riverol M, Ríos-Rivera MM, Imaz-Aguayo L, Solis-Barquero SM, Arrondo C, Montoya-Murillo G, Villino-Rodríguez R, García-Eulate R, Domínguez P, Fernández-Seara MA. Structural neuroimaging changes associated with subjective cognitive decline from a clinical sample. Neuroimage Clin 2024; 42:103615. [PMID: 38749146 PMCID: PMC11109886 DOI: 10.1016/j.nicl.2024.103615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is characterized by progressive deterioration of cognitive functions. Some individuals with subjective cognitive decline (SCD) are in the early phase of the disease and subsequently progress through the AD continuum. Although neuroimaging biomarkers could be used for the accurate and early diagnosis of preclinical AD, the findings in SCD samples have been heterogeneous. This study established the morphological differences in brain magnetic resonance imaging (MRI) findings between individuals with SCD and those without cognitive impairment based on a clinical sample of patients defined according to SCD-Initiative recommendations. Moreover, we investigated baseline structural changes in the brains of participants who remained stable or progressed to mild cognitive impairment or dementia. METHODS This study included 309 participants with SCD and 43 healthy controls (HCs) with high-quality brain MRI at baseline. Among the 99 subjects in the SCD group who were followed clinically, 32 progressed (SCDp) and 67 remained stable (SCDnp). A voxel-wise statistical comparison of gray and white matter (WM) volume was performed between the HC and SCD groups and between the HC, SCDp, and SCDnp groups. XTRACT ATLAS was used to define the anatomical location of WM tract damage. Region-of-interest (ROI) analyses were performed to determine brain volumetric differences. White matter lesion (WML) burden was established in each group. RESULTS Voxel-based morphometry (VBM) analysis revealed that the SCD group exhibited gray matter atrophy in the middle frontal gyri, superior orbital gyri, superior frontal gyri, right rectal gyrus, whole occipital lobule, and both thalami and precunei. Meanwhile, ROI analysis revealed decreased volume in the left rectal gyrus, bilateral medial orbital gyri, middle frontal gyri, superior frontal gyri, calcarine fissure, and left thalamus. The SCDp group exhibited greater hippocampal atrophy (p < 0.001) than the SCDnp and HC groups on ROI analyses. On VBM analysis, however, the SCDp group exhibited increased hippocampal atrophy only when compared to the SCDnp group (p < 0.001). The SCD group demonstrated lower WM volume in the uncinate fasciculus, cingulum, inferior fronto-occipital fasciculus, anterior thalamic radiation, and callosum forceps than the HC group. However, no significant differences in WML number (p = 0.345) or volume (p = 0.156) were observed between the SCD and HC groups. CONCLUSIONS The SCD group showed brain atrophy mainly in the frontal and occipital lobes. However, only the SCDp group demonstrated atrophy in the medial temporal lobe at baseline. Structural damage in the brain regions was anatomically connected, which may contribute to early memory decline.
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Affiliation(s)
- Mario Riverol
- Department of Neurology, Clínica Universidad de Navarra, Pamplona 31008, Navarra, Spain; Instituto de Investigación Sanitaria de Navarra, Pamplona 31008, Navarra, Spain.
| | - Mirla M Ríos-Rivera
- Department of Neurology, Clínica Universidad de Navarra, Pamplona 31008, Navarra, Spain; School of Medicine, Universidad Autónoma de Chiriquí, David 4001, Chiriquí, Panama
| | - Laura Imaz-Aguayo
- Department of Neurology, Clínica Universidad de Navarra, Pamplona 31008, Navarra, Spain
| | | | - Carlota Arrondo
- Department of Neurology, Clínica Universidad de Navarra, Pamplona 31008, Navarra, Spain
| | | | | | - Reyes García-Eulate
- Department of Radiology, Clínica Universidad de Navarra, Pamplona 31008, Navarra, Spain
| | - Pablo Domínguez
- Department of Radiology, Clínica Universidad de Navarra, Pamplona 31008, Navarra, Spain; Instituto de Investigación Sanitaria de Navarra, Pamplona 31008, Navarra, Spain
| | - Maria A Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona 31008, Navarra, Spain; Instituto de Investigación Sanitaria de Navarra, Pamplona 31008, Navarra, Spain
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15
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Flaherty R, Sui YV, Masurkar AV, Betensky RA, Rusinek H, Lazar M. Diffusion imaging markers of accelerated aging of the lower cingulum in subjective cognitive decline. Front Neurol 2024; 15:1360273. [PMID: 38784911 PMCID: PMC11111894 DOI: 10.3389/fneur.2024.1360273] [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: 12/22/2023] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Introduction Alzheimer's Disease (AD) typically starts in the medial temporal lobe, then develops into a neurodegenerative cascade which spreads to other brain regions. People with subjective cognitive decline (SCD) are more likely to develop dementia, especially in the presence of amyloid pathology. Thus, we were interested in the white matter microstructure of the medial temporal lobe in SCD, specifically the lower cingulum bundle that leads into the hippocampus. Diffusion tensor imaging (DTI) has been shown to differentiate SCD participants who will progress to mild cognitive impairment from those who will not. However, the biology underlying these DTI metrics is unclear, and results in the medial temporal lobe have been inconsistent. Methods To better characterize the microstructure of this region, we applied DTI to cognitively normal participants in the Cam-CAN database over the age of 55 with cognitive testing and diffusion MRI available (N = 325, 127 SCD). Diffusion MRI was processed to generate regional and voxel-wise diffusion tensor values in bilateral lower cingulum white matter, while T1-weighted MRI was processed to generate regional volume and cortical thickness in the medial temporal lobe white matter, entorhinal cortex, temporal pole, and hippocampus. Results SCD participants had thinner cortex in bilateral entorhinal cortex and right temporal pole. No between-group differences were noted for any of the microstructural metrics of the lower cingulum. However, correlations with delayed story recall were significant for all diffusion microstructure metrics in the right lower cingulum in SCD, but not in controls, with a significant interaction effect. Additionally, the SCD group showed an accelerated aging effect in bilateral lower cingulum with MD, AxD, and RD. Discussion The diffusion profiles observed in both interaction effects are suggestive of a mixed neuroinflammatory and neurodegenerative pathology. Left entorhinal cortical thinning correlated with decreased FA and increased RD, suggestive of demyelination. However, right entorhinal cortical thinning also correlated with increased AxD, suggestive of a mixed pathology. This may reflect combined pathologies implicated in early AD. DTI was more sensitive than cortical thickness to the associations between SCD, memory, and age. The combined effects of mixed pathology may increase the sensitivity of DTI metrics to variations with age and cognition.
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Affiliation(s)
- Ryn Flaherty
- Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY, United States
| | - Yu Veronica Sui
- Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Arjun V. Masurkar
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
| | - Rebecca A. Betensky
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, United States
| | - Henry Rusinek
- Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
| | - Mariana Lazar
- Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
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Zhu QQ, Tian S, Zhang L, Ding HY, Gao YX, Tang Y, Yang X, Zhu Y, Qi M. Altered dynamic amplitude of low-frequency fluctuation in individuals at high risk for Alzheimer's disease. Eur J Neurosci 2024; 59:2391-2402. [PMID: 38314647 DOI: 10.1111/ejn.16267] [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: 03/31/2023] [Revised: 01/12/2024] [Accepted: 01/14/2024] [Indexed: 02/06/2024]
Abstract
The brain's dynamic spontaneous neural activity is significant in supporting cognition; however, how brain dynamics go awry in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remains unclear. Thus, the current study aimed to investigate the dynamic amplitude of low-frequency fluctuation (dALFF) alterations in patients at high risk for Alzheimer's disease and to explore its correlation with clinical cognitive assessment scales, to identify an early imaging sign for these special populations. A total of 152 participants, including 72 SCD patients, 44 MCI patients and 36 healthy controls (HCs), underwent a resting-state functional magnetic resonance imaging and were assessed with various neuropsychological tests. The dALFF was measured using sliding-window analysis. We employed canonical correlation analysis (CCA) to examine the bi-multivariate correlations between neuropsychological scales and altered dALFF among multiple regions in SCD and MCI patients. Compared to those in the HC group, both the MCI and SCD groups showed higher dALFF values in the right opercular inferior frontal gyrus (voxel P < .001, cluster P < .05, correction). Moreover, the CCA models revealed that behavioural tests relevant to inattention correlated with the dALFF of the right middle frontal gyrus and right opercular inferior frontal gyrus, which are involved in frontoparietal networks (R = .43, P = .024). In conclusion, the brain dynamics of neural activity in frontal areas provide insights into the shared neural basis underlying SCD and MCI.
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Affiliation(s)
- Qin-Qin Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shui Tian
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ling Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hong-Yuan Ding
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ya-Xin Gao
- Rehabilitation Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Yin Tang
- Department of Medical imaging, Jingjiang People's Hospital, Jingjiang, China
| | - Xi Yang
- School of Rehabilitation Medicine, Nanjing Medical University, Nanjing, China
| | - Yi Zhu
- Rehabilitation Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ming Qi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Yu X, Sun X, Wei M, Deng S, Zhang Q, Guo T, Shao K, Zhang M, Jiang J, Han Y. Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer's Disease Conversion: Evidence from a 2-Cohort Longitudinal Study. RESEARCH (WASHINGTON, D.C.) 2024; 7:0354. [PMID: 38711474 PMCID: PMC11070845 DOI: 10.34133/research.0354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/21/2024] [Indexed: 05/08/2024]
Abstract
To explore the complementary relationship between magnetic resonance imaging (MRI) radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer's disease (AD), our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD. This longitudinal multicentric cohort study included 2 independent cohorts: the Sino Longitudinal Study on Cognitive Decline (SILCODE) project and the Alzheimer Disease Neuroimaging Initiative (ADNI). We collected comprehensive assessments, MRI, plasma samples, and amyloid positron emission tomography data. A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator. The optimal model's performance and generalizability were assessed across populations in 2 cross-racial cohorts. A total of 897 subjects were included, including 635 from the SILCODE cohort (mean [SD] age, 64.93 [6.78] years; 343 [63%] female) and 262 from the ADNI cohort (mean [SD] age, 73.96 [7.06] years; 140 [53%] female). The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset, respectively. A calibration analysis displayed excellent consistency between the prognosis and actual observation. The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker. Importantly, built upon data-driven analyses commonly seen in previous radiomics studies, our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.
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Affiliation(s)
- Xianfeng Yu
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Xiaoming Sun
- Institute of Biomedical Engineering, School of Life Science,
Shanghai University, Shanghai 200444, China
| | - Min Wei
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Shuqing Deng
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Qi Zhang
- Institute of Biomedical Engineering, School of Life Science,
Shanghai University, Shanghai 200444, China
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Kai Shao
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Mingkai Zhang
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science,
Shanghai University, Shanghai 200444, China
| | - Ying Han
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
- Center of Alzheimer’s Disease,
Beijing Institute for Brain Disorders, Beijing 100069, China
- National Clinical Research Center for Geriatric Disorders, Beijing 100053, China
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18
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Ding X, Shi J, Wang Q, Chen H, Shi X, Li Z. Research hotspots and nursing inspiration in research of older adults with subjective cognitive decline from 2003 to 2023: A bibliometric analysis. Int J Nurs Sci 2024; 11:222-232. [PMID: 38707682 PMCID: PMC11064555 DOI: 10.1016/j.ijnss.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/25/2024] [Accepted: 03/05/2024] [Indexed: 05/07/2024] Open
Abstract
Objectives There has been a significant increase in subjective cognitive decline (SCD) studies in older adults over the years. A bibliometric analysis was conducted to demonstrate the current hotspots and emerging trends in SCD research in older adults and provide references for further research in this field. Methods The study conducted a bibliometric analysis based on co-citation analysis. From the Web of Science Core Collection database, this study obtained 1,436 manuscripts regarding SCD in older adults published from 2003 to 2023. Software CiteSpace was used to analyse the results for countries, institutions, authors, journals, keywords, top-cited papers, and burst citations scientifically and intuitively. Results Our result showed an overall upward trend in the volume of publications on SCD in the elderly population, suggesting that the study of SCD in older adults has attracted the attention of researchers. The United States dominates this research field, followed by China and France. The top three institutions were the University of California System, Institut National de la Sante et de la Recherche Medicale (Inserm), and UDICE-French Research Universities. Frank Jessen, Han Ying, and Kathryn Ellis were the top three researchers. The top three cited journals were Neurology, Alzheimers & Dementia, and Journal of Alzheimer's Disease. The keywords clustering were "depression", "functional connectivity", "cognitive reserve", "cognitive function", "physical activity", "recommendations", "dementia prevention", " behavioral disorders", "primary care", "early diagnosis", and "community-based study". Keywords with the highest citation bursts include "physical activity", "framework", "preclinical Alzheimer's disease", "future dementia", and "late life depression". Conclusions Parallel to the growth trend, the range of research scopes and topics is expanding steadily, focusing on early screening and prevention, negative emotion, and symptom management, broadening researchers' perspectives, which can provide reference and guidance for nursing researchers to conduct research.
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Affiliation(s)
- Xiaotong Ding
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jiyuan Shi
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Qing Wang
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- School of Nursing, Lanzhou University, Lanzhou, China
| | - Hongli Chen
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiuxiu Shi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Zheng Li
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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19
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Medrano J, Friston K, Zeidman P. Linking fast and slow: The case for generative models. Netw Neurosci 2024; 8:24-43. [PMID: 38562283 PMCID: PMC10861163 DOI: 10.1162/netn_a_00343] [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: 08/08/2023] [Accepted: 10/11/2023] [Indexed: 04/04/2024] Open
Abstract
A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.
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Affiliation(s)
- Johan Medrano
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Peter Zeidman
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
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20
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Liu J, Roccati E, Chen Y, Zhu Z, Wang W, He M, Shang X. Seasonal Variations in Vitamin D Levels and the Incident Dementia Among Older Adults Aged ≥60 Years in the UK Biobank. J Alzheimers Dis Rep 2024; 8:411-422. [PMID: 38549631 PMCID: PMC10977452 DOI: 10.3233/adr-230077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/17/2024] [Indexed: 05/16/2024] Open
Abstract
Background Limited knowledge exists regarding the association between dementia incidence and vitamin D insufficiency/deficiency across seasons. Objective This study aimed to evaluate the impact of seasonal serum vitamin D (25(OH)D) levels on dementia and its subtypes, considering potential modifiers. Methods We analyzed 193,003 individuals aged 60-73 at baseline (2006-2010) from the UK Biobank cohort, with follow-up until 2018. 25(OH)D were measured at baseline, and incident dementia cases were identified through hospital records, death certificates, and self-reports. Results Out of 1,874 documented all-cause dementia cases, the median follow-up duration was 8.9 years. Linear and nonlinear associations between 25(OH)D and dementia incidence across seasons were observed. In multivariable-adjusted analysis, 25(OH)D deficiency was associated with a 1.5-fold (95% CIs: 1.2-2.0), 2.2-fold (1.5-3.0), 2.0-fold (1.5-2.7), and 1.7-fold (1.3-2.3) increased incidence of all-cause dementia in spring, summer, autumn, and winter, respectively. Adjusting for seasonal variations, 25(OH)D insufficiency and deficiency were associated with a 1.3-fold (1.1-1.4) and 1.8-fold (1.6-2.2) increased dementia incidence, respectively. This association remained significant across subgroups, including baseline age, gender, and education levels. Furthermore, 25(OH)D deficiency was associated with a 1.4-fold (1.1-1.8) and 1.5-fold (1.1-2.0) higher incidence of Alzheimer's disease and vascular dementia, respectively. These associations remained significant across all subgroups. Conclusions 25(OH)D deficiency is associated with an increased incidence of dementia and its subtypes throughout the year.
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Affiliation(s)
- Jiahao Liu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
| | - Eddy Roccati
- Wicking Dementia Research and Education Centre, University of Tasmania, TAS, Australia
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Yutong Chen
- Faculty of Medicine, Nursing and Health Science, Monash University, Clayton, VIC, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Experimental Ophthalmology, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xianwen Shang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
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21
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Liu D, Lu J, Wei L, Yao M, Yang H, Lv P, Wang H, Zhu Y, Zhu Z, Zhang X, Chen J, Yang QX, Zhang B. Olfactory deficit: a potential functional marker across the Alzheimer's disease continuum. Front Neurosci 2024; 18:1309482. [PMID: 38435057 PMCID: PMC10907997 DOI: 10.3389/fnins.2024.1309482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
Alzheimer's disease (AD) is a prevalent form of dementia that affects an estimated 32 million individuals globally. Identifying early indicators is vital for screening at-risk populations and implementing timely interventions. At present, there is an urgent need for early and sensitive biomarkers to screen individuals at risk of AD. Among all sensory biomarkers, olfaction is currently one of the most promising indicators for AD. Olfactory dysfunction signifies a decline in the ability to detect, identify, or remember odors. Within the spectrum of AD, impairment in olfactory identification precedes detectable cognitive impairments, including mild cognitive impairment (MCI) and even the stage of subjective cognitive decline (SCD), by several years. Olfactory impairment is closely linked to the clinical symptoms and neuropathological biomarkers of AD, accompanied by significant structural and functional abnormalities in the brain. Olfactory behavior examination can subjectively evaluate the abilities of olfactory identification, threshold, and discrimination. Olfactory functional magnetic resonance imaging (fMRI) can provide a relatively objective assessment of olfactory capabilities, with the potential to become a promising tool for exploring the neural mechanisms of olfactory damage in AD. Here, we provide a timely review of recent literature on the characteristics, neuropathology, and examination of olfactory dysfunction in the AD continuum. We focus on the early changes in olfactory indicators detected by behavioral and fMRI assessments and discuss the potential of these techniques in MCI and preclinical AD. Despite the challenges and limitations of existing research, olfactory dysfunction has demonstrated its value in assessing neurodegenerative diseases and may serve as an early indicator of AD in the future.
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Affiliation(s)
- Dongming Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Liangpeng Wei
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Mei Yao
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Huiquan Yang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Pin Lv
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Haoyao Wang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yajing Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhengyang Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Qing X. Yang
- Department of Radiology, Center for NMR Research, Penn State University College of Medicine, Hershey, PA, United States
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, China
- Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Provincial Medical Key Discipline (Laboratory), Nanjing, China
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Li X, Zhang W, Bi Y, Chen J, Fu L, Zhang Z, Chen Q, Zhang X, Zhu Z, Zhang B. Non-alcoholic fatty liver disease is associated with brain function disruption in type 2 diabetes patients without cognitive impairment. Diabetes Obes Metab 2024; 26:650-662. [PMID: 37961040 DOI: 10.1111/dom.15354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023]
Abstract
AIMS To investigate the neural static and dynamic intrinsic activity of intra-/inter-network topology among patients with type 2 diabetes (T2D) with non-alcoholic fatty liver disease (NAFLD) and those without NAFLD (T2NAFLD group and T2noNAFLD group, respectively) and to assess the relationship with metabolism. METHODS Fifty-six patients with T2NAFLD, 78 with T2noNAFLD, and 55 healthy controls (HCs) were recruited to the study. Participants had normal cognition and underwent functional magnetic resonance imaging scans, clinical measurements, and global cognition evaluation. Independent component analysis was used to identify frequency spectrum parameters, static functional network connectivity, and temporal properties of dynamic functional network connectivity (P < 0.05, false discovery rate-corrected). Statistical analysis involved one-way analysis of covariance with post hoc, partial correlation and canonical correlation analyses. RESULTS Our findings showed that: (i) T2NAFLD patients had more disordered glucose and lipid metabolism, had more severe insulin resistance, and were more obese than T2noNAFLD patients; (ii) T2D patients exhibited disrupted brain function, as evidenced by alterations in intra-/inter-network topology, even without clinically measurable cognitive impairment; (iii) T2NAFLD patients had more significant reductions in the frequency spectrum parameters of cognitive executive and visual networks than those with T2noNAFLD; and (iv) altered brain function in T2D patients was correlated with postprandial glucose, high-density lipoprotein cholesterol, and waist-hip ratio. CONCLUSION This study may provide novel insights into neuroimaging correlates for underlying pathophysiological processes inducing brain damage in T2NAFLD. Thus, controlling blood glucose levels, lipid levels and abdominal obesity may reduce brain damage risk in such patients.
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Affiliation(s)
- Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Wen Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Yan Bi
- Department of Endocrinology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Linqing Fu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhou Zhang
- Department of Endocrinology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qian Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Zhengyang Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Institute of Brain Science, Nanjing University, Nanjing, China
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Liu M, Huang Q, Huang L, Ren S, Cui L, Zhang H, Guan Y, Guo Q, Xie F, Shen D. Dysfunctions of multiscale dynamic brain functional networks in subjective cognitive decline. Brain Commun 2024; 6:fcae010. [PMID: 38304005 PMCID: PMC10833653 DOI: 10.1093/braincomms/fcae010] [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: 05/30/2023] [Revised: 11/22/2023] [Accepted: 01/15/2024] [Indexed: 02/03/2024] Open
Abstract
Subjective cognitive decline is potentially the earliest symptom of Alzheimer's disease, whose objective neurological basis remains elusive. To explore the potential biomarkers for subjective cognitive decline, we developed a novel deep learning method based on multiscale dynamical brain functional networks to identify subjective cognitive declines. We retrospectively constructed an internal data set (with 112 subjective cognitive decline and 64 healthy control subjects) to develop and internally validate the deep learning model. Conventional deep learning methods based on static and dynamic brain functional networks are compared. After the model is established, we prospectively collect an external data set (26 subjective cognitive decline and 12 healthy control subjects) for testing. Meanwhile, our method provides monitoring of the transitions between normal and abnormal (subjective cognitive decline-related) dynamical functional network states. The features of abnormal dynamical functional network states are quantified by network and variability metrics and associated with individual cognitions. Our method achieves an area under the receiver operating characteristic curve of 0.807 ± 0.046 in the internal validation data set and of 0.707 (P = 0.007) in the external testing data set, which shows improvements compared to conventional methods. The method further suggests that, at the local level, the abnormal dynamical functional network states are characterized by decreased connectivity strength and increased connectivity variability at different spatial scales. At the network level, the abnormal states are featured by scale-specifically altered modularity and all-scale decreased efficiency. Low tendencies to stay in abnormal states and high state transition variabilities are significantly associated with high general, language and executive functions. Overall, our work supports the deficits in multiscale brain dynamical functional networks detected by the deep learning method as reliable and meaningful neural alternation underpinning subjective cognitive decline.
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Affiliation(s)
- Mianxin Liu
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
- School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China
| | - Qi Huang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Lin Huang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China
| | - Shuhua Ren
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Liang Cui
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China
| | - Han Zhang
- School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China
| | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Dinggang Shen
- School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
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Mandino F, Shen X, Desrosiers-Gregoire G, O'Connor D, Mukherjee B, Owens A, Qu A, Onofrey J, Papademetris X, Chakravarty MM, Strittmatter SM, Lake EM. Aging-Dependent Loss of Connectivity in Alzheimer's Model Mice with Rescue by mGluR5 Modulator. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.15.571715. [PMID: 38260465 PMCID: PMC10802481 DOI: 10.1101/2023.12.15.571715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD ( App NL-G-F /hMapt ), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.
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Rodini M, Bonarota S, Serra L, Caltagirone C, Carlesimo GA. Could Accelerated Long-Term Forgetting Be a Feature of the Higher Rate of Memory Complaints Associated with Subjective Cognitive Decline? An Exploratory Study. J Alzheimers Dis 2024; 100:1165-1182. [PMID: 39031357 DOI: 10.3233/jad-240218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
Background Recently, subjective cognitive decline (SCD) was proposed as an early risk factor for future Alzheimer's disease (AD). Objective In this study, we investigated whether accelerated long-term forgetting (ALF), assessed with extended testing intervals than those adopted in clinical practice, might be a cognitive feature of SCD. Using an explorative MRI analysis of the SCD sample, we attempted to investigate the areas most likely involved in the ALF pattern. Methods We recruited 31 individuals with SCD from our memory clinic and subdivided them based on their rate of memory complaints into mild SCDs (n = 18) and severe SCDs (n = 13). A long-term forgetting procedure, involving the recall of verbal and visuo-spatial material at four testing delays (i.e., immediate, 30 min, 24 h, and 7 days post-encoding) was used to compare the two sub-groups of SCDs with a healthy control group (HC; n = 16). Results No significant between-group difference was found on the standard neuropsychological tests, nor in the immediate and 30 min recall of the experimental procedure. By contrast, on the verbal test severe SCDs forgot significantly more than HCs in the prolonged intervals (i.e., 24 h and 7 days), with the greatest decline between 30 min and 24 h. Finally, in the whole SCD sample, we found significant associations between functional connectivity values within some cortical networks involved in memory (default mode network, salience network, and fronto-parietal network) and verbal long-term measures. Conclusions Our preliminary findings suggest that long-term forgetting procedures could be a sensitive neuropsychological tool for detecting memory concerns in SCDs, contributing to early AD detection.
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Affiliation(s)
- Marta Rodini
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychology of Memory, IRCSS Santa Lucia Foundation, Rome, Italy
| | - Sabrina Bonarota
- Department of Clinical Neuroscience and Neurorehabilitation, Neuroimaging Laboratory, IRCSS Santa Lucia Foundation, Rome, Italy
| | - Laura Serra
- Department of Clinical Neuroscience and Neurorehabilitation, Neuroimaging Laboratory, IRCSS Santa Lucia Foundation, Rome, Italy
| | - Carlo Caltagirone
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychology of Memory, IRCSS Santa Lucia Foundation, Rome, Italy
- Department of Clinical Neuroscience and Neurorehabilitation, Neuroimaging Laboratory, IRCSS Santa Lucia Foundation, Rome, Italy
| | - Giovanni Augusto Carlesimo
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychology of Memory, IRCSS Santa Lucia Foundation, Rome, Italy
- Department of Systems Medicine, Tor Vergata University, Rome, Italy
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Zhang M, Chen H, Huang W, Guo T, Ma G, Han Y, Shu N. Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum. Hum Brain Mapp 2024; 45:e26566. [PMID: 38224535 PMCID: PMC10785192 DOI: 10.1002/hbm.26566] [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: 08/23/2023] [Revised: 11/11/2023] [Accepted: 11/30/2023] [Indexed: 01/17/2024] Open
Abstract
Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0-T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including β-amyloid (Aβ), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10-24 ; quadratic trend: t = 7.708, p = 2.25 × 10-13 ; adjusted R2 = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10-10 ; quadratic trend: t = 3.896, p = 1.22 × 10-4 ; adjusted R2 = 0.330), p-tau181 (linear trend: t = 8.452, p = 1.61 × 10-15 ; quadratic trend: t = 6.316, p = 1.05 × 10-9 ; adjusted R2 = 0.346) and Aβ42/Aβ40 (linear trend: t = -3.257, p = 1.27 × 10-3 ; quadratic trend: t = -1.662, p = 9.76 × 10-2 ; adjusted R2 = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = -0.61, p = 6.3 × 10-7 ), NfL (R = -0.57, p = 6.4 × 10-6 ), and p-tau181 (R = -0.48, p = 2.0 × 10-4 ). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = -0.224, 95% confidence interval [CI] = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA) or NfL (ab = -0.224, 95% CI = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network-plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.
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Affiliation(s)
- Mingkai Zhang
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- BABRI CentreBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- BABRI CentreBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
| | - Tengfei Guo
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Guolin Ma
- Department of RadiologyChina‐Japan Friendship HospitalBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
- School of Biomedical EngineeringHainan UniversityHaikouChina
- National Clinical Research Center for Geriatric DiseasesBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- BABRI CentreBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
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Paban V, Mheich A, Spieser L, Sacher M. A multidimensional model of memory complaints in older individuals and the associated hub regions. Front Aging Neurosci 2023; 15:1324309. [PMID: 38187362 PMCID: PMC10771290 DOI: 10.3389/fnagi.2023.1324309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024] Open
Abstract
Memory complaints are highly prevalent among middle-aged and older adults, and they are frequently reported in individuals experiencing subjective cognitive decline (SCD). SCD has received increasing attention due to its implications for the early detection of dementia. This study aims to advance our comprehension of individuals with SCD by elucidating potential cognitive/psychologic-contributing factors and characterizing cerebral hubs within the brain network. To identify these potential contributing factors, a structural equation modeling approach was employed to investigate the relationships between various factors, such as metacognitive beliefs, personality, anxiety, depression, self-esteem, and resilience, and memory complaints. Our findings revealed that self-esteem and conscientiousness significantly influenced memory complaints. At the cerebral level, analysis of delta and theta electroencephalographic frequency bands recorded during rest was conducted to identify hub regions using a local centrality metric known as betweenness centrality. Notably, our study demonstrated that certain brain regions undergo changes in their hub roles in response to the pathology of SCD. Specifically, the inferior temporal gyrus and the left orbitofrontal area transition into hubs, while the dorsolateral prefrontal cortex and the middle temporal gyrus lose their hub function in the presence of SCD. This rewiring of the neural network may be interpreted as a compensatory response employed by the brain in response to SCD, wherein functional connectivity is maintained or restored by reallocating resources to other regions.
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Affiliation(s)
- Véronique Paban
- Aix-Marseille Université, CNRS, LNC (Laboratoire de Neurosciences Cognitives–UMR 7291), Marseille, France
| | - A. Mheich
- CHUV-Centre Hospitalier Universitaire Vaudois, Service des Troubles du Spectre de l’Autisme et Apparentés, Lausanne University Hospital, Lausanne, Switzerland
| | - L. Spieser
- Aix-Marseille Université, CNRS, LNC (Laboratoire de Neurosciences Cognitives–UMR 7291), Marseille, France
| | - M. Sacher
- University of Toulouse Jean-Jaurès, CNRS, LCLLE (Laboratoire Cognition, Langues, Langage, Ergonomie–UMR 5263), Toulouse, France
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Zhvania M, Japaridze N, Tizabi Y, Lomidze N, Pochkhidze N, Rzayev F, Gasimov E. Differential effects of aging on hippocampal ultrastructure in male vs. female rats. Biogerontology 2023; 24:925-935. [PMID: 37515624 DOI: 10.1007/s10522-023-10052-z] [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: 04/28/2023] [Accepted: 07/09/2023] [Indexed: 07/31/2023]
Abstract
Age-related decline in physical and cognitive functions are facts of life that do not affect everyone to the same extent. We had reported earlier that such cognitive decline is both sex- and context-dependent. Moreover, age-associated ultrastructural changes were observed in the hippocampus of male rats. In this study, we sought to determine potential differences in ultrastructural changes between male and female rats at various stages of life. We performed quantitative electron microscopic evaluation of hippocampal CA1 region, an area intimately involved in cognitive behavior, in both male and female adolescent, adult and old Wistar rats. Specifically, we measured the number of docking synaptic vesicles in axo-dendritic synapses, the length of active zone as well as the total number of synaptic vesicles. Distinct age- and sex-dependent effects were observed in several parameters. Thus, adult female rats had the lowest synaptic active zone compared to both adolescent and old female rats. Moreover, the same parameter was significantly lower in adult and old female rats compared to their male counterparts. On the other hand, old male rats had significantly lower number of total synaptic vesicles compared to both adolescent and adult male rats as well as compared to their female counterparts. Taken together, it may be suggested that age- and sex-dependent ultrastructural changes in the hippocampus may underlie at least some of the differences in cognitive functions among these groups.
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Affiliation(s)
- Mzia Zhvania
- School of Natural Sciences and Medicine, Ilia State University, 3/5 K. Cholokashvili Avenue, 0162, Tbilisi, Georgia.
- Department of Brain Ultrastructure and Nanoarchitecture, Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia.
| | - Nadezhda Japaridze
- Department of Brain Ultrastructure and Nanoarchitecture, Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia
- New Vision University, Tbilisi, Georgia
| | - Yousef Tizabi
- Department of Pharmacology, Howard University College of Medicine, Washington, DC, USA
| | - Nino Lomidze
- School of Natural Sciences and Medicine, Ilia State University, 3/5 K. Cholokashvili Avenue, 0162, Tbilisi, Georgia
- Department of Brain Ultrastructure and Nanoarchitecture, Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia
| | - Nino Pochkhidze
- School of Natural Sciences and Medicine, Ilia State University, 3/5 K. Cholokashvili Avenue, 0162, Tbilisi, Georgia
- Department of Brain Ultrastructure and Nanoarchitecture, Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia
| | - Fuad Rzayev
- Department of Histology, Embryology and Cytology, Azerbaijan Medical University, Baku, Azerbaijan
| | - Eldar Gasimov
- Department of Histology, Embryology and Cytology, Azerbaijan Medical University, Baku, Azerbaijan
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Fu Z, Zhao M, Li Y, He Y, Wang X, Zhou Z, Han Y, Li S. Heterogeneity in subjective cognitive decline in the Sino Longitudinal Study on Cognitive Decline(SILCODE): Empirically derived subtypes, structural and functional verification. CNS Neurosci Ther 2023; 29:4032-4042. [PMID: 37475187 PMCID: PMC10651943 DOI: 10.1111/cns.14327] [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: 01/15/2023] [Revised: 03/01/2023] [Accepted: 06/17/2023] [Indexed: 07/22/2023] Open
Abstract
AIMS We evaluated whether Subjective Cognitive Decline (SCD) subtypes could be empirically derived within the Sino Longitudinal Study on Cognitive Decline (SILCODE) SCD cohort and examined associated neuroimaging markers, biomarkers, and clinical outcomes. METHODS A cluster analysis was performed on eight neuropsychological test scores from 124 SCD SILCODE participants and 57 normal control (NC) subjects. Structural and functional neuroimaging indices were used to evaluate the SCD subgroups. RESULTS Four subtypes emerged: (1) dysexecutive/mixed SCD (n = 23), (2) neuropsychiatric SCD (n = 24), (3) amnestic SCD (n = 22), and (4) cluster-derived normal (n = 55) who exhibited normal performance in neuropsychological tests. Compared with the NC group, each subgroup showed distinct patterns in gray matter (GM) volume and the amplitude of low-frequency fluctuations (ALFF). Lower fractional anisotropy (FA) values were only found in the neuropsychiatric SCD group relative to NC. CONCLUSION The identification of empirically derived SCD subtypes demonstrates the presence of heterogeneity in SCD neuropsychological profiles. The cluster-derived normal group may represent the majority of SCD individuals who do not show progressive cognitive decline; the dysexecutive/mixed SCD and amnestic SCD might represent high-risk groups with progressing cognitive decline; and finally, the neuropsychiatric SCD may represent a new topic in SCD research.
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Affiliation(s)
- Zhenrong Fu
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU)Ministry of EducationWuhanChina
- School of Psychology, Key Laboratory of Human Development and Mental Health of Hubei ProvinceCentral China Normal UniversityWuhanChina
| | - Mingyan Zhao
- Department of PsychologyTangshan Gongren HospitalTangshanChina
| | - Yuxia Li
- Department of NeurologyTangshan Central HospitalTangshanChina
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Yirong He
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Xuetong Wang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Zongkui Zhou
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU)Ministry of EducationWuhanChina
- School of Psychology, Key Laboratory of Human Development and Mental Health of Hubei ProvinceCentral China Normal UniversityWuhanChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical EngineeringHainan UniversityHaikouChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
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Yang J, Liang L, Wei Y, Liu Y, Li X, Huang J, Zhang Z, Li L, Deng D. Altered cortical and subcortical morphometric features and asymmetries in the subjective cognitive decline and mild cognitive impairment. Front Neurol 2023; 14:1297028. [PMID: 38107635 PMCID: PMC10722314 DOI: 10.3389/fneur.2023.1297028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023] Open
Abstract
Introduction This study aimed to evaluate morphological changes in cortical and subcortical regions and their asymmetrical differences in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). These morphological changes may provide valuable insights into the early diagnosis and treatment of Alzheimer's disease (AD). Methods We conducted structural MRI scans on a cohort comprising 62 SCD patients, 97 MCI patients, and 70 age-, sex-, and years of education-matched healthy controls (HC). Using Freesurfer, we quantified surface area, thickness, the local gyrification index (LGI) of cortical regions, and the volume of subcortical nuclei. Asymmetry measures were also calculated. Additionally, we explored the correlation between morphological changes and clinical variables related to cognitive decline. Results Compared to HC, patients with MCI exhibited predominantly left-sided surface morphological changes in various brain regions, including the transverse temporal gyrus, superior temporal gyrus, insula, and pars opercularis. SCD patients showed relatively minor surface morphological changes, primarily in the insula and pars triangularis. Furthermore, MCI patients demonstrated reduced volumes in the anterior-superior region of the right hypothalamus, the fimbria of the bilateral hippocampus, and the anterior region of the left thalamus. These observed morphological changes were significantly associated with clinical ratings of cognitive decline. Conclusion The findings of this study suggest that cortical and subcortical morphometric changes may contribute to cognitive impairment in MCI, while compensatory mechanisms may be at play in SCD to preserve cognitive function. These insights have the potential to aid in the early diagnosis and treatment of AD.
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Affiliation(s)
- Jin Yang
- School of Medicine, Guangxi University, Nanning, Guangxi, China
| | - Lingyan Liang
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Yichen Wei
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Ying Liu
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Xiaocheng Li
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Jiazhu Huang
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Zhiguo Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Linling Li
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Demao Deng
- School of Medicine, Guangxi University, Nanning, Guangxi, China
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
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Xue C, Li J, Hao M, Chen L, Chen Z, Tang Z, Tang H, Fang Q. High prevalence of subjective cognitive decline in older Chinese adults: a systematic review and meta-analysis. Front Public Health 2023; 11:1277995. [PMID: 38106895 PMCID: PMC10722401 DOI: 10.3389/fpubh.2023.1277995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023] Open
Abstract
Background Subjective cognitive decline (SCD) is considered a preclinical stage of Alzheimer's disease. However, reliable prevalence estimates of SCD in the Chinese population are lacking, underscoring the importance of such metrics for policymakers to formulate appropriate healthcare strategies. Objective To systematically evaluate SCD prevalence among older Chinese adults. Methods PubMed, Web of Science, The Cochrane Library, Embase, CNKI, Wanfang, VIP, CBM, and Airiti Library databases were searched for studies on SCD in older Chinese individuals published before May 2023. Two investigators independently screened the literature, extracted the information, and assessed the bias risk of the included studies. A meta-analysis was then conducted using Stata 16.0 software via a random-effects model to analyze SCD prevalence in older Chinese adults. Results A total of 17 studies were included (n = 31,782). The SCD prevalence in older Chinese adults was 46.4% (95% CI, 40.6-52.2%). Further, subgroup analyzes indicated that SCD prevalence was 50.8% in men and 58.9% among women. Additionally, SCD prevalence in individuals aged 60-69, 70-79, and ≥ 80 years was 38.0, 45.2, and 60.3%, respectively. Furthermore, SCD prevalence in older adults with BMI <18.5, 18.5-24.0, and > 24.0 was 59.3, 54.0, and 52.9%, respectively. Geographically, SCD prevalence among older Chinese individuals was 41.3% in North China and 50.0% in South China. In terms of residence, SCD prevalence was 47.1% in urban residents and 50.0% among rural residents. As for retired individuals, SCD prevalence was 44.2% in non-manual workers and 49.2% among manual workers. In the case of education, individuals with an education level of "elementary school and below" had an SCD prevalence rate of 62.8%; "middle school, "52.4%; "high school, "55.0%; and "college and above, "51.3%. Finally, SCD prevalence was lower among married individuals with surviving spouses than in single adults who were divorced, widowed, or unmarried. Conclusion Our systematic review and meta-analysis identified significant and widespread SCD prevalence in the older population in China. Therefore, our review findings highlight the urgent requirement for medical institutions and policymakers across all levels to prioritize and rapidly develop and implement comprehensive preventive and therapeutic strategies for SCD.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023406950, identifier: CRD42023406950.
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Affiliation(s)
- Chao Xue
- School of Nursing, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Juan Li
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Mingqing Hao
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Lihua Chen
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Zuoxiu Chen
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Zeli Tang
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Huan Tang
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Qian Fang
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
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Wang L, Xu H, Wang M, Brendel M, Rominger A, Shi K, Han Y, Jiang J. A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans. Hum Brain Mapp 2023; 44:6020-6030. [PMID: 37740923 PMCID: PMC10619407 DOI: 10.1002/hbm.26493] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.
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Affiliation(s)
- Luyao Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Huanyu Xu
- School of Communication and Information EngineeringShanghai UniversityShanghaiChina
| | - Min Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Matthias Brendel
- Department of Nuclear MedicineUniversity Hospital of Munich, Ludwig Maximilian University of MunichMunichGermany
| | - Axel Rominger
- Department of Nuclear MedicineInselspital, University Hospital BernBernSwitzerland
| | - Kuangyu Shi
- Department of Nuclear MedicineInselspital, University Hospital BernBernSwitzerland
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Hainan UniversityHaikouChina
| | - Jiehui Jiang
- School of Life SciencesShanghai UniversityShanghaiChina
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Zhang T, Huang S, Lu Q, Song J, Teng J, Wang T, Shen Y. Effects of repetitive transcranial magnetic stimulation on episodic memory in patients with subjective cognitive decline: study protocol for a randomized clinical trial. Front Psychol 2023; 14:1298065. [PMID: 38022972 PMCID: PMC10646583 DOI: 10.3389/fpsyg.2023.1298065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Early decline of episodic memory is detectable in subjective cognitive decline (SCD). The left dorsolateral prefrontal cortex (DLPFC) is associated with encoding episodic memories. Repetitive transcranial magnetic stimulation (rTMS) is a novel and viable tool to improve cognitive function in Alzheimer's disease (AD) and mild cognitive impairment, but the treatment effect in SCD has not been studied. We aim to investigate the efficacy of rTMS on episodic memory in individuals with SCD, and to explore the potential mechanisms of neural plasticity. Methods In our randomized, sham-controlled trial, patients (n = 60) with SCD will receive 20 sessions (5 consecutive days per week for 4 weeks) of real rTMS (n = 30) or sham rTMS (n = 30) over the left DLPFC. The primary outcome is the Auditory Verbal Learning Test-Huashan version (AVLT-H). Other neuropsychological examinations and the long-term potentiation (LTP)-like cortical plasticity evaluation serve as the secondary outcomes. These outcomes will be assessed before and at the end of the intervention. Discussion If the episodic memory of SCD improve after the intervention, the study will confirm that rTMS is a promising intervention for cognitive function improvement on the early stage of dementia. This study will also provide important clinical evidence for early intervention in AD and emphasizes the significance that impaired LTP-like cortical plasticity may be a potential biomarker of AD prognosis by demonstrating the predictive role of LTP on cognitive improvement in SCD. Ethics and dissemination The study was approved by the Human Research Ethics Committee of the hospital (No. 2023-002-01). The results will be published in peer-review publications. Clinical trial registration https://www.chictr.org.cn/, identifier ChiCTR2300075517.
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Affiliation(s)
- Tianjiao Zhang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Sisi Huang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qian Lu
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Jie Song
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Teng
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tong Wang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ying Shen
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Cheng GR, Liu D, Huang LY, Han GB, Hu FF, Wu ZX, He XM, Huang YW, Yu YF, Xu L, Li JQ, Chen YS, Wei Z, Wu Q, Mei YF, Chen XX, Ou YM, Zhang JJ, Yang ML, Lian PF, Tan W, Xie XY, Zeng Y. Prevalence and risk factors for subjective cognitive decline and the correlation with objective cognition among community-dwelling older adults in China: Results from the Hubei memory and aging cohort study. Alzheimers Dement 2023; 19:5074-5085. [PMID: 37186161 DOI: 10.1002/alz.13047] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/18/2023] [Accepted: 02/23/2023] [Indexed: 05/17/2023]
Abstract
INTRODUCTION The prevalence and risk factors for subjective cognitive decline (SCD) and its correlation with objective cognition decline (OCD) among community-dwelling older adults is inconsistent. METHODS Older adults underwent neuropsychological and clinical evaluations to reach a consensus on diagnoses. RESULTS This study included 7486 adults without mild cognitive impairment and dementia (mean age: 71.35 years [standard deviation = 5.40]). The sex-, age-, and residence-adjusted SCD prevalence was 58.33% overall (95% confidence interval: 58.29% to 58.37%), with higher rates of 61.25% and 59.87% in rural and female subgroups, respectively. SCD global and OCD language, SCD memory and OCD global, SCD and OCD memory, and SCD and OCD language were negatively correlated in fully adjusted models. Seven health and lifestyle factors were associated with an increased risk for SCD. DISCUSSION SCD affected 58.33% of older adults and may indicate concurrent OCD, which should prompt the initiation of preventative intervention for dementia. HIGHLIGHTS SCD affects 58.33% of older adults in China. SCD may indicate concurrent objective cognitive decline. Difficulty finding words and memory impairments may indicate a risk for AD. The presence of SCD may prompt preventative treatment initiation of MCI or dementia. Social network factors may be initial targets for the early prevention of SCD.
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Affiliation(s)
- Gui-Rong Cheng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Dan Liu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Lin-Ya Huang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Gang-Bin Han
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Fei-Fei Hu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | | | - Xiao-Ming He
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Wei Huang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Ya-Fu Yu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Lang Xu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Jin-Quan Li
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Shan Chen
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Zhen Wei
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Qiong Wu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Fei Mei
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Xing-Xing Chen
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yang-Ming Ou
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jing-Jing Zhang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Meng-Liu Yang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Peng-Fei Lian
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Wei Tan
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Xin-Yan Xie
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Yan Zeng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
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Wang H, Ma LZ, Sheng ZH, Liu JY, Yuan WY, Guo F, Zhang W, Tan L. Association between cerebrospinal fluid clusterin and biomarkers of Alzheimer's disease pathology in mild cognitive impairment: a longitudinal cohort study. Front Aging Neurosci 2023; 15:1256389. [PMID: 37941999 PMCID: PMC10629112 DOI: 10.3389/fnagi.2023.1256389] [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: 07/10/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Background Clusterin, a glycoprotein implicated in Alzheimer's disease (AD), remains unclear. The objective of this study was to analyze the effect of cerebrospinal fluid (CSF) clusterin in relation to AD biomarkers using a longitudinal cohort of non-demented individuals. Methods We gathered a sample comprising 86 individuals under cognition normal (CN) and 134 patients diagnosed with MCI via the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. To investigate the correlation of CSF clusterin with cognitive function and markers of key physiological changes, we employed multiple linear regression and mixed-effect models. We undertook a causal mediation analysis to inspect the mediating influence of CSF clusterin on cognitive abilities. Results Pathological characteristics associated with baseline Aβ42, Tau, brain volume, exhibited a correlation with initial CSF clusterin in the general population, Specifically, these correlations were especially prominent in the MCI population; CSF Aβ42 (PCN = 0.001; PMCI = 0.007), T-tau (PCN < 0.001; PMCI < 0.001), and Mid temporal (PCN = 0.033; PMCI = 0.005). Baseline CSF clusterin level was predictive of measurable cognitive shifts in the MCI population, as indicated by MMSE (β = 0.202, p = 0.029), MEM (β = 0.186, p = 0.036), RAVLT immediate recall (β = 0.182, p = 0.038), and EF scores (β = 0.221, p = 0.013). In MCI population, the alterations in brain regions (17.87% of the total effect) mediated the effect of clusterin on cognition. It was found that variables such as age, gender, and presence of APOE ε4 carrier status, influenced some of these connections. Conclusion Our investigation underscored a correlation between CSF clusterin concentrations and pivotal AD indicators, while also highlighting clusterin's potential role as a protective factor for cognitive abilities in MCI patients.
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Affiliation(s)
- Hao Wang
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Ling-Zhi Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ze-Hu Sheng
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jia-Yao Liu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei-Yu Yuan
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Fan Guo
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
| | - Lan Tan
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
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Sheng C, Du W, Liang Y, Xu P, Ding Q, Chen X, Jia S, Wang X. An integrated neuroimaging-omics approach for the gut-brain communication pathways in Alzheimer's disease. Front Aging Neurosci 2023; 15:1211979. [PMID: 37869373 PMCID: PMC10587434 DOI: 10.3389/fnagi.2023.1211979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 09/22/2023] [Indexed: 10/24/2023] Open
Abstract
A key role of the gut microbiota in the pathogenesis of neurodegenerative diseases, such as Alzheimer's disease (AD), has been identified over the past decades. Increasing clinical and preclinical evidence implicates that there is bidirectional communication between the gut microbiota and the central nervous system (CNS), which is also known as the microbiota-gut-brain axis. Nevertheless, current knowledge on the interplay between gut microbiota and the brain remains largely unclear. One of the primary mediating factors by which the gut microbiota interacts with the host is peripheral metabolites, including blood or gut-derived metabolites. However, mechanistic knowledge about the effect of the microbiome and metabolome signaling on the brain is limited. Neuroimaging techniques, such as multi-modal magnetic resonance imaging (MRI), and fluorodeoxyglucose-positron emission tomography (FDG-PET), have the potential to directly elucidate brain structural and functional changes corresponding with alterations of the gut microbiota and peripheral metabolites in vivo. Employing a combination of gut microbiota, metabolome, and advanced neuroimaging techniques provides a future perspective in illustrating the microbiota-gut-brain pathway and further unveiling potential therapeutic targets for AD treatments.
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Affiliation(s)
- Can Sheng
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, China
| | - Wenying Du
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - Yuan Liang
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, China
| | - Peng Xu
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, China
| | - Qingqing Ding
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, China
| | - Xue Chen
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, China
| | - Shulei Jia
- Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Xiaoni Wang
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Miller-Winder AP, Schierer A, Relerford RR, Murawski A, Opsasnick L, Olvera C, Curtis LM, Kim KY, Ramirez-Zohfeld V, Lindquist LA. Subjective cognitive decline and missed aging-in-place/long-term care planning. J Am Geriatr Soc 2023; 71:3314-3316. [PMID: 37235515 PMCID: PMC10592648 DOI: 10.1111/jgs.18425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/05/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023]
Affiliation(s)
- Amber P. Miller-Winder
- Division of Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Allison Schierer
- Division of Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Raven R. Relerford
- Division of Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Alaine Murawski
- Division of Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Lauren Opsasnick
- Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Charles Olvera
- Division of Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Laura M. Curtis
- Division of Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Kwang-Youn Kim
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Vanessa Ramirez-Zohfeld
- Division of Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Lee A. Lindquist
- Division of Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
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Huang W, Zeng J, Jia L, Zhu D, O’Brien J, Ritchie C, Shu N, Su L. Genetic risks of Alzheimer's by APOE and MAPT on cortical morphology in young healthy adults. Brain Commun 2023; 5:fcad234. [PMID: 37693814 PMCID: PMC10489122 DOI: 10.1093/braincomms/fcad234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/29/2023] [Accepted: 08/30/2023] [Indexed: 09/12/2023] Open
Abstract
Genetic risk factors such as APOE ε4 and MAPT (rs242557) A allele are associated with amyloid and tau pathways and grey matter changes at both early and established stages of Alzheimer's disease, but their effects on cortical morphology in young healthy adults remain unclear. A total of 144 participants aged from 18 to 24 underwent 3T MRI and genotyping for APOE and MAPT to investigate unique impacts of these genetic risk factors in a cohort without significant comorbid conditions such as metabolic and cardiovascular diseases. We segmented the cerebral cortex into 68 regions and calculated the cortical area, thickness, curvature and folding index for each region. Then, we trained machine learning models to classify APOE and MAPT genotypes using these morphological features. In addition, we applied a growing hierarchical self-organizing maps algorithm, which clustered the 68 regions into 4 subgroups representing different morphological patterns. Then, we performed general linear model analyses to estimate the interaction between APOE and MAPT on cortical patterns. We found that the classifiers using all cortical features could accurately classify individuals carrying genetic risks of dementia outperforming each individual feature alone. APOE ε4 carriers had a more convoluted and thinner cortex across the cerebral cortex. A similar pattern was found in MAPT A allele carriers only in the regions that are vulnerable for early tau pathology. With the clustering analysis, we found a synergetic effect between APOE ε4 and MAPT A allele, i.e. carriers of both risk factors showed the most deviation of cortical pattern from the typical pattern of that cluster. Genetic risk factors of dementia by APOE ε4 and MAPT (rs242557) A allele were associated with variations of cortical morphology, which can be observed in young healthy adults more than 30 years before Alzheimer's pathology is likely to occur and 50 years before dementia symptoms may begin.
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Affiliation(s)
- Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Department of Neuroscience, Neuroscience Institute, Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield S10 2HQ, UK
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Jianmin Zeng
- Faculty of Psychology, Sino-Britain Centre for Cognition and Ageing Research, Southwest University, Chongqing 400715, China
| | - Lina Jia
- Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Dajiang Zhu
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
| | - John O’Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Craig Ritchie
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh EH4 2XU, UK
- Scottish Brain Sciences, Edinburgh EH12 9DQ, UK
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Li Su
- Department of Neuroscience, Neuroscience Institute, Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield S10 2HQ, UK
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SZ, UK
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Sehar U, Kopel J, Reddy PH. Alzheimer's disease and its related dementias in US Native Americans: A major public health concern. Ageing Res Rev 2023; 90:102027. [PMID: 37544432 PMCID: PMC10515314 DOI: 10.1016/j.arr.2023.102027] [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: 05/08/2023] [Revised: 07/25/2023] [Accepted: 08/03/2023] [Indexed: 08/08/2023]
Abstract
Alzheimer's disease (AD) and Alzheimer's related dementias (ADRD) are growing public health concerns in aged populations of all ethnic and racial groups. AD and ADRD are caused by multiple factors, such as genetic mutations, modifiable and non-modifiable risk factors, and lifestyle. Studies of postmortem brains have revealed multiple cellular changes implicated in AD and ADRD, including the accumulation of amyloid beta and phosphorylated tau, synaptic damage, inflammatory responses, hormonal imbalance, mitochondrial abnormalities, and neuronal loss. These changes occur in both early-onset familial and late-onset sporadic forms. Two-thirds of women and one-third of men are at life time risk for AD. A small proportion of total AD cases are caused by genetic mutations in amyloid precursor protein, presenilin 1, and presenilin 1 genes, and the APOE4 allele is a risk factor. Tremendous research on AD/ADRD, and other comorbidities such as diabetes, obesity, hypertension, and cancer has been done on almost all ethnic groups, however, very little biomedical research done on US Native Americans. AD/ADRD prevalence is high among all ethnic groups. In addition, US Native Americans have poorer access to healthcare and medical services and are less likely to receive a diagnosis once they begin to exhibit symptoms, which presents difficulties in treating Alzheimer's and other dementias. One in five US Native American people who are 45 years of age or older report having memory issues. Further, the impact of caregivers and other healthcare aspects on US Native Americans is not yet. In the current article, we discuss the history of Native Americans of United States (US) and health disparities, occurrence, and prevalence of AD/ADRD, and shedding light on the culturally sensitive caregiving practices in US Native Americans. This article is the first to discuss biomedical research and healthcare disparities in US Native Americans with a focus on AD and ADRD, we also discuss why US Native Americans are reluctant to participate in biomedical research.
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Affiliation(s)
- Ujala Sehar
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Jonathan Kopel
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - P Hemachandra Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Nutritional Sciences Department, College of Human Sciences, Texas Tech University, 1301 Akron Ave, Lubbock, TX 79409, USA; Neurology, Departments of School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Public Health Department of Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Speech, Language and Hearing Sciences, School Health Professions, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
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40
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Ding H, Wang Z, Tang Y, Wang T, Qi M, Dou W, Qian L, Gao Y, Zhong Q, Yang X, Tian H, Zhang L, Zhu Y. Topological properties of individual gray matter morphological networks in identifying the preclinical stages of Alzheimer's disease: a preliminary study. Quant Imaging Med Surg 2023; 13:5258-5270. [PMID: 37581056 PMCID: PMC10423385 DOI: 10.21037/qims-22-1373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/16/2023]
Abstract
Background Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are preclinical stages of Alzheimer's disease (AD). Individual biomarkers are essential for evaluating altered neurological outcomes at both SCD and MCI stages for early diagnosis and intervention of AD. In this study, we aimed to investigate the relationships between topological properties of the individual brain morphological network and clinical cognitive performances among healthy controls (HCs) and patients with SCD or MCI. Methods The topological measurements of individual morphological networks were analyzed using graph theory, and inter-group differences of standard graph topology were correlated and regressed to scores of clinical cognitive functions. Results Compared with HCs, the topology of the individual morphological networks in SCD and MCI patients was significantly altered. At the global level, altered topology was characterized by lower global efficiency, shorter characteristics path length, and normalized characteristics path length [all P<0.05, false discovery rate (FDR) corrected]. In addition, at the regional level, SCD and MCI patients exhibited abnormal degree centrality in the caudate nucleus and nodal efficiency in the caudate nucleus, right insula, lenticular nucleus, and putamen (all P<0.05, FDR corrected). Conclusions The topological features of individual gray matter morphological networks may serve as biomarkers to improve disease prognosis and intervention in the early stages of AD, namely SCD and MCI. Moreover, these findings may further elucidate the relationships between brain morphological alterations and cognitive dysfunctions in SCD and MCI.
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Affiliation(s)
- Hongyuan Ding
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhihao Wang
- School of Biological Science & Medical Engineering, Southeast University, Nanjing, China
| | - Yin Tang
- Department of Medical Imaging, Jingjiang People’s Hospital, Jingjiang, China
| | - Tong Wang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ming Qi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | | | - Long Qian
- MR Research, GE Healthcare, Beijing, China
| | - Yaxin Gao
- Department of Rehabilitation, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
- Gusu School, Nanjing Medical University, Suzhou, China
| | - Qian Zhong
- Department of Rehabilitation, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Xi Yang
- School of Rehabilitation Medicine, Nanjing Medical University, Nanjing, China
| | - Huifang Tian
- School of Rehabilitation Medicine, Nanjing Medical University, Nanjing, China
| | - Ling Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yi Zhu
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Zhang S, Dong H, Bian J, Li D, Liu C. Targeting amyloid proteins for clinical diagnosis of neurodegenerative diseases. FUNDAMENTAL RESEARCH 2023; 3:505-519. [PMID: 38933553 PMCID: PMC11197785 DOI: 10.1016/j.fmre.2022.10.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/16/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Abnormal aggregation and accumulation of pathological amyloid proteins such as amyloid-β, Tau, and α-synuclein play key pathological roles and serve as histological hallmarks in different neurodegenerative diseases (NDs) such as Alzheimer's disease (AD) and Parkinson's disease (PD). In addition, various post-translational modifications (PTMs) have been identified on pathological amyloid proteins and are subjected to change during disease progression. Given the central role of amyloid proteins in NDs, tremendous efforts have been made to develop amyloid-targeting strategies for clinical diagnosis and molecular classification of NDs. In this review, we summarize two major strategies for targeting amyloid aggregates, with a focus on the trials in AD diagnosis. The first strategy is a positron emission tomography (PET) scan of protein aggregation in the brain. We mainly focus on introducing the development of small-molecule PET tracers for specifically recognizing pathological amyloid fibrils. The second strategy is the detection of PTM biomarkers on amyloid proteins in cerebrospinal fluid and plasma. We discuss the pathological roles of different PTMs in diseases and how we can use the PTM profile of amyloid proteins for clinical diagnosis. Finally, we point out the potential technical challenges of these two strategies, and outline other potential strategies, as well as a combination of multiple strategies, for molecular diagnosis of NDs.
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Affiliation(s)
- Shenqing Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hui Dong
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Jiang Bian
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Dan Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
- Bio-X-Renji Hospital Research Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China
- Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Cong Liu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- State Key Laboratory of Bio-Organic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
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周 德, 金 益, 陈 瑛. [The application scenarios study on the intervention of cognitive decline in elderly population using metaverse technology]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:573-581. [PMID: 37380399 PMCID: PMC10307614 DOI: 10.7507/1001-5515.202208092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 04/02/2023] [Indexed: 06/30/2023]
Abstract
China is facing the peak of an ageing population, and there is an increase in demand for intelligent healthcare services for the elderly. The metaverse, as a new internet social communication space, has shown infinite potential for application. This paper focuses on the application of the metaverse in medicine in the intervention of cognitive decline in the elderly population. The problems in assessment and intervention of cognitive decline in the elderly group were analyzed. The basic data required to construct the metaverse in medicine was introduced. Moreover, it is demonstrated that the elderly users can conduct self-monitoring, experience immersive self-healing and health-care through the metaverse in medicine technology. Furthermore, we proposed that it is feasible that the metaverse in medicine has obvious advantages in prediction and diagnosis, prevention and rehabilitation, as well as assisting patients with cognitive decline. Risks for its application were pointed out as well. The metaverse in medicine technology solves the problem of non-face-to-face social communication for elderly users, which may help to reconstruct the social medical system and service mode for the elderly population.
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Affiliation(s)
- 德富 周
- 苏州市职业大学 江苏省现代企业信息化应用支撑软件工程技术研发中心(江苏苏州 215104)Suzhou Vocational University Jiangsu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, Jiangsu 215104, P. R. China
| | - 益 金
- 苏州市职业大学 江苏省现代企业信息化应用支撑软件工程技术研发中心(江苏苏州 215104)Suzhou Vocational University Jiangsu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, Jiangsu 215104, P. R. China
| | - 瑛 陈
- 苏州市职业大学 江苏省现代企业信息化应用支撑软件工程技术研发中心(江苏苏州 215104)Suzhou Vocational University Jiangsu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, Jiangsu 215104, P. R. China
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Hoxhaj P, Habiya SK, Sayabugari R, Balaji R, Xavier R, Ahmad A, Khanam M, Kachhadia MP, Patel T, Abdin ZU, Haider A, Nazir Z. Investigating the Impact of Epilepsy on Cognitive Function: A Narrative Review. Cureus 2023; 15:e41223. [PMID: 37525802 PMCID: PMC10387362 DOI: 10.7759/cureus.41223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2023] [Indexed: 08/02/2023] Open
Abstract
It has been noted that people who have epilepsy have an increased propensity for cognitive dysfunction. We explored 25 relevant articles on PubMed and Cochrane Library after implementing inclusion criteria. Different factors have been postulated and studied that may cause cognitive dysfunction in these patients; structural brain abnormalities, polypharmacy of antiepileptic medication, and neuropsychiatric disorders are the most common causes. Cognitive assessments such as Montreal Cognitive Assessment (MOCA) and Mini-Mental State Exam (MMSE) are the mainstay tools used to diagnose the degree of cognitive decline, and alterations in EEG (electroencephalogram) parameters have also been noted in people with cognitive decline. The mechanisms and treatments for cognitive decline are still being studied, while attention has also been directed toward preventive and predictive methods. Early detection and treatment of cognitive impairment can help minimize its impact on the patient's quality of life. Regular cognitive assessments are essential for epileptic patients, particularly those on multiple antiepileptic drugs. While proper management of epilepsy and related comorbidities would reduce cognitive decline and improve the overall quality of life for people with epilepsy.
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Affiliation(s)
- Pranvera Hoxhaj
- Medicine, University of Medicine, Tirana, Tirana, ALB
- Obstetrics and Gynaecology, Scher & Kerenyi MDS, New York, USA
| | - Sana K Habiya
- Internal Medicine, Indian Institute of Medical Science and Research, Jalna, IND
- Public Health, Northeastern Illinois University, Chicago, USA
| | | | - Roghan Balaji
- Neurology, Ponjesly Super Speciality Hospital, Nagercoil, IND
- Neurology, Sri Manakula Vinayagar Medical College and Hospital, Pondicherry, IND
| | - Roshni Xavier
- Internal Medicine, Rajagiri Hospital, Aluva, IND
- Internal Medicine, Carewell Hospital, Malappuram, IND
| | - Arghal Ahmad
- Internal Medicine, Ziauddin University, Karachi, PAK
| | | | | | - Tirath Patel
- Internal Medicine, American University of Antigua, St John, ATG
| | - Zain U Abdin
- Internal Medicine, District Head Quarter Hospital, Faisalabad, PAK
| | - Ali Haider
- Internal Medicine, Quetta Institute of Medical Sciences, Quetta, PAK
| | - Zahra Nazir
- Internal Medicine Clinical Research, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
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Göschel L, Kurz L, Dell'Orco A, Köbe T, Körtvélyessy P, Fillmer A, Aydin S, Riemann LT, Wang H, Ittermann B, Grittner U, Flöel A. 7T amygdala and hippocampus subfields in volumetry-based associations with memory: A 3-year follow-up study of early Alzheimer's disease. Neuroimage Clin 2023; 38:103439. [PMID: 37253284 PMCID: PMC10236463 DOI: 10.1016/j.nicl.2023.103439] [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: 03/02/2023] [Revised: 05/09/2023] [Accepted: 05/15/2023] [Indexed: 06/01/2023]
Abstract
INTRODUCTION The hippocampus is the most prominent single region of interest (ROI) for the diagnosis and prediction of Alzheimer's disease (AD). However, its suitability in the earliest stages of cognitive decline, i.e., subjective cognitive decline (SCD), remains uncertain which warrants the pursuit of alternative or complementary regions. The amygdala might be a promising candidate, given its implication in memory as well as other psychiatric disorders, e.g. depression and anxiety, which are prevalent in SCD. In this 7 tesla (T) magnetic resonance imaging (MRI) study, we aimed to compare the contribution of volumetric measurements of the hippocampus, the amygdala, and their respective subfields, for early diagnosis and prediction in an AD-related study population. METHODS Participants from a longitudinal study were grouped into SCD (n = 29), mild cognitive impairment (MCI, n = 23), AD (n = 22) and healthy control (HC, n = 31). All participants underwent 7T MRI at baseline and extensive neuropsychological testing at up to three visits (baseline n = 105, 1-year n = 78, 3-year n = 39). Analysis of covariance (ANCOVA) was used to assess group differences of baseline volumes of the amygdala and the hippocampus and their subfields. Linear mixed models were used to estimate the effects of baseline volumes on yearly changes of a z-scaled memory score. All models were adjusted to age, sex and education. RESULTS Compared to the HC group, individuals with SCD showed smaller amygdala ROI volumes (range across subfields -11% to -1%), but not hippocampus ROI volumes (-2% to 1%) except for the hippocampus-amygdala-transition-area (-7%). However, cross-sectional associations between baseline memory and volumes were smaller for amygdala ROIs (std. ß [95% CI] ranging between 0.16 [0.08; 0.25] and 0.46 [0.31; 0.60]) than hippocampus ROIs (between 0.32 [0.19; 0.44] and 0.53 [0.40; 0.67]). Further, the association of baseline volumes with yearly memory change in the HC and SCD groups was similarly weak for amygdala ROIs and hippocampus ROIs. In the MCI group, volumes of amygdala ROIs were associated with a relevant yearly memory decline [95% CI] ranging between -0.12 [-0.24; 0.00] and -0.26 [-0.42; -0.09] for individuals with 20% smaller volumes than the HC group. However, effects were stronger for hippocampus ROIs with a corresponding yearly memory decline ranging between -0.21 [-0.35; -0.07] and -0.31 [-0.50; -0.13]. CONCLUSION Volumes of amygdala ROIs, as determined by 7T MRI, might contribute to objectively and non-invasively identify patients with SCD, and thus aid early diagnosis and treatment of individuals at risk to develop dementia due to AD, however associations with other psychiatric disorders should be evaluated in further studies. The amygdala's value in the prediction of longitudinal memory changes in the SCD group remains questionable. Primarily in patients with MCI, memory decline over 3 years appears to be more strongly associated with volumes of hippocampus ROIs than amygdala ROIs.
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Affiliation(s)
- Laura Göschel
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NCRC - Neuroscience Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany.
| | - Lea Kurz
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NCRC - Neuroscience Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany
| | - Andrea Dell'Orco
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NCRC - Neuroscience Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuroradiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Theresa Köbe
- Deutsches Zentrum für Luft- und Raumfahrt e.V. Projektträger (DLR-PT), Berlin, Germany
| | - Peter Körtvélyessy
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE), site Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany
| | - Ariane Fillmer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Semiha Aydin
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Layla Tabea Riemann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany; Institute for Applied Medical Informatics, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Hui Wang
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NCRC - Neuroscience Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany; Department of Neurology and Pain Treatment, Immanuel Klinik Rüdersdorf, University Hospital of the Brandenburg Medical School Theodor Fontane, Rüdersdorf bei Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Ulrike Grittner
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Agnes Flöel
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany; German Center for Neurodegenerative Diseases (DZNE), Standort Rostock/Greifswald, Germany
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Chen Q, Chen F, Long C, Zhu Y, Jiang Y, Zhu Z, Lu J, Zhang X, Nedelska Z, Hort J, Zhang B. Spatial navigation is associated with subcortical alterations and progression risk in subjective cognitive decline. Alzheimers Res Ther 2023; 15:86. [PMID: 37098612 PMCID: PMC10127414 DOI: 10.1186/s13195-023-01233-6] [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: 12/04/2022] [Accepted: 04/18/2023] [Indexed: 04/27/2023]
Abstract
BACKGROUND Subjective cognitive decline (SCD) may serve as a symptomatic indicator for preclinical Alzheimer's disease; however, SCD is a heterogeneous entity regarding clinical progression. We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of progression to objective cognitive impairment in SCD individuals. METHODS One hundred and eighty participants were enrolled: those with SCD (n = 80), normal controls (NCs, n = 77), and mild cognitive impairment (MCI, n = 23). SCD participants were further divided into the SCD-good (G-SCD, n = 40) group and the SCD-bad (B-SCD, n = 40) group according to their spatial navigation performance. Volumes of subcortical structures were calculated and compared among the four groups, including basal forebrain, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Topological properties of the subcortical structural covariance network were also calculated. With an interval of 1.5 years ± 12 months of follow-up, the progression rate to MCI was compared between the G-SCD and B-SCD groups. RESULTS Volumes of the basal forebrain, the right hippocampus, and their respective subfields differed significantly among the four groups (p < 0.05, false discovery rate corrected). The B-SCD group showed lower volumes in the basal forebrain than the G-SCD group, especially in the Ch4p and Ch4a-i subfields. Furthermore, the structural covariance network of the basal forebrain and right hippocampal subfields showed that the B-SCD group had a larger Lambda than the G-SCD group, which suggested weakened network integration in the B-SCD group. At follow-up, the B-SCD group had a significantly higher conversion rate to MCI than the G-SCD group. CONCLUSION Compared to SCD participants with good spatial navigation performance, SCD participants with bad performance showed lower volumes in the basal forebrain, a reorganized structural covariance network of subcortical nuclei, and an increased risk of progression to MCI. Our findings indicated that spatial navigation may have great potential to identify SCD subjects at higher risk of clinical progression, which may contribute to making more precise clinical decisions for SCD individuals who seek medical help.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, 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
| | - Futao Chen
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Cong Long
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yajing Zhu
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, 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
| | - Yaoxian Jiang
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhengyang Zhu
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zuzana Nedelska
- Memory Clinic, Department of Neurology, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czechia
| | - Jakub Hort
- Memory Clinic, Department of Neurology, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czechia
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, 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.
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, China.
- Institute of Brain Science, Nanjing University, Nanjing, China.
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Lassi M, Fabbiani C, Mazzeo S, Burali R, Vergani AA, Giacomucci G, Moschini V, Morinelli C, Emiliani F, Scarpino M, Bagnoli S, Ingannato A, Nacmias B, Padiglioni S, Micera S, Sorbi S, Grippo A, Bessi V, Mazzoni A. Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum? Neuroimage Clin 2023; 38:103407. [PMID: 37094437 PMCID: PMC10149415 DOI: 10.1016/j.nicl.2023.103407] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/29/2023] [Accepted: 04/14/2023] [Indexed: 04/26/2023]
Abstract
Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.
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Affiliation(s)
- Michael Lassi
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, viale Rinaldo Piaggio 34, 56025 Pisa, Italy
| | - Carlo Fabbiani
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy
| | - Salvatore Mazzeo
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy; Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy
| | - Alberto Arturo Vergani
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, viale Rinaldo Piaggio 34, 56025 Pisa, Italy
| | - Giulia Giacomucci
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Valentina Moschini
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Carmen Morinelli
- Dipartimento Neuromuscolo-scheletrico e degli organi di senso, Careggi University Hospital, 50134 Florence, Italy
| | - Filippo Emiliani
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Maenia Scarpino
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy
| | - Silvia Bagnoli
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Assunta Ingannato
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Benedetta Nacmias
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy; Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Sonia Padiglioni
- Regional Referral Centre for Relational Criticalities - Tuscany Region, 50139 Florence, Italy
| | - Silvestro Micera
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, viale Rinaldo Piaggio 34, 56025 Pisa, Italy; Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Sandro Sorbi
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy; Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Antonello Grippo
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy
| | - Valentina Bessi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, viale Rinaldo Piaggio 34, 56025 Pisa, Italy.
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Piao S, Chen K, Wang N, Bao Y, Liu X, Hu B, Lu Y, Yang L, Geng D, Li Y. Modular Level Alterations Of Structural-Functional Connectivity Coupling in Mild Cognitive Impairment Patients and Interactions with Age Effect. J Alzheimers Dis 2023; 92:1439-1450. [PMID: 36911934 DOI: 10.3233/jad-220837] [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: 03/12/2023]
Abstract
BACKGROUND Structural-functional connectivity (SC- FC) coupling is related to various cognitive functions and more sensitive for the detection of subtle brain alterations. OBJECTIVE To investigate whether decoupling of SC-FC was detected in mild cognitive impairment (MCI) patients on a modular level, the interaction effect of aging and disease, and its relationship with network efficiency. METHODS 73 patients with MCI and 65 healthy controls were enrolled who underwent diffusion tensor imaging and resting-state functional MRI to generate structural and functional networks. Five modules were defined based on automated anatomical labeling 90 atlas, including default mode network (DMN), frontoparietal attention network (FPN), sensorimotor network (SMN), subcortical network (SCN), and visual network (VIS). Intra-module and inter-module SC-FC coupling were compared between two groups. The interaction effect of aging and group on modular SC-FC coupling was further analyzed by two-way ANOVA. The correlation between the coupling and network efficiency was finally calculated. RESULTS In MCI patients, aberrant intra-module coupling was noted in SMN, and altered inter-module coupling was found in the other four modules. Intra-module coupling exhibited significant age-by-group effects in DMN and SMN, and inter-module coupling showed significant age-by-group effects in DMN and FPN. In MCI patients, both positive or negative correlations between coupling and network efficiency were found in DMN, FPN, SCN, and VIS. CONCLUSION SC-FC coupling could reflect the association of SC and FC, especially in modular levels. In MCI, SC-FC coupling could be affected by the interaction effect of aging and disease, which may shed light on advancing the pathophysiological mechanisms of MCI.
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Affiliation(s)
- Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Keliang Chen
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Na Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yucheng Lu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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Chen Q, Chen F, Zhu Y, Long C, Lu J, Zhang X, Nedelska Z, Hort J, Chen J, Ma G, Zhang B. Reconfiguration of brain network dynamics underlying spatial deficits in subjective cognitive decline. Neurobiol Aging 2023; 127:82-93. [PMID: 37116409 DOI: 10.1016/j.neurobiolaging.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/12/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023]
Abstract
Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.
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Lee D, Park JY, Kim WJ. Altered functional connectivity of the default mode and dorsal attention network in subjective cognitive decline. J Psychiatr Res 2023; 159:165-171. [PMID: 36738647 DOI: 10.1016/j.jpsychires.2023.01.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/23/2022] [Accepted: 01/26/2023] [Indexed: 01/31/2023]
Abstract
Subjective cognitive decline (SCD) is a clinical condition in which performance on standardized cognitive tests does not indicate impairment like mild cognitive impairment (MCI), but self-experienced cognitive capacity persistently declines. We aimed to explore the functional connectivity (FC) characteristics of SCD subjects compared to healthy controls and MCI patients. Resting-state functional MRI was performed on 152 elderly subjects: 65 normal controls, 62 SCD subjects, and 25 MCI patients. A seed-based FC analysis was performed to compare groups. The major brain regions of the default mode network (DMN) and dorsal attention network (DAN), large-scale brain networks disrupted in patients with dementia, were selected as seed regions. As a result, the SCD group showed stronger FC than the MCI group between DMN seeds and the supramarginal gyrus. Both the SCD and MCI groups showed stronger FC between the left lateral parietal cortex and right dorsolateral prefrontal cortex. In the FC analysis centred on DAN seeds, both the SCD and MCI groups showed weaker FC of the right posterior intraparietal sulcus in the left anterior cingulate cortex and the left insula, compared to those in the control group. Within the SCD group, hyperconnectivity between the right lateral parietal cortex and left supramarginal gyrus was significantly correlated with better performance on the Controlled Oral Word Association Test. In conclusion, the SCD group showed several DMN- and DAN-related FC alterations, similar to the MCI group, but with distinct hyperconnectivity between DMN seeds and the supramarginal gyrus. In particular, SCD has DMN-related FC patterns distinct from those of MCI that are associated with verbal fluency retention.
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Affiliation(s)
- Deokjong Lee
- Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea; Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Young Park
- Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea; Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea; Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea.
| | - Woo Jung Kim
- Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea; Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea.
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50
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D’Alonzo ZJ, Lam V, Takechi R, Nesbit M, Vaccarezza M, Mamo JCL. Peripheral metabolism of lipoprotein-amyloid beta as a risk factor for Alzheimer's disease: potential interactive effects of APOE genotype with dietary fats. GENES & NUTRITION 2023; 18:2. [PMID: 36841786 PMCID: PMC9960179 DOI: 10.1186/s12263-023-00722-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 02/07/2023] [Indexed: 02/27/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder pathologically characterized by brain parenchymal abundance of amyloid-beta (Aβ) and the accumulation of lipofuscin material that is rich in neutral lipids. However, the mechanisms for aetiology of AD are presently not established. There is increasing evidence that metabolism of lipoprotein-Aβ in blood is associated with AD risk, via a microvascular axis that features breakdown of the blood-brain barrier, extravasation of lipoprotein-Aβ to brain parenchyme and thereafter heightened inflammation. A peripheral lipoprotein-Aβ/capillary axis for AD reconciles alternate hypotheses for a vascular, or amyloid origin of disease, with amyloidosis being probably consequential. Dietary fats may markedly influence the plasma abundance of lipoprotein-Aβ and by extension AD risk. Similarly, apolipoprotein E (Apo E) serves as the primary ligand by which lipoproteins are cleared from plasma via high-affinity receptors, for binding to extracellular matrices and thereafter for uptake of lipoprotein-Aβ via resident inflammatory cells. The epsilon APOE ε4 isoform, a major risk factor for AD, is associated with delayed catabolism of lipoproteins and by extension may increase AD risk due to increased exposure to circulating lipoprotein-Aβ and microvascular corruption.
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Affiliation(s)
- Zachary J. D’Alonzo
- grid.1032.00000 0004 0375 4078Faculty of Health Sciences, Curtin Medical School, Curtin University, Perth, Western Australia Australia ,grid.1032.00000 0004 0375 4078Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Western Australia Australia
| | - Virginie Lam
- grid.1032.00000 0004 0375 4078Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Western Australia Australia ,grid.1032.00000 0004 0375 4078Faculty of Health Sciences, School of Population Health, Curtin University, Perth, Western Australia Australia
| | - Ryu Takechi
- grid.1032.00000 0004 0375 4078Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Western Australia Australia ,grid.1032.00000 0004 0375 4078Faculty of Health Sciences, School of Population Health, Curtin University, Perth, Western Australia Australia
| | - Michael Nesbit
- grid.1032.00000 0004 0375 4078Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Western Australia Australia
| | - Mauro Vaccarezza
- grid.1032.00000 0004 0375 4078Faculty of Health Sciences, Curtin Medical School, Curtin University, Perth, Western Australia Australia ,grid.1032.00000 0004 0375 4078Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Western Australia Australia
| | - John C. L. Mamo
- grid.1032.00000 0004 0375 4078Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Western Australia Australia ,grid.1032.00000 0004 0375 4078Faculty of Health Sciences, School of Population Health, Curtin University, Perth, Western Australia Australia
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