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Wan K, Yin W, Tang Y, Zhu W, Wang Z, Zhou X, Zhang W, Zhang C, Yu X, Zhao W, Li C, Zhu X, Sun Z. Brain Gray Matter Volume Mediated the Correlation Between Plasma P-Tau and Cognitive Function of Early Alzheimer's Disease in China: A Cross-Sectional Observational Study. J Alzheimers Dis 2023; 92:81-93. [PMID: 36710682 DOI: 10.3233/jad-221100] [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: 01/28/2023]
Abstract
BACKGROUND The primary manifestations of Alzheimer's disease (AD) include cognitive decline and brain gray matter volume (GMV) atrophy. Recent studies have found that plasma phosphorylated-tau (p-tau) concentrations perform better in diagnosing, differentiating, and monitoring the progression of AD. However, the correlation between plasma p-tau, GMV, and cognition remains unclear. OBJECTIVE To investigate whether GMV plays a mediating role in the association between plasma p-tau concentrations and cognition. METHODS In total, 99 participants (47 patients with AD and 52 cognitively unimpaired [CU] individuals) were included. All participants underwent neuropsychological assessments, laboratory examinations, and magnetic resonance imaging scans. Plasma p-tau217 and p-tau181 concentrations were measured using an enzyme-linked immunosorbent assay kit. Voxel-based morphometry was performed to assess participants' brain GMV. Partial correlation and mediation analyses were conducted in AD group. RESULTS Plasma p-tau concentrations were significantly higher in the AD group than in the CU group. Patients with AD had significant brain GMV atrophy in the right hippocampus, bilateral middle temporal gyrus, and right inferior temporal gyrus. In the AD group, there were significant correlations between plasma p-tau217 concentrations, GMV, and Mini-Mental State Examination (MMSE) scores. Brain GMV of the right hippocampus mediated the association between plasma p-tau217 concentrations and MMSE scores. A significant correlation between plasma p-tau181 and MMSE scores was not identified. CONCLUSION The findings indicate that p-tau217 is a promising biomarker for central processes affecting brain GMV and cognitive function. This may provide potential targets for future intervention and treatment of tau-targeting therapies in the early stages of AD.
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Affiliation(s)
- Ke Wan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Wenwen Yin
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Yating Tang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Wenhao Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Zhiqiang Wang
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Menzies Institute for Medical Research, University of Tasmania, Private Bag 23, Hobart, Tasmania, Australia
| | - Xia Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Wei Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xianfeng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chenchen Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Xiaoqun Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Zhongwu Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
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152
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Dong H, Guo L, Yang H, Zhu W, Liu F, Xie Y, Zhang Y, Xue K, Li Q, Liang M, Zhang N, Qin W. Association between gray matter atrophy, cerebral hypoperfusion, and cognitive impairment in Alzheimer's disease. Front Aging Neurosci 2023; 15:1129051. [PMID: 37091519 PMCID: PMC10117777 DOI: 10.3389/fnagi.2023.1129051] [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: 12/21/2022] [Accepted: 03/15/2023] [Indexed: 04/25/2023] Open
Abstract
Background Alzheimer's disease (AD) is one of the most severe neurodegenerative diseases leading to dementia in the elderly. Cerebral atrophy and hypoperfusion are two important pathophysiological characteristics. However, it is still unknown about the area-specific causal pathways between regional gray matter atrophy, cerebral hypoperfusion, and cognitive impairment in AD patients. Method Forty-two qualified AD patients and 49 healthy controls (HC) were recruited in this study. First, we explored voxel-wise inter-group differences in gray matter volume (GMV) and arterial spin labeling (ASL) -derived cerebral blood flow (CBF). Then we explored the voxel-wise associations between GMV and Mini-Mental State Examination (MMSE) score, GMV and CBF, and CBF and MMSE to identify brain targets contributing to cognitive impairment in AD patients. Finally, a mediation analysis was applied to test the causal pathways among atrophied GMV, hypoperfusion, and cognitive impairment in AD. Results Voxel-wise permutation test identified that the left middle temporal gyrus (MTG) had both decreased GMV and CBF in the AD. Moreover, the GMV of this region was positively correlated with MMSE and its CBF, and CBF of this region was also positively correlated with MMSE in AD (p < 0.05, corrected). Finally, mediation analysis revealed that gray matter atrophy of left MTG drives cognitive impairment of AD via the mediation of CBF (proportion of mediation = 55.82%, β = 0.242, 95% confidence interval by bias-corrected and accelerated bootstrap: 0.082 to 0.530). Conclusion Our findings indicated suggested that left MTG is an important hub linking gray matter atrophy, hypoperfusion, and cognitive impairment for AD, and might be a potential treatment target for AD.
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Affiliation(s)
- Haoyang Dong
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hailei Yang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Fang Liu
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Qiang Li
- Technical College for the Deaf, Tianjin University of Technology, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Nan Zhang
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
- *Correspondence: Nan Zhang,
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Wen Qin,
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Central Stimulatory Effect of Kynurenic Acid on BDNF-TrkB Signaling and BER Enzymatic Activity in the Hippocampal CA1 Field in Sheep. Int J Mol Sci 2022; 24:ijms24010136. [PMID: 36613581 PMCID: PMC9820639 DOI: 10.3390/ijms24010136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/12/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Deficiency of neurotrophic factors and oxidative DNA damage are common causes of many neurodegenerative diseases. Recently, the importance of kynurenic acid (KYNA), an active metabolite of tryptophan, has increased as a neuroprotective molecule in the brain. Therefore, the present study tested the hypothesis that centrally acting KYNA would positively affect: (1) brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signaling and (2) selected base excision repair (BER) pathway enzymes activities in the hippocampal CA1 field in sheep. Both lower (20 μg in total) and higher (100 μg in total) doses of KYNA infused into the third brain ventricle differentially increased the abundance of BDNF and TrkB mRNA in the CA1 field; additionally, the higher dose increased BDNF tissue concentration. The lower dose of KYNA increased mRNA expression for 8-oxoguanine glycosylase (OGG1), N-methylpurine DNA glycosylase (MPG), and thymine DNA glycosylase and stimulated the repair of 1,N6-ethenodeoxyadenosine and 3,N4-ethenodeoxy-cytosine as determined by the excision efficiency of lesioned nucleobases. The higher dose increased the abundance of OGG1 and MPG transcripts, however, its stimulatory effect on repair activity was less pronounced in all cases compared to the lower dose. The increased level of AP-endonuclease mRNA expression was dose-dependent. In conclusion, the potential neurotrophic and neuroprotective effects of KYNA in brain cells may involve stimulation of the BDNF-TrkB and BER pathways.
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154
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Kwak K, Stanford W, Dayan E. Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion. Hum Brain Mapp 2022; 43:5509-5519. [PMID: 35904092 PMCID: PMC9704798 DOI: 10.1002/hbm.26026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/02/2022] [Accepted: 07/08/2022] [Indexed: 01/15/2023] Open
Abstract
Progressive brain atrophy is a key neuropathological hallmark of Alzheimer's disease (AD) dementia. However, atrophy patterns along the progression of AD dementia are diffuse and variable and are often missed by univariate methods. Consequently, identifying the major regional atrophy patterns underlying AD dementia progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy patterns are predictive of AD dementia progression, while holding all other atrophy changes constant using a total sample of 334 subjects. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD dementia versus those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding different regions of interest (ROIs) from the model's testing set's input. We also validated this approach by occluding ROIs based on Braak's staging scheme. We found that the hippocampus, fusiform, and inferior temporal gyri were the strongest predictors of AD dementia progression, in agreement with established staging models. We also found that occlusion of limbic ROIs defined according to Braak stage III had the largest impact on the performance of the model. Our predictive model reveals the major regional patterns of atrophy predictive of AD dementia progression. These results highlight the potential for early diagnosis and stratification of individuals with prodromal AD dementia based on patterns of cortical atrophy, prior to interventional clinical trials.
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Affiliation(s)
- Kichang Kwak
- Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - William Stanford
- Neuroscience Curriculum, Biological and Biomedical Sciences ProgramUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Eran Dayan
- Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Neuroscience Curriculum, Biological and Biomedical Sciences ProgramUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Department of RadiologyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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155
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Liu X, Tyler LK, Rowe JB, Tsvetanov KA. Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects. Hum Brain Mapp 2022; 43:5490-5508. [PMID: 35855641 PMCID: PMC9704789 DOI: 10.1002/hbm.26025] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 01/15/2023] Open
Abstract
Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.
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Affiliation(s)
- Xulin Liu
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Lorraine K. Tyler
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Cambridge Centre for Ageing and Neuroscience (Cam‐CAN), MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
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156
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Manippa V, Palmisano A, Filardi M, Vilella D, Nitsche MA, Rivolta D, Logroscino G. An update on the use of gamma (multi)sensory stimulation for Alzheimer's disease treatment. Front Aging Neurosci 2022; 14:1095081. [PMID: 36589536 PMCID: PMC9797689 DOI: 10.3389/fnagi.2022.1095081] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by reduced fast brain oscillations in the gamma band (γ, > 30 Hz). Several animal studies show that inducing gamma oscillations through (multi)sensory stimulation at 40 Hz has the potential to impact AD-related cognitive decline and neuropathological processes, including amyloid plaques deposition, neurofibrillary tangles formation, and neuronal and synaptic loss. Therefore Gamma Entrainment Using Sensory stimulation (GENUS) is among the most promising approaches for AD patients' treatment. This review summarizes the evidence on GENUS effectiveness, from animal models to AD patients. Despite the application on human is in its infancy, the available findings suggest its feasibility for the treatment of AD. We discuss such results in light of parameter improvement and possible underlying mechanisms. We finally emphasize the need for further research for its development as a disease-modifying non-pharmacological intervention.
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Affiliation(s)
- Valerio Manippa
- Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy,Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany,*Correspondence: Valerio Manippa,
| | - Annalisa Palmisano
- Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy
| | - Marco Filardi
- Department of Basic Medicine, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy,Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | - Davide Vilella
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | - Michael A. Nitsche
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany,Bielefeld University, University Hospital OWL, Protestant Hospital of Bethel Foundation, University Clinic of Psychiatry and Psychotherapy and University Clinic of Child and Adolescent Psychiatry and Psychotherapy, Bielefeld, Germany
| | - Davide Rivolta
- Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy
| | - Giancarlo Logroscino
- Department of Basic Medicine, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy,Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
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157
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Chow TE, Veziris CR, La Joie R, Lee AJ, Brown JA, Yokoyama JS, Rankin KP, Kramer JH, Miller BL, Rabinovici GD, Seeley WW, Sturm VE. Increasing empathic concern relates to salience network hyperconnectivity in cognitively healthy older adults with elevated amyloid-β burden. Neuroimage Clin 2022; 37:103282. [PMID: 36525744 PMCID: PMC9758499 DOI: 10.1016/j.nicl.2022.103282] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/20/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022]
Abstract
Enhanced emotional empathy, the ability to share others' affective experiences, can be a feature of Alzheimer's disease (AD), but whether emotional empathy increases in the preclinical phase of the disease is unknown. We measured emotional empathy over time (range = 0 - 7.3 years, mean = 2.4 years) in 86 older adults during a period in which they were cognitively healthy, functionally normal, and free of dementia symptoms. For each participant, we computed longitudinal trajectories for empathic concern (i.e., an other-oriented form of emotional empathy that promotes prosocial actions) and emotional contagion (i.e., a self-focused form of emotional empathy often accompanied by feelings of distress) from informant ratings of participants' empathy on the Interpersonal Reactivity Index. Amyloid-β (Aβ) positron emission tomography (PET) scans were used to classify participants as either Aβ positive (Aβ+, n = 23) or negative (Aβ-, n = 63) based on Aβ-PET cortical binding. Participants also underwent structural and task-free functional magnetic resonance imaging approximately two years on average after their last empathy assessment, at which time most participants remained cognitively healthy. Results indicated that empathic concern, but not emotional contagion, increased more over time in Aβ+ participants than in Aβ- participants despite no initial group difference at the first measurement. Higher connectivity between certain salience network node-pairs (i.e., pregenual anterior cingulate cortex and periaqueductal gray) predicted longitudinal increases in empathic concern in the Aβ+ group but not in the Aβ- group. The Aβ+ participants also had higher overall salience network connectivity than Aβ- participants despite no differences in gray matter volume. These results suggest gains in empathic concern may be a very early feature of AD pathophysiology that relates to hyperconnectivity in the salience network, a system that supports emotion generation and interoception. A better understanding of emotional empathy trajectories in the early stages of AD pathophysiology will broaden the lens on preclinical AD changes and help clinicians to identify older adults who should be screened for AD biomarkers.
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Affiliation(s)
- Tiffany E Chow
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
| | - Christina R Veziris
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
| | - Alex J Lee
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
| | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94158, USA.
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94158, USA.
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
| | - Virginia E Sturm
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94158, USA.
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158
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Multi-modality MRI for Alzheimer's disease detection using deep learning. Phys Eng Sci Med 2022; 45:1043-1053. [PMID: 36063346 DOI: 10.1007/s13246-022-01165-9] [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: 11/06/2021] [Accepted: 07/20/2022] [Indexed: 12/15/2022]
Abstract
Diffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human body in vivo and non-invasively. It identifies the microstructure of white matter (WM) connectivity by estimating the movement of water molecules at each voxel. This makes possible the identification of the damage to WM integrity caused by Alzheimer's disease (AD) at its early stage, called mild cognitive impairment (MCI). Furthermore, the brain's gray matter (GM) atrophy characterizes the main structural changes in AD, which can be sensitively detected by structural MRI (sMRI) modality. In this research, we aimed to classify the Alzheimer's diseases stages by developing a novel multi-modality MRI (DTI and sMRI) fusion strategy to detect WM alterations and GM atrophy in AD patients. The latter is based on a 2-dimensional deep convolutional neural network (CNN) features extractor and a support vector machine (SVM) classifier. The fusion framework consists of merging features extracted from DTI scalar metrics [(fractional anisotropy (FA) and mean diffusivity (MD)], and GM using 2D-CNN and feeding them to SVM to classify AD versus cognitively normal (CN), AD versus MCI, and MCI versus CN. Our novel multimodal AD method demonstrates a superior performance with an accuracy of 99.79%, 99.6%, and 97.00% for AD/CN, AD/MCI, and MCI/CN respectively.
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159
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Astillero-Lopez V, Gonzalez-Rodriguez M, Villar-Conde S, Flores-Cuadrado A, Martinez-Marcos A, Ubeda-Banon I, Saiz-Sanchez D. Neurodegeneration and astrogliosis in the entorhinal cortex in Alzheimer's disease: Stereological layer-specific assessment and proteomic analysis. Alzheimers Dement 2022; 18:2468-2480. [PMID: 35142030 DOI: 10.1002/alz.12580] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/30/2021] [Accepted: 12/12/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The entorhinal cortex is among the earliest areas involved in Alzheimer's disease. Volume reduction and neural loss in this area have been widely reported. Human entorhinal cortex atrophy is, in part, due to neural loss, but microglial and/or astroglial involvement in the different layers remains unclear. Additionally, -omic approaches in the human entorhinal cortex are scarce. METHODS Herein, stereological layer-specific and proteomic analyses were carried out in the human brain. RESULTS Neurodegeneration, microglial reduction, and astrogliosis have been demonstrated, and proteomic data have revealed relationships with up- (S100A6, PPP1R1B, BAG3, and PRDX6) and downregulated (GSK3B, SYN1, DLG4, and RAB3A) proteins. Namely, clusters of these proteins were related to synaptic, neuroinflammatory, and oxidative stress processes. DISCUSSION Differential layer involvement among neural and glial populations determined by proteinopathies and identified proteins related to neurodegeneration and astrogliosis could explain how the cortical circuitry facilitates pathological spreading within the medial temporal lobe.
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Affiliation(s)
- Veronica Astillero-Lopez
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Melania Gonzalez-Rodriguez
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Sandra Villar-Conde
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Alicia Flores-Cuadrado
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Alino Martinez-Marcos
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Isabel Ubeda-Banon
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Daniel Saiz-Sanchez
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
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160
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Yang Y, Chen Y, Sang F, Zhao S, Wang J, Li X, Chen C, Chen K, Zhang Z. Successful or pathological cognitive aging? Converging into a "frontal preservation, temporal impairment (FPTI)" hypothesis. Sci Bull (Beijing) 2022; 67:2285-2290. [PMID: 36546218 DOI: 10.1016/j.scib.2022.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/12/2022] [Accepted: 10/28/2022] [Indexed: 11/13/2022]
Affiliation(s)
- Yiru Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing 100875, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing 100875, China.
| | - Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing 100875, China
| | - Shaokun Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing 100875, China
| | - Jun Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing 100875, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing 100875, China
| | - Chuansheng Chen
- Department of Psychology and Social Behavior, University of California, Irvine 92697, USA
| | - Kewei Chen
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing 100875, China; Banner Alzheimer's Institute, Phoenix 85006, USA
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing 100875, China.
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Regmi S, Liu DD, Shen M, Kevadiya BD, Ganguly A, Primavera R, Chetty S, Yarani R, Thakor AS. Mesenchymal stromal cells for the treatment of Alzheimer’s disease: Strategies and limitations. Front Mol Neurosci 2022; 15:1011225. [PMID: 36277497 PMCID: PMC9584646 DOI: 10.3389/fnmol.2022.1011225] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 09/20/2022] [Indexed: 11/27/2022] Open
Abstract
Alzheimer’s disease (AD) is a major cause of age-related dementia and is characterized by progressive brain damage that gradually destroys memory and the ability to learn, which ultimately leads to the decline of a patient’s ability to perform daily activities. Although some of the pharmacological treatments of AD are available for symptomatic relief, they are not able to limit the progression of AD and have several side effects. Mesenchymal stem/stromal cells (MSCs) could be a potential therapeutic option for treating AD due to their immunomodulatory, anti-inflammatory, regenerative, antioxidant, anti-apoptotic, and neuroprotective effects. MSCs not only secret neuroprotective and anti-inflammatory factors to promote the survival of neurons, but they also transfer functional mitochondria and miRNAs to boost their bioenergetic profile as well as improve microglial clearance of accumulated protein aggregates. This review focuses on different clinical and preclinical studies using MSC as a therapy for treating AD, their outcomes, limitations and the strategies to potentiate their clinical translation.
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162
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Chen PH, Lin SI, Liao YY, Hsu WL, Cheng FY. Associations between blood-based biomarkers of Alzheimer's disease with cognition in motoric cognitive risk syndrome: A pilot study using plasma Aβ42 and total tau. Front Aging Neurosci 2022; 14:981632. [PMID: 36268195 PMCID: PMC9577229 DOI: 10.3389/fnagi.2022.981632] [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: 06/29/2022] [Accepted: 09/20/2022] [Indexed: 01/28/2023] Open
Abstract
Background Motoric cognitive risk (MCR) syndrome is a conceptual construct that combines slow gait speed with subjective cognitive complaints and has been shown to be associated with an increased risk of developing dementia. However, the relationships between the pathology of Alzheimer's disease (AD) and MCR syndrome remain uncertain. Therefore, the purpose of this study was to determine the levels of plasma AD biomarkers (Aβ42 and total tau) and their relationships with cognition in individuals with MCR. Materials and methods This was a cross-sectional pilot study that enrolled 25 individuals with normal cognition (NC), 27 with MCR, and 16 with AD. Plasma Aβ42 and total tau (t-tau) levels were measured using immunomagnetic reduction (IMR) assays. A comprehensive neuropsychological assessment was also performed. Results The levels of plasma t-tau proteins did not differ significantly between the MCR and AD groups, but that of plasma t-tau was significantly increased in the MCR and AD groups, compared to the NC group. Visuospatial performance was significantly lower in the MCR group than in the NC group. The levels of plasma t-tau correlated significantly with the Montreal Cognitive Assessment (MoCA) and Boston naming test scores in the MCR group. Conclusion In this pilot study, we found significantly increased plasma t-tau proteins in the MCR and AD groups, compared with the NC group. The plasma t-tau levels were also significantly correlated with the cognitive function of older adults with MCR. These results implied that MCR and AD may share similar pathology. However, these findings need further confirmation in longitudinal studies.
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Affiliation(s)
- Pei-Hao Chen
- Department of Neurology, MacKay Memorial Hospital, Taipei, Taiwan,Department of Medicine, MacKay Medical College, New Taipei City, Taiwan,Graduate Institute of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - Sang-I Lin
- Institute of Long-Term Care, MacKay Medical College, New Taipei City, Taiwan
| | - Ying-Yi Liao
- Department of Gerontological Health Care, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Wei-Ling Hsu
- Institute of Long-Term Care, MacKay Medical College, New Taipei City, Taiwan,Center of Dementia Care, MacKay Memorial Hospital, New Taipei City, Taiwan
| | - Fang-Yu Cheng
- Institute of Long-Term Care, MacKay Medical College, New Taipei City, Taiwan,*Correspondence: Fang-Yu Cheng,
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163
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Huang X, Wang YJ, Xiang Y. Bidirectional communication between brain and visceral white adipose tissue: Its potential impact on Alzheimer's disease. EBioMedicine 2022; 84:104263. [PMID: 36122553 PMCID: PMC9490488 DOI: 10.1016/j.ebiom.2022.104263] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 08/21/2022] [Accepted: 08/24/2022] [Indexed: 11/20/2022] Open
Abstract
A variety of axes between brain and abdominal organs have been reported, but the interaction between brain and visceral white adipose tissue (vWAT) remains unclear. In this review, we summarized human studies on the association between brain and vWAT, and generalized their interaction and the underlying mechanisms according to animal and cell experiments. On that basis, we come up with the concept of the brain-vWAT axis (BVA). Furthermore, we analyzed the potential mechanisms of involvement of BVA in the pathogenesis of Alzheimer's disease (AD), including vWAT-derived fatty acids, immunological properties of vWAT, vWAT-derived retinoic acid and vWAT-regulated insulin resistance. The proposal of BVA may expand our understanding to some extent of how the vWAT impacts on brain health and diseases, and provide a novel approach to study the pathogenesis and treatment strategies of neurodegenerative disorders.
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164
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Altendorfer B, Unger MS, Poupardin R, Hoog A, Asslaber D, Gratz IK, Mrowetz H, Benedetti A, de Sousa DMB, Greil R, Egle A, Gate D, Wyss-Coray T, Aigner L. Transcriptomic Profiling Identifies CD8 + T Cells in the Brain of Aged and Alzheimer's Disease Transgenic Mice as Tissue-Resident Memory T Cells. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 209:1272-1285. [PMID: 36165202 PMCID: PMC9515311 DOI: 10.4049/jimmunol.2100737] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 07/20/2022] [Indexed: 12/13/2022]
Abstract
Peripheral immune cell infiltration into the brain is a prominent feature in aging and various neurodegenerative diseases such as Alzheimer's disease (AD). As AD progresses, CD8+ T cells infiltrate into the brain parenchyma, where they tightly associate with neurons and microglia. The functional properties of CD8+ T cells in the brain are largely unknown. To gain further insights into the putative functions of CD8+ T cells in the brain, we explored and compared the transcriptomic profile of CD8+ T cells isolated from the brain and blood of transgenic AD (APPswe/PSEN1dE9, line 85 [APP-PS1]) and age-matched wild-type (WT) mice. Brain CD8+ T cells of APP-PS1 and WT animals had similar transcriptomic profiles and substantially differed from blood circulating CD8+ T cells. The gene signature of brain CD8+ T cells identified them as tissue-resident memory (Trm) T cells. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis on the significantly upregulated genes revealed overrepresentation of biological processes involved in IFN-β signaling and the response to viral infections. Furthermore, brain CD8+ T cells of APP-PS1 and aged WT mice showed similar differentially regulated genes as brain Trm CD8+ T cells in mouse models with acute virus infection, chronic parasite infection, and tumor growth. In conclusion, our profiling of brain CD8+ T cells suggests that in AD, these cells exhibit similar adaptive immune responses as in other inflammatory diseases of the CNS, potentially opening the door for immunotherapy in AD.
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Affiliation(s)
- Barbara Altendorfer
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Michael Stefan Unger
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Rodolphe Poupardin
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
- Experimental and Clinical Cell Therapy Institute, Paracelsus Medical University, Salzburg, Austria
| | - Anna Hoog
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
- Experimental and Clinical Cell Therapy Institute, Paracelsus Medical University, Salzburg, Austria
| | - Daniela Asslaber
- IIIrd Medical Department with Hematology and Medical Oncology, Oncologic Center, Paracelsus Medical University, Salzburg, Austria
- Salzburg Cancer Research Institute with Laboratory of Immunological and Molecular Cancer Research and Center for Clinical Cancer and Immunology Trials, Salzburg, Austria
- Cancer Cluster Salzburg, Salzburg, Austria
| | - Iris Karina Gratz
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Heike Mrowetz
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Ariane Benedetti
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
- Institute of Experimental Neuroregeneration, Paracelsus Medical University, Salzburg, Austria
| | - Diana Marisa Bessa de Sousa
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Richard Greil
- IIIrd Medical Department with Hematology and Medical Oncology, Oncologic Center, Paracelsus Medical University, Salzburg, Austria
- Salzburg Cancer Research Institute with Laboratory of Immunological and Molecular Cancer Research and Center for Clinical Cancer and Immunology Trials, Salzburg, Austria
- Cancer Cluster Salzburg, Salzburg, Austria
| | - Alexander Egle
- IIIrd Medical Department with Hematology and Medical Oncology, Oncologic Center, Paracelsus Medical University, Salzburg, Austria
- Salzburg Cancer Research Institute with Laboratory of Immunological and Molecular Cancer Research and Center for Clinical Cancer and Immunology Trials, Salzburg, Austria
- Cancer Cluster Salzburg, Salzburg, Austria
| | - David Gate
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
- Veterans Administration Palo Alto Healthcare System, Palo Alto, CA; and
| | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
- Veterans Administration Palo Alto Healthcare System, Palo Alto, CA; and
| | - Ludwig Aigner
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria;
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
- Austrian Cluster for Tissue Regeneration, Vienna, Austria
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165
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Huang SY, Li YZ, Zhang YR, Huang YY, Wu BS, Zhang W, Deng YT, Chen SD, He XY, Chen SF, Dong Q, Zhang C, Chen RJ, Suckling J, Rolls ET, Feng JF, Cheng W, Yu JT. Sleep, physical activity, sedentary behavior, and risk of incident dementia: a prospective cohort study of 431,924 UK Biobank participants. Mol Psychiatry 2022; 27:4343-4354. [PMID: 35701596 DOI: 10.1038/s41380-022-01655-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/17/2022] [Accepted: 05/31/2022] [Indexed: 02/07/2023]
Abstract
Although sleep, physical activity and sedentary behavior have been found to be associated with dementia risk, findings are inconsistent and their joint relationship remains unclear. This study aimed to investigate independent and joint associations of these three modifiable behaviors with dementia risks. A total of 431,924 participants (median follow-up 9.0 years) without dementia from UK Biobank were included. Multiple Cox regressions were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Models fitted with restricted cubic spline were conducted to test for linear and nonlinear shapes of each association. Sleep duration, leisure-time physical activity (LTPA), and screen-based sedentary behavior individually associated with dementia risks in different non-linear patterns. Sleep duration associated with dementia in a U-shape with a nadir at 7 h/day. LTPA revealed a curvilinear relationship with dementia in diminishing tendency, while sedentary behavior revealed a J-shaped relationship. The dementia risk was 17% lower in the high LTPA group (HR[95%CI]: 0.83[0.76-0.91]) and 22% higher in the high sedentary behavior group (1.22[1.10-1.35]) compared to the corresponding low-level group, respectively. A combination of seven-hour/day sleep, moderate-to-high LTPA, and low-to-moderate sedentary behavior showed the lowest dementia risk (0.59[0.50-0.69]) compared to the referent group (longer or shorter sleep/low LTPA/high sedentary behavior). Notably, each behavior was non-linearly associated with brain structures in a pattern similar to its association with dementia, suggesting they may affect dementia risk by affecting brain structures. Our findings highlight the potential to change these three daily behaviors individually and simultaneously to reduce the risk of dementia.
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Affiliation(s)
- Shu-Yi Huang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiao-Yu He
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shu-Fen Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Can Zhang
- Genetics and Aging Research Unit, McCance Center for Brain Health, Mass General Institute for Neurodegenerative Diseases (MIND), Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Ren-Jie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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166
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St-Pierre MK, Carrier M, González Ibáñez F, Šimončičová E, Wallman MJ, Vallières L, Parent M, Tremblay MÈ. Ultrastructural characterization of dark microglia during aging in a mouse model of Alzheimer's disease pathology and in human post-mortem brain samples. J Neuroinflammation 2022; 19:235. [PMID: 36167544 PMCID: PMC9513936 DOI: 10.1186/s12974-022-02595-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 09/12/2022] [Indexed: 11/10/2022] Open
Abstract
A diverse heterogeneity of microglial cells was previously described in Alzheimer's disease (AD) pathology, including dark microglia, a state characterized by ultrastructural markers of cellular stress. To provide novel insights into the roles of dark microglia during aging in the context of AD pathology, we performed a quantitative density and ultrastructural analysis of these cells using high-throughput scanning electron microscopy in the ventral hippocampus CA1 stratum lacunosum-moleculare of 20-month-old APP-PS1 vs C57BL/6J male mice. The density of dark microglia was significantly higher in APP-PS1 vs C57BL/6J mice, with these cells accounting for nearly half of all microglia observed near amyloid-beta (Aβ) plaques. This dark microglial state interacted more with dystrophic neurites compared to other APP-PS1 microglia and possessed glycogen granules, associated with a metabolic shift toward glycolysis, which provides the first ultrastructural evidence of their presence in microglia. Dark microglia were further observed in aging human post-mortem brain samples showing similar ultrastructural features as in mouse. Overall, our results provide a quantitative ultrastructural characterization of a microglial state associated with cellular stress (i.e., dark microglia) that is primarily restricted near Aβ plaques and dystrophic neurites. The presence of this microglial state in the aging human post-mortem brain is further revealed.
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Affiliation(s)
- Marie-Kim St-Pierre
- Axe Neurosciences, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, Canada.,Department of Molecular Medicine, Université Laval, Québec City, QC, Canada.,Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Micaël Carrier
- Axe Neurosciences, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, Canada.,Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Fernando González Ibáñez
- Department of Molecular Medicine, Université Laval, Québec City, QC, Canada.,Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Eva Šimončičová
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada.,Neuroscience Graduate Program, University of Victoria, Victoria, BC, Canada
| | - Marie-Josée Wallman
- Département de Psychiatrie et de Neurosciences, Faculté de Médecine, Université Laval, Quebec, QC, Canada.,CERVO Brain Research Center, Quebec, QC, Canada
| | - Luc Vallières
- Axe Neurosciences, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, Canada.,Department of Molecular Medicine, Université Laval, Québec City, QC, Canada
| | - Martin Parent
- Département de Psychiatrie et de Neurosciences, Faculté de Médecine, Université Laval, Quebec, QC, Canada.,CERVO Brain Research Center, Quebec, QC, Canada
| | - Marie-Ève Tremblay
- Axe Neurosciences, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, Canada. .,Department of Molecular Medicine, Université Laval, Québec City, QC, Canada. .,Division of Medical Sciences, University of Victoria, Victoria, BC, Canada. .,Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada. .,Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada. .,Centre for Advanced Materials and Related Technology (CAMTEC), University of Victoria, Victoria, BC, Canada.
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167
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Mumtaz I, Ayaz MO, Khan MS, Manzoor U, Ganayee MA, Bhat AQ, Dar GH, Alghamdi BS, Hashem AM, Dar MJ, Ashraf GM, Maqbool T. Clinical relevance of biomarkers, new therapeutic approaches, and role of post-translational modifications in the pathogenesis of Alzheimer's disease. Front Aging Neurosci 2022; 14:977411. [PMID: 36158539 PMCID: PMC9490081 DOI: 10.3389/fnagi.2022.977411] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/18/2022] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that causes progressive loss of cognitive functions like thinking, memory, reasoning, behavioral abilities, and social skills thus affecting the ability of a person to perform normal daily functions independently. There is no definitive cure for this disease, and treatment options available for the management of the disease are not very effective as well. Based on histopathology, AD is characterized by the accumulation of insoluble deposits of amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs). Although several molecular events contribute to the formation of these insoluble deposits, the aberrant post-translational modifications (PTMs) of AD-related proteins (like APP, Aβ, tau, and BACE1) are also known to be involved in the onset and progression of this disease. However, early diagnosis of the disease as well as the development of effective therapeutic approaches is impeded by lack of proper clinical biomarkers. In this review, we summarized the current status and clinical relevance of biomarkers from cerebrospinal fluid (CSF), blood and extracellular vesicles involved in onset and progression of AD. Moreover, we highlight the effects of several PTMs on the AD-related proteins, and provide an insight how these modifications impact the structure and function of proteins leading to AD pathology. Finally, for disease-modifying therapeutics, novel approaches, and targets are discussed for the successful treatment and management of AD.
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Affiliation(s)
- Ibtisam Mumtaz
- Laboratory of Nanotherapeutics and Regenerative Medicine, Department of Nanotechnology, University of Kashmir, Srinagar, India
| | - Mir Owais Ayaz
- Laboratory of Cell and Molecular Biology, Department of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Jammu, India
- Centre for Scientific and Innovative Research, Ghaziabad, Utter Pradesh, India
| | - Mohamad Sultan Khan
- Neurobiology and Molecular Chronobiology Laboratory, Department of Animal Biology, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Umar Manzoor
- Laboratory of Immune and Inflammatory Disease, Jeju Research Institute of Pharmaceutical Sciences, Jeju National University, Jeju, South Korea
| | - Mohd Azhardin Ganayee
- Laboratory of Nanotherapeutics and Regenerative Medicine, Department of Nanotechnology, University of Kashmir, Srinagar, India
- Department of Chemistry, Indian Institute of Technology Madras, Chennai, India
| | - Aadil Qadir Bhat
- Laboratory of Cell and Molecular Biology, Department of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Jammu, India
- Centre for Scientific and Innovative Research, Ghaziabad, Utter Pradesh, India
| | - Ghulam Hassan Dar
- Sri Pratap College, Cluster University Srinagar, Jammu and Kashmir, India
| | - Badrah S. Alghamdi
- Department of Physiology, Neuroscience Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Pre-clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Anwar M. Hashem
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Vaccines and Immunotherapy Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mohd Jamal Dar
- Laboratory of Cell and Molecular Biology, Department of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Jammu, India
- Centre for Scientific and Innovative Research, Ghaziabad, Utter Pradesh, India
| | - Gulam Md. Ashraf
- Pre-clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tariq Maqbool
- Laboratory of Nanotherapeutics and Regenerative Medicine, Department of Nanotechnology, University of Kashmir, Srinagar, India
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168
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Zhong X, Chen B, Hou L, Wang Q, Liu M, Yang M, Zhang M, Zhou H, Wu Z, Zhang S, Lin G, Ning Y. Shared and specific dynamics of brain activity and connectivity in amnestic and nonamnestic mild cognitive impairment. CNS Neurosci Ther 2022; 28:2053-2065. [PMID: 35975454 PMCID: PMC9627396 DOI: 10.1111/cns.13937] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 07/25/2022] [Accepted: 07/26/2022] [Indexed: 02/06/2023] Open
Abstract
AIMS The present study aimed to compare temporal variability in the spontaneous fluctuations of activity and connectivity between amnestic MCI (aMCI) and nonamnestic MCI (naMCI), which enhances the understanding of their different pathophysiologies and provides targets for individualized intervention. METHODS Sixty-five naMCI and 48 aMCI subjects and 75 healthy controls were recruited. A sliding window analysis was used to evaluate the dynamic amplitude of low-frequency fluctuations (dALFF), dynamic regional homogeneity (dReHo), and dynamic functional connectivity (dFC). The caudal/rostral hippocampus was selected as the seeds for calculating dFC. RESULTS Both aMCI and naMCI exhibited abnormal dALFF, dReHo, and hippocampal dFC compared with healthy controls. Compared with individuals with naMCI, those with aMCI exhibited (1) higher dALFF variability in the right putamen, left Rolandic operculum, and right middle cingulum, (2) lower dReHo variability in the right superior parietal lobule, and (3) lower dFC variability between the hippocampus and other regions (left superior occipital gyrus, middle frontal gyrus, inferior cerebellum, precuneus, and right superior frontal gyrus). Additionally, variability in dALFF, dReHo, and hippocampal dFC exhibited different associations with cognitive scores in aMCI and naMCI patients, respectively. Finally, dReHo variability in the right superior parietal lobule and dFC variability between the right caudal hippocampus and left inferior cerebellum exhibited partially mediated effects on the different memory scores between people with aMCI and naMCI. CONCLUSION The aMCI and naMCI patients exhibited shared and specific patterns of dynamic brain activity and connectivity. The dReHo of the superior parietal lobule and dFC of the hippocampus-cerebellum contributed to the memory heterogeneity of MCI subtypes. Analyzing the temporal variability in the spontaneous fluctuations of brain activity and connectivity provided a new perspective for exploring the different pathophysiological mechanisms in MCI subtypes.
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Affiliation(s)
- Xiaomei Zhong
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Ben Chen
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Le Hou
- Department of NeurologyThe Affiliated Brain Hospital of Guangzhou Medical UniversityGuangzhouGuangdong ProvinceChina
| | - Qiang Wang
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina,Department of Geriatric PsychiatryThe Second People's Hospital of Dali Bai Autonomous PrefectureDaliYunnan ProvinceChina
| | - Meiling Liu
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Mingfeng Yang
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Min Zhang
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Huarong Zhou
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Zhangying Wu
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Si Zhang
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Gaohong Lin
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina
| | - Yuping Ning
- Center for Geriatric NeuroscienceThe Affiliated Brain Hospital of Guangzhou Medical University, Memory ClinicGuangzhouGuangdong ProvinceChina,The First School of Clinical Medicine, Southern Medical UniversityGuangzhouGuangdong ProvinceChina,Guangdong Engineering Technology Research Center for Translational Medicine of Mental DisordersGuangzhouChina
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169
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St-Pierre MK, VanderZwaag J, Loewen S, Tremblay MÈ. All roads lead to heterogeneity: The complex involvement of astrocytes and microglia in the pathogenesis of Alzheimer’s disease. Front Cell Neurosci 2022; 16:932572. [PMID: 36035256 PMCID: PMC9413962 DOI: 10.3389/fncel.2022.932572] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/11/2022] [Indexed: 01/04/2023] Open
Abstract
In recent years, glial cells have been acknowledged as key players in the pathogenesis of Alzheimer’s disease (AD), a neurodegenerative condition in which an accumulation of intracellular neurofibrillary tangles and extracellular fibrillar amyloid beta is notably observed in the central nervous system. Genome-wide association studies have shown, both in microglia and astrocytes, an increase in gene variants associated with a higher risk of developing late-onset AD. Microglia, the resident innate immune cells of the brain, and astrocytes, glial cells crucial for vascular integrity and neuronal support, both agglomerate near amyloid beta plaques and dystrophic neurites where they participate in the elimination of these harmful parenchymal elements. However, their role in AD pathogenesis has been challenging to resolve due to the highly heterogeneous nature of these cell populations, i.e., their molecular, morphological, and ultrastructural diversity, together with their ever-changing responsiveness and functions throughout the pathological course of AD. With the recent expansions in the field of glial heterogeneity through innovative advances in state-of-the-art microscopy and -omics techniques, novel concepts and questions arose, notably pertaining to how the diverse microglial and astrocytic states interact with each other and with the AD hallmarks, and how their concerted efforts/actions impact the progression of the disease. In this review, we discuss the recent advances and findings on the topic of glial heterogeneity, particularly focusing on the relationships of these cells with AD hallmarks (e.g., amyloid beta plaques, neurofibrillary tangles, synaptic loss, and dystrophic neurites) in murine models of AD pathology and post-mortem brain samples of patients with AD.
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Affiliation(s)
- Marie-Kim St-Pierre
- Département de Médecine Moléculaire, Université Laval, Quebec City, QC, Canada
- Axe Neurosciences, Center de Recherche du CHU de Québec, Université Laval, Quebec City, QC, Canada
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Jared VanderZwaag
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Neuroscience Graduate Program, University of Victoria, Victoria, BC, Canada
| | - Sophia Loewen
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Department of Biology, University of Victoria, Victoria, BC, Canada
| | - Marie-Ève Tremblay
- Département de Médecine Moléculaire, Université Laval, Quebec City, QC, Canada
- Axe Neurosciences, Center de Recherche du CHU de Québec, Université Laval, Quebec City, QC, Canada
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Neurology and Neurosurgery Department, McGill University, Montréal, QC, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
- Center for Advanced Materials and Related Technology (CAMTEC), University of Victoria, Victoria, BC, Canada
- *Correspondence: Marie-Ève Tremblay,
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170
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Janahi M, Aksman L, Schott JM, Mokrab Y, Altmann A. Nomograms of human hippocampal volume shifted by polygenic scores. eLife 2022; 11:e78232. [PMID: 35938915 PMCID: PMC9391046 DOI: 10.7554/elife.78232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/06/2022] [Indexed: 11/25/2022] Open
Abstract
Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44-82 from the UK Biobank (UKB), we built HV nomograms using Gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV polygenic scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm3 (2.3% of the average HV), which equates to 3 years of normal aging for a person aged ~65. Clinical impact of genetically adjusted nomograms was investigated by comparing 818 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database diagnosed as either cognitively normal (CN), having mild cognitive impairment (MCI) or Alzheimer's disease (AD) patients. While no significant change in the survival analysis was found for MCI-to-AD conversion, an average of 68% relative decrease was found in intra-diagnostic-group variance, highlighting the importance of genetic adjustment in untangling phenotypic heterogeneity.
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Affiliation(s)
- Mohammed Janahi
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College LondonLondonUnited Kingdom
- Medical and Population Genomics Lab, Human Genetics Department, Research Branch, Sidra MedicineDohaQatar
| | - Leon Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Jonathan M Schott
- Dementia Research Centre (DRC), Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Younes Mokrab
- Medical and Population Genomics Lab, Human Genetics Department, Research Branch, Sidra MedicineDohaQatar
- Department of Genetic Medicine, Weill Cornell Medicine-QatarDohaQatar
| | - Andre Altmann
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College LondonLondonUnited Kingdom
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171
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Wang L, Feng Q, Ge X, Chen F, Yu B, Chen B, Liao Z, Lin B, Lv Y, Ding Z. Textural features reflecting local activity of the hippocampus improve the diagnosis of Alzheimer’s disease and amnestic mild cognitive impairment: A radiomics study based on functional magnetic resonance imaging. Front Neurosci 2022; 16:970245. [PMID: 36003964 PMCID: PMC9393721 DOI: 10.3389/fnins.2022.970245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/19/2022] [Indexed: 11/14/2022] Open
Abstract
Background Textural features of the hippocampus in structural magnetic resonance imaging (sMRI) images can serve as potential diagnostic biomarkers for Alzheimer’s disease (AD), while exhibiting a relatively poor discriminant performance in detecting early AD, such as amnestic mild cognitive impairment (aMCI). In contrast to sMRI, functional magnetic resonance imaging (fMRI) can identify brain functional abnormalities in the early stages of cerebral disorders. However, whether the textural features reflecting local functional activity in the hippocampus can improve the diagnostic performance for AD and aMCI remains unclear. In this study, we combined the textural features of the amplitude of low frequency fluctuation (ALFF) in the slow-5 frequency band and structural images in the hippocampus to investigate their diagnostic performance for AD and aMCI using multimodal radiomics technique. Methods Totally, 84 AD, 50 aMCI, and 44 normal controls (NCs) were included in the current study. After feature extraction and feature selection, the radiomics models incorporating sMRI images, ALFF values and their combinations in the bilateral hippocampus were established for the diagnosis of AD and aMCI. The effectiveness of these models was evaluated by receiver operating characteristic (ROC) analysis. The radiomics models were further validated using the external data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results The results of ROC analysis showed that the radiomics models based on structural images in the hippocampus had a better diagnostic performance for AD compared with the models using ALFF, while the ALFF-based model exhibited better discriminant performance for aMCI than the models with structural images. The radiomics models based on the combinations of structural images and ALFF were found to exhibit the highest accuracy for distinguishing AD from NCs and aMCI from NCs. Conclusion In this study, we found that the textural features reflecting local functional activity could improve the diagnostic performance of traditional structural models for both AD and aMCI. These findings may deepen our understanding of the pathogenesis of AD, contributing to the early diagnosis of AD.
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Affiliation(s)
- Luoyu Wang
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Center for Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qi Feng
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiuhong Ge
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fenyang Chen
- The Fourth School of Medical, Zhejiang Chinese Medical University, Hangzhou, China
| | - Bo Yu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, China
| | - Bing Chen
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Zhengluan Liao
- Center for Rehabilitation Medicine, Department of Geriatric VIP No. 3, Department of Clinical Psychology, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Biying Lin
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yating Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- *Correspondence: Yating Lv,
| | - Zhongxiang Ding
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhongxiang Ding,
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172
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Obesity-Related Neuroinflammation: Magnetic Resonance and Microscopy Imaging of the Brain. Int J Mol Sci 2022; 23:ijms23158790. [PMID: 35955925 PMCID: PMC9368789 DOI: 10.3390/ijms23158790] [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: 07/17/2022] [Revised: 08/03/2022] [Accepted: 08/06/2022] [Indexed: 12/01/2022] Open
Abstract
Obesity is a major risk factor of Alzheimer’s disease and related dementias. The principal feature of dementia is a loss of neurons and brain atrophy. The mechanistic links between obesity and the neurodegenerative processes of dementias are not fully understood, but recent research suggests that obesity-related systemic inflammation and subsequent neuroinflammation may be involved. Adipose tissues release multiple proinflammatory molecules (fatty acids and cytokines) that impact blood and vessel cells, inducing low-grade systemic inflammation that can transition to tissues, including the brain. Inflammation in the brain—neuroinflammation—is one of key elements of the pathobiology of neurodegenerative disorders; it is characterized by the activation of microglia, the resident immune cells in the brain, and by the structural and functional changes of other cells forming the brain parenchyma, including neurons. Such cellular changes have been shown in animal models with direct methods, such as confocal microscopy. In humans, cellular changes are less tangible, as only indirect methods such as magnetic resonance (MR) imaging are usually used. In these studies, obesity and low-grade systemic inflammation have been associated with lower volumes of the cerebral gray matter, cortex, and hippocampus, as well as altered tissue MR properties (suggesting microstructural variations in cellular and molecular composition). How these structural variations in the human brain observed using MR imaging relate to the cellular variations in the animal brain seen with microscopy is not well understood. This review describes the current understanding of neuroinflammation in the context of obesity-induced systemic inflammation, and it highlights need for the bridge between animal microscopy and human MR imaging studies.
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173
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He Q, Shi L, Luo Y, Wan C, Malone IB, Mok VCT, Cole JH, Anatürk M. Validation of the Alzheimer's disease-resemblance atrophy index in classifying and predicting progression in Alzheimer's disease. Front Aging Neurosci 2022; 14:932125. [PMID: 36062150 PMCID: PMC9435378 DOI: 10.3389/fnagi.2022.932125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/14/2022] [Indexed: 12/01/2022] Open
Abstract
Background Automated tools for characterising dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer's disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction. Methods Age- and sex-matched 44 probable AD (Age: 69.13 ± 7.13; MMSE: 27-30) and 22 non-demented control (Age: 69.38 ± 7.21; MMSE: 27-30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong's test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed. Results AD-RAI had excellent same-day repeatability and excellent 2-week reproducibility. AD-RAI's AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients. Conclusions The AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.
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Affiliation(s)
- Qiling He
- UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Lin Shi
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | - Chao Wan
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ian B. Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Vincent C. T. Mok
- Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - James H. Cole
- Department of Computer Science, Faculty of Engineering Science, University College London, London, United Kingdom
- Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
| | - Melis Anatürk
- Department of Computer Science, Faculty of Engineering Science, University College London, London, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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174
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Chen CY, Lin YS, Lee WJ, Liao YC, Kuo YS, Yang AC, Fuh JL. Endophenotypic effects of the SORL1 variant rs2298813 on regional brain volume in patients with late-onset Alzheimer’s disease. Front Aging Neurosci 2022; 14:885090. [PMID: 35992588 PMCID: PMC9389408 DOI: 10.3389/fnagi.2022.885090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction: Two common variants of sortilin-related receptor 1 gene (SORL1), rs2298813 and rs1784933, have been associated with late-onset Alzheimer’s disease (AD) in the Han Chinese population in Taiwan. However, neuroimaging correlates of these two SORL1 variants remain unknown. We aimed to determine whether the two SORL1 polymorphisms were associated with any volumetric differences in brain regions in late-onset AD patients. Methods: We recruited 200 patients with late-onset AD from Taipei Veterans General Hospital. All patients received a structural magnetic resonance (MR) imaging brain scan and completed a battery of neurocognitive tests at enrollment. We followed up to assess changes in Mini-Mental State Examination (MMSE) scores in 155 patients (77.5%) at an interval of 2 years. Volumetric measures and cortical thickness of various brain regions were performed using FreeSurfer. Regression analysis controlled for apolipoprotein E status. Multiple comparisons were corrected for using the false discovery rate. Results: The homozygous major allele of rs2298813 was associated with larger volumes in the right putamen (p = 0.0442) and right pallidum (p = 0.0346). There was no link between the rs1784933 genotypes with any regional volume or thickness of the brain. In the rs2298813 homozygous major allele carriers, the right putaminal volume was associated with verbal fluency (p = 0.008), and both the right putaminal and pallidal volumes were predictive of clinical progression at follow-up (p = 0.020). In the minor allele carriers, neither of the nuclei was related to cognitive test performance or clinical progression. Conclusion: The major and minor alleles of rs2298813 had differential effects on the right lentiform nucleus volume and distinctively modulated the association between the regional volume and cognitive function in patients with AD.
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Affiliation(s)
- Chun-Yu Chen
- Department of Medicine, Taipei Veterans General Hospital Yuli Branch, Hualien, Taiwan
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yung-Shuan Lin
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Ju Lee
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Neurological Institute, Taichung Veterans General Hospital, Taichung, Taiwan
- Dementia Center and Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Yi-Chu Liao
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Division of Peripheral Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Shan Kuo
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Albert C. Yang
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jong-Ling Fuh
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- *Correspondence: Jong-Ling Fuh
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175
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Diogo VS, Ferreira HA, Prata D. Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach. Alzheimers Res Ther 2022; 14:107. [PMID: 35922851 PMCID: PMC9347083 DOI: 10.1186/s13195-022-01047-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 07/13/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND Early and accurate diagnosis of Alzheimer's disease (AD) is essential for disease management and therapeutic choices that can delay disease progression. Machine learning (ML) approaches have been extensively used in attempts to develop algorithms for reliable early diagnosis of AD, although clinical usefulness, interpretability, and generalizability of the classifiers across datasets and MRI protocols remain limited. METHODS We report a multi-diagnostic and generalizable approach for mild cognitive impairment (MCI) and AD diagnosis using structural MRI and ML. Classifiers were trained and tested using subjects from the AD Neuroimaging Initiative (ADNI) database (n = 570) and the Open Access Series of Imaging Studies (OASIS) project database (n = 531). Several classifiers are compared and combined using voting for a decision. Additionally, we report tests of generalizability across datasets and protocols (IR-SPGR and MPRAGE), the impact of using graph theory measures on diagnostic classification performance, the relative importance of different brain regions on classification for better interpretability, and an evaluation of the potential for clinical applicability of the classifier. RESULTS Our "healthy controls (HC) vs. AD" classifier trained and tested on the combination of ADNI and OASIS datasets obtained a balanced accuracy (BAC) of 90.6% and a Matthew's correlation coefficient (MCC) of 0.811. Our "HC vs. MCI vs. AD" classifier trained and tested on the ADNI dataset obtained a 62.1% BAC (33.3% being the by-chance cut-off) and 0.438 MCC. Hippocampal features were the strongest contributors to the classification decisions (approx. 25-45%), followed by temporal (approx. 13%), cingulate, and frontal regions (approx. 8-13% each), which is consistent with our current understanding of AD and its progression. Classifiers generalized well across both datasets and protocols. Finally, using graph theory measures did not improve classification performance. CONCLUSIONS In sum, we present a diagnostic tool for MCI and AD trained using baseline scans and a follow-up diagnosis regardless of progression, which is multi-diagnostic, generalizable across independent data sources and acquisition protocols, and with transparently reported performance. Rated as potentially clinically applicable, our tool may be clinically useful to inform diagnostic decisions in dementia, if successful in real-world prospective clinical trials.
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Affiliation(s)
- Vasco Sá Diogo
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, 1749-016, Lisboa, Portugal.
- Iscte-Instituto Universitário de Lisboa, CIS-Iscte, Lisboa, Portugal.
| | - Hugo Alexandre Ferreira
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, 1749-016, Lisboa, Portugal
| | - Diana Prata
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, 1749-016, Lisboa, Portugal.
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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176
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Leonardsen EH, Peng H, Kaufmann T, Agartz I, Andreassen OA, Celius EG, Espeseth T, Harbo HF, Høgestøl EA, Lange AMD, Marquand AF, Vidal-Piñeiro D, Roe JM, Selbæk G, Sørensen Ø, Smith SM, Westlye LT, Wolfers T, Wang Y. Deep neural networks learn general and clinically relevant representations of the ageing brain. Neuroimage 2022; 256:119210. [PMID: 35462035 PMCID: PMC7614754 DOI: 10.1016/j.neuroimage.2022.119210] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/16/2022] [Accepted: 04/11/2022] [Indexed: 12/17/2022] Open
Abstract
The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.
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Affiliation(s)
- Esten H Leonardsen
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Han Peng
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Germany
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Elisabeth Gulowsen Celius
- Department of Neurology, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thomas Espeseth
- Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychology, Bjørknes University College, Oslo, Norway
| | - Hanne F Harbo
- Department of Neurology, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Einar A Høgestøl
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Neurology, Oslo University Hospital, Norway
| | - Ann-Marie de Lange
- Department of Psychology, University of Oslo, Oslo, Norway; LREN, Centre for Research in Neurosciences-Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | | | - James M Roe
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Geir Selbæk
- Norwegian National Advisory Unit on Aging and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Department of Psychology, University of Oslo, Oslo, Norway
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177
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Batta I, Abrol A, Calhoun VD, the Alzheimer’s Disease Neuroimaging Initiative. SVR-based Multimodal Active Subspace Analysis for the Brain using Neuroimaging Data.. [DOI: 10.1101/2022.07.28.501879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
ABSTRACTUnderstanding the patterns of changes in brain function and structure due to various disorders and diseases is of utmost importance. There have been numerous efforts toward successful biomarker discovery for complex brain disorders by evaluating neuroimaging datasets with novel analytical frameworks. However, due to the multi-faceted nature of the disorders involving a wide and overlapping range of symptoms as well as complex changes in structural and functional brain networks, it is increasingly important to devise computational frameworks that can consider the underlying patterns of heterogeneous changes with specific target assessments, at the same time producing a summarizing output from the high-dimensional neuroimaging data. While various machine learning approaches focus on diagnostic prediction, many learning frameworks analyze important features at the level of brain regions involved in prediction using supervised methods. Unsupervised learning methods have also been utilized to break down the neuroimaging features into lower dimensional components. However, most learning frameworks either do not consider the target assessment information while extracting brain subspaces, or can extract only higher dimensional importance associations as an ordered list of involved features, making manual interpretation at the level of subspaces difficult. We present a novel multimodal active subspace learning framework to understand various subspaces within the brain that are associated with changes in particular biological and cognitive traits. For a given cognitive or biological trait, our framework performs a decomposition of the feature importances to extract robust multimodal subspaces that define the most significant change in the given trait. Through a rigorous cross-validation procedure on an Alzheimer’s disease (AD) dataset, we show that our framework can extract subspaces covering both functional and structural modalities, which are specific to a given clinical assessment (like memory and other cognitive skills) and also retain predictive performance in standard machine learning algorithms. We show that our framework not only uncovers AD-related brain regions (e.g., hippocampus, entorhinal cortex) in the associated brain subspaces, but also enables an automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and cognitive skill proficiency related to brain disorders like AD.
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Mukaino T, Uehara T, Yokohama J, Okadome T, Arakawa T, Yokoyama S, Sakata A, Takase KI, Togao O, Akamatsu N, Shigeto H, Isobe N, Kira JI. Atrophy of the hippocampal CA1 subfield relates to long-term forgetting in focal epilepsy. Epilepsia 2022; 63:2623-2636. [PMID: 35892321 DOI: 10.1111/epi.17378] [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: 03/17/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE The mechanisms underlying accelerated long-term forgetting (ALF) in patients with epilepsy are still under investigation. We examined the contribution of hippocampal subfields and their morphology to long-term memory performance in patients with focal epilepsy. METHODS We prospectively assessed long-term memory and performed magnetic resonance imaging in 80 patients with focal epilepsy (61 with temporal lobe epilepsy and 19 with extratemporal lobe epilepsy) and 30 healthy controls. The patients also underwent electroencephalography recording. Verbal and visuospatial memory was tested 30 seconds, 10 minutes, and 1 week after learning. We assessed the volumes of the whole hippocampus and seven subfields and deformation of the hippocampal shape. The contributions of the hippocampal volumes and shape deformation to long-term forgetting, controlling for confounding factors, including the presence of interictal epileptiform discharges, were assessed by multiple regression analyses. RESULTS Patients with focal epilepsy had lower intelligence quotients and route recall scores at 10 minutes than controls. The focal epilepsy group had smaller volumes of both the right and left hippocampal tails than the control group, but there were no significant group differences for the volumes of the whole hippocampus or other hippocampal subfields. Multiple regression analyses showed a significant association between the left CA1 volume and the 1-week story retention (β = 7.76; Bonferroni-corrected P = 0.044), but this was not found for the whole hippocampus or other subfield volumes. Hippocampal shape analyses revealed that atrophy of the superior-lateral, superior-central, and inferior-medial regions of the left hippocampus, corresponding to CA1 and CA2/3, was associated with the verbal retention rate. SIGNIFICANCE Our results suggest that atrophy of the hippocampal CA1 region and its associated structures disrupts long-term memory consolidation in focal epilepsy. Neuronal cell loss in specific hippocampal subfields could be a key underlying cause of ALF in patients with epilepsy.
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Affiliation(s)
- Takahiko Mukaino
- Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Taira Uehara
- Department of Neurology, School of Medicine, International University of Health and Welfare, Narita, Japan
| | - Jun Yokohama
- Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toshiki Okadome
- Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomomi Arakawa
- Department of Rehabilitation Medicine, Kyushu University Hospital, Fukuoka, Japan
| | | | - Ayumi Sakata
- Department of Clinical Chemistry and Laboratory Medicine, Kyushu University Hospital, Fukuoka, Japan
| | | | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naoki Akamatsu
- Department of Neurology, School of Medicine, International University of Health and Welfare, Narita, Japan.,Sleep and Epilepsy Disorders Center, Fukuoka, Fukuoka, Japan
| | - Hiroshi Shigeto
- Division of Medical Technology, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Noriko Isobe
- Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Jun-Ichi Kira
- Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Translational Neuroscience Center, Graduate School of Medicine, and School of Pharmacy at Fukuoka, International University of Health and Welfare, Okawa, Japan.,Department of Neurology, Brain and Nerve Center, Fukuoka Central Hospital, International University of Health and Welfare, Fukuoka, Japan
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179
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Saini F, Dell’Acqua F, Strydom A. Structural Connectivity in Down Syndrome and Alzheimer's Disease. Front Neurosci 2022; 16:908413. [PMID: 35937882 PMCID: PMC9354601 DOI: 10.3389/fnins.2022.908413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/21/2022] [Indexed: 12/02/2022] Open
Abstract
Down syndrome (DS) arises from the triplication of chromosome 21, which leads to an atypical neurodevelopment and the overproduction of the amyloid precursor protein, predisposing to early Alzheimer's disease (AD). Not surprisingly, trisomy 21 is widely considered a model to study predementia stages of AD. After decades, in which neural loss was the main focus, research in AD is now moving toward understanding the neurodegenerative aspects affecting white matter. Motivated by the development of magnetic resonance imaging (MRI)-based diffusion techniques, this shift in focus has led to several exploratory studies on both young and older individuals with DS. In this review, we synthesise the initial efforts made by researchers in characterising in-vivo structural connectivity in DS, together with the AD footprint on top of such pre-existing connectivity related to atypical brain development. The white matter structures found to be affected in DS are the corpus callosum and all the main long-association fibres, namely the inferior fronto-occipital fasciculus, the inferior and superior longitudinal fasciculus, the uncinate fasciculus and the cingulum bundle. Furthermore, the cingulum bundle and the corpus callosum appear to be particularly sensitive to early AD changes in this population. Findings are discussed in terms of their functional significance, alongside methodological considerations and implications for future research.
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Affiliation(s)
- Fedal Saini
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Flavio Dell’Acqua
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Andre Strydom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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Tea Polyphenols as Prospective Natural Attenuators of Brain Aging. Nutrients 2022; 14:nu14153012. [PMID: 35893865 PMCID: PMC9332553 DOI: 10.3390/nu14153012] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/18/2022] [Accepted: 07/18/2022] [Indexed: 12/26/2022] Open
Abstract
No organism can avoid the process of aging, which is often accompanied by chronic disease. The process of biological aging is driven by a series of interrelated mechanisms through different signal pathways, including oxidative stress, inflammatory states, autophagy and others. In addition, the intestinal microbiota play a key role in regulating oxidative stress of microglia, maintaining homeostasis of microglia and alleviating age-related diseases. Tea polyphenols can effectively regulate the composition of the intestinal microbiota. In recent years, the potential anti-aging benefits of tea polyphenols have attracted increasing attention because they can inhibit neuroinflammation and prevent degenerative effects in the brain. The interaction between human neurological function and the gut microbiota suggests that intervention with tea polyphenols is a possible way to alleviate brain-aging. Studies have been undertaken into the possible mechanisms underpinning the preventative effect of tea polyphenols on brain-aging mediated by the intestinal microbiota. Tea polyphenols may be regarded as potential neuroprotective substances which can act with high efficiency and low toxicity.
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181
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Mashal Y, Abdelhady H, Iyer AK. Comparison of Tau and Amyloid-β Targeted Immunotherapy Nanoparticles for Alzheimer’s Disease. Biomolecules 2022; 12:biom12071001. [PMID: 35883556 PMCID: PMC9313310 DOI: 10.3390/biom12071001] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/06/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Alzheimer’s disease (AD) is a rapidly growing global concern associated with the accumulation of amyloid-β plaques and intracellular neurofibrillary tangles in the brain combined with a high acetylcholinesterase activity. AD diagnosis is usually made too late, when patients have an extensive neuronal death, and brain damage is irreversible. Several therapeutic targets have been defined mainly related to two hypotheses of AD: the tau hypothesis and the amyloid-β hypothesis. Here, we intend to investigate and to compare different therapeutic approaches for AD, mainly based on nanoparticles (NPs) targeted at the brain and at the pathological hallmarks of the disease. We analyzed preclinical trials that have successfully improved drug bioavailability in the brain by using targeted nanocarriers towards either tau, amyloid-β, or both. We then compared these trials to find out which protein is more efficient in therapeutic targeting. We found that the search for a cure was mostly based on the amyloid-β hypothesis, with Aβ dysplasia emerging as the most confirmed and convincing therapeutic target. Targeted NPs have proven useful to enhance both the bioavailability and the performance of therapies against AD in animal models. A better understanding of AD mechanisms will help the successful application of targeted NPs for combined therapies.
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Affiliation(s)
- Yara Mashal
- Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48201, USA;
- International Academy East, Troy, MI 48085, USA
| | - Hosam Abdelhady
- Department of Physiology & Pharmacology, College of Osteopathic Medicine, Sam Houston State University, Conroe, TX 77304, USA
- Correspondence: (H.A.); (A.K.I.); Tel.: +1-936-202-5221 (H.A.); +1-313-577-5875 (A.K.I.)
| | - Arun K. Iyer
- Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48201, USA;
- Correspondence: (H.A.); (A.K.I.); Tel.: +1-936-202-5221 (H.A.); +1-313-577-5875 (A.K.I.)
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Vlegels N, Ossenkoppele R, van der Flier WM, Koek HL, Reijmer YD, Wisse LEM, Biessels GJ. Does Loss of Integrity of the Cingulum Bundle Link Amyloid-β Accumulation and Neurodegeneration in Alzheimer’s Disease? J Alzheimers Dis 2022; 89:39-49. [DOI: 10.3233/jad-220024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration. Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy. Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer’s Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant. Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results. Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy.
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Affiliation(s)
- Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, VU University Medical Center, Amsterdam, The Netherlands
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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183
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Nissim NR, Harvey DY, Haslam C, Friedman L, Bharne P, Litz G, Phillips JS, Cousins KAQ, Xie SX, Grossman M, Hamilton RH. Through Thick and Thin: Baseline Cortical Volume and Thickness Predict Performance and Response to Transcranial Direct Current Stimulation in Primary Progressive Aphasia. Front Hum Neurosci 2022; 16:907425. [PMID: 35874157 PMCID: PMC9302040 DOI: 10.3389/fnhum.2022.907425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/02/2022] [Indexed: 11/23/2022] Open
Abstract
Objectives We hypothesized that measures of cortical thickness and volume in language areas would correlate with response to treatment with high-definition transcranial direct current stimulation (HD-tDCS) in persons with primary progressive aphasia (PPA). Materials and Methods In a blinded, within-group crossover study, PPA patients (N = 12) underwent a 2-week intervention HD-tDCS paired with constraint-induced language therapy (CILT). Multi-level linear regression (backward-fitted models) were performed to assess cortical measures as predictors of tDCS-induced naming improvements, measured by the Western Aphasia Battery-naming subtest, from baseline to immediately after and 6 weeks post-intervention. Results Greater baseline thickness of the pars opercularis significantly predicted naming gains (p = 0.03) immediately following intervention, while greater thickness of the middle temporal gyrus (MTG) and lower thickness of the superior temporal gyrus (STG) significantly predicted 6-week naming gains (p's < 0.02). Thickness did not predict naming gains in sham. Volume did not predict immediate gains for active stimulation. Greater volume of the pars triangularis and MTG, but lower STG volume significantly predicted 6-week naming gains in active stimulation. Greater pars orbitalis and MTG volume, and lower STG volume predicted immediate naming gains in sham (p's < 0.05). Volume did not predict 6-week naming gains in sham. Conclusion Cortical thickness and volume were predictive of tDCS-induced naming improvement in PPA patients. The finding that frontal thickness predicted immediate active tDCS-induced naming gains while temporal areas predicted naming changes at 6-week suggests that a broader network of regions may be important for long-term maintenance of treatment gains. The finding that volume predicted immediate naming performance in the sham condition may reflect the benefits of behavioral speech language therapy and neural correlates of its short-lived treatment gains. Collectively, thickness and volume were predictive of treatment gains in the active condition but not sham, suggesting that pairing HD-tDCS with CILT may be important for maintaining treatment effects.
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Affiliation(s)
- Nicole R. Nissim
- Laboratory for Cognition and Neural Stimulation, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Moss Rehabilitation Research Institute, Elkins Park, PA, United States
| | - Denise Y. Harvey
- Laboratory for Cognition and Neural Stimulation, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher Haslam
- Laboratory for Cognition and Neural Stimulation, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Leah Friedman
- Laboratory for Cognition and Neural Stimulation, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Pandurang Bharne
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Geneva Litz
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Jeffrey S. Phillips
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Katheryn A. Q. Cousins
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Roy H. Hamilton
- Laboratory for Cognition and Neural Stimulation, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
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Trapp S, Guitart-Masip M, Schröger E. A link between age, affect, and predictions? Eur J Ageing 2022; 19:945-952. [PMID: 36692760 PMCID: PMC9729523 DOI: 10.1007/s10433-022-00710-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/10/2022] [Indexed: 01/26/2023] Open
Abstract
The prevalence of depressive symptoms decreases from late adolescence to middle age adulthood. Furthermore, despite significant losses in motor and cognitive functioning, overall emotional well-being tends to increase with age, and a bias to positive information has been observed multiple times. Several causes have been discussed for this age-related development, such as improvement in emotion regulation, less regret, and higher socioeconomic status. Here, we explore a further explanation. Our minds host mental models that generate predictions about forthcoming events to successfully interact with our physical and social environment. To keep these models faithful, the difference between the predicted and the actual event, that is, the prediction error, is computed. We argue that prediction errors are attenuated in the middle age and older mind, which, in turn, may translate to less negative affect, lower susceptibility to affective disorders, and possibly, to a bias to positive information. Our proposal is primarily linked to perceptual inferences, but may hold as well for higher-level, cognitive, and emotional forms of error processing.
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Affiliation(s)
- Sabrina Trapp
- grid.434949.70000 0001 1408 3925Macromedia University of Applied Sciences, Munich, Germany ,grid.4714.60000 0004 1937 0626Aging Research Center, Karolinska Institutet, 17165 Stockholm, Sweden
| | - Marc Guitart-Masip
- grid.4714.60000 0004 1937 0626Aging Research Center, Karolinska Institutet, 17165 Stockholm, Sweden ,grid.467087.a0000 0004 0442 1056Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm, Sweden ,grid.83440.3b0000000121901201Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, WC1B 5EH UK ,grid.9647.c0000 0004 7669 9786Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany
| | - Erich Schröger
- grid.4714.60000 0004 1937 0626Aging Research Center, Karolinska Institutet, 17165 Stockholm, Sweden ,grid.9647.c0000 0004 7669 9786Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany
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Takagi S, Yamashiro K, Sugihara G, Matsuura M, Takahashi H. Hippocampal calcification and its effects on cognitive function and symptoms in dementia. Psychogeriatrics 2022; 22:427-432. [PMID: 35445506 DOI: 10.1111/psyg.12831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Hippocampal calcification (HC), highly prevalent in older people, has not attracted attention until recently. Despite its potential effects on cognition and behaviour, and its possible impact on the diagnosis and severity of dementia, it has not been investigated. This study aimed to evaluate the prevalence of HC and its influence on cognition and behavioural symptoms in patients with dementia. METHODS Data from consecutive patients who visited a medical centre for dementia, for the first time between April 2016 and September 2018, were extracted and analysed. These data included the patients' demographics, the presence of HC and hippocampal thickness as measured on computed tomography, the diagnosis of dementia and its type, cognitive function measured using the Mini-Mental State Examination and the Clock Drawing Test, and the chief complaints or symptoms prompting the visit. RESULTS A high incidence of HC (85/267 patients) was observed. There was no significant difference in the ages of patients with and without HC. Patients with HC had higher cognitive function than those without HC at their first visit. This result was contrary to our expectations as it was not explained by the chief complaints recorded at the first visit. CONCLUSIONS Our study showed a high prevalence of HC in older patients with dementia. Patients with HC had better cognitive function than did those without HC during their first hospital visit. This study suggests that HC may not affect the cognitive functions related to dementia. However, further research is needed to evaluate the long-term consequences of dementia with HC.
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Affiliation(s)
- Shunsuke Takagi
- Department of Psychiatry and Behavioural Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.,Ureshinogaoka Samariyabito Hospital, Okinawa, Japan
| | | | - Genichi Sugihara
- Department of Psychiatry and Behavioural Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masato Matsuura
- Ureshinogaoka Samariyabito Hospital, Okinawa, Japan.,Tazaki Hospital, Okinawa, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioural Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Barca ML, Alnæs D, Engedal K, Persson K, Eldholm RS, Siafarikas N, Selseth Almdahl I, Stylianou-Korsnes M, Saltvedt I, Selbæk G, Westlye LT. Brain Morphometric Correlates of Depressive Symptoms among Patients with and without Dementia. Dement Geriatr Cogn Dis Extra 2022; 12:107-114. [PMID: 35950148 PMCID: PMC9251457 DOI: 10.1159/000521114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 11/21/2021] [Indexed: 12/02/2022] Open
Abstract
Introduction Findings regarding brain morphometry among patients with dementia and concomitant depressive symptoms have been inconsistent. Thus, the aim of the present study was to test the hypothesis that dementia and concomitant depressive symptoms are associated with structural brain changes in the temporal lobe measured with structural magnetic resonance imaging (MRI). Methods A sample of 492 patients from Norwegian memory clinics (n = 363) and Old Age Psychiatry services (n = 129) was studied. The assessment included the Cornell Scale for Depression in Dementia (CSDD), Instrumental Activities of Daily Living Scale, Mini Mental State Examination, and MRI of the brain, processed with FreeSurfer to derive ROI measures of cortical thickness, volume, and area using the Desikan-Killiany parcellation, as well as subcortical volumes. Dementia was diagnosed according to ICD-10 research criteria. Correlates of brain morphometry using multiple linear regression were examined. Results Higher scores on the CSDD were associated with larger cortical volume (β = 0.125; p value = 0.003) and area of the left isthmus of the cingulate gyrus (β = 0.151; p value = <0.001) across all patients. Inclusion of an interaction term (dementia × CSDD) revealed a smaller area in the left temporal pole (β = −0.345; p value = 0.001) and right-transverse temporal cortex (β = −0.321; p value = 0.001) in patients with dementia and depressive symptoms. Discussion/Conclusion We confirm the previous findings of structural brain changes in temporal regions among patients with dementia and concomitant depressive symptoms. This may contribute to a better understanding of the mechanisms underlying depression in dementia. To the best of our knowledge, this is the largest study conducted on this topic to date.
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Affiliation(s)
- Maria Lage Barca
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- *Maria Lage Barca,
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Bjørknes College, Oslo, Norway
| | - Knut Engedal
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Karin Persson
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Rannveig Sakshaug Eldholm
- Department of Neuromedicine and Movement Science, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nikias Siafarikas
- Department of Geriatric Psychiatry, Akershus University Hospital, Lørenskog, Norway
| | - Ina Selseth Almdahl
- Department of Old Age Psychiatry, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Maria Stylianou-Korsnes
- Department of Old Age Psychiatry, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Geriatric Department, St. Olav Hospital, University Hospital of Trondheim, Trondheim, Norway
| | - Geir Selbæk
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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Zamani J, Sadr A, Javadi AH. Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative. PLoS One 2022; 17:e0267608. [PMID: 35727837 PMCID: PMC9212187 DOI: 10.1371/journal.pone.0267608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.
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Affiliation(s)
- Jafar Zamani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ali Sadr
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, United Kingdom
- School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
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188
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Systemic inflammatory markers in relation to cognitive function and measures of brain atrophy: a Mendelian randomization study. GeroScience 2022; 44:2259-2270. [PMID: 35689786 PMCID: PMC9616983 DOI: 10.1007/s11357-022-00602-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/03/2022] [Indexed: 11/07/2022] Open
Abstract
Observational studies have implied associations between multiple cytokines and cognitive decline, anti-inflammatory drugs however did not yield any protective effects on cognitive decline. We aimed to assess the associations of systemic inflammation, as measured by multiple cytokine and growth factor, with cognitive performance and brain atrophy using two-sample Mendelian randomization (MR). Independent genetic instruments (p < 5e − 8 and p < 5e − 6) for 41 systemic inflammatory markers were retrieved from a genome-wide association study conducted in 8293 Finnish participants. Summary statistics for gene-outcome associations were obtained for cognitive performance (N = 257,841) and for brain atrophy measures of cerebral cortical surface area and thickness (N = 51,665) and hippocampal volume (N = 33,536). To rule out the heterogeneity in the cognitive performance, we additionally included three domains: the fluid intelligence score (N = 108,818), prospective memory result (N = 111,099), and reaction time (N = 330,069). Main results were computed by inverse-variance weighting; sensitivity analyses taking pleiotropy and invalid instruments into account were performed by using weighted-median estimator, MR-Egger, and MR PRESSO. After correcting for multiple testing using false discovery rate, only genetically predicted (with p < 5e − 6 threshold) per-SD (standard deviation) higher IL-8 was associated with − 0.103 (− 0.155, − 0.051, padjusted = 0.004) mm3 smaller hippocampal volume and higher intelligence fluid score [β: 0.103 SD (95% CI: 0.042, 0.165), padjusted = 0.041]. Sensitivity analyses generally showed similar results, and no pleiotropic effect, heterogeneity, or possible reverse causation was detected. Our results suggested a possible causal association of high IL-8 levels with better cognitive performance but smaller hippocampal volume among the general healthy population, highlighting the complex role of inflammation in dementia-related phenotypes. Further research is needed to elucidate mechanisms underlying these associations.
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189
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Sun Z, Zhao S, Suo X, Dou Y. Sirt1 protects against hippocampal atrophy and its induced cognitive impairment in middle-aged mice. BMC Neurosci 2022; 23:33. [PMID: 35668361 PMCID: PMC9169381 DOI: 10.1186/s12868-022-00718-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/23/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Sirtuin 1 (Sirt1) is a recognized longevity gene and has been shown to be associated with aging and its related diseases. Hippocampal volume is considered to be the most sensitive brain imaging phenotype for cognition, but the effect of Sirt1 on hippocampal morphology during aging has not been reported. RESULTS Herein, we investigated the effect of conditional Sirt1 knockdown on hippocampal volume in middle-aged mice, as well as its cognitive function and the underlying molecular mechanisms. Brain structural magnetic resonance imaging (MRI) showed that adeno-associated virus (AAV) mediated hippocampal Sirt1 knockdown caused hippocampal atrophy in 8-month-old mice. Open field test (OFT) and Morris Water Maze (MWM) test revealed that hippocampal Sirt1 knockdown significantly weakened spatial learning and memory of mice without effect on anxiety and exploratory behavior. Western blotting analysis showed that P-tau levels at serine 396 epitope were significantly increased with slightly decreased T-tau levels, while PSD95 and NMDAR2B levels were obviously reduced, indicating that hippocampal Sirt1 knockdown could activate tau hyperphosphorylation and synaptic damage. CONCLUSIONS This work revealed that Sirt1 is an important protective gene against hippocampal atrophy and its induced cognitive impairment during aging, providing potential therapeutic targets for the prevention and intervention of aging-related neuropsychic diseases.
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Affiliation(s)
- Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.,School of Medical Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Shuang Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China
| | - Xinjun Suo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.,School of Medical Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Yan Dou
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.
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190
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Haehner A, Chen B, Espin M, Haussmann R, Matthes C, Desser D, Loessner L, Brandt MD, Donix M, Hummel T. Training with Odors Impacts Hippocampal Thickness in Patients with Mild Cognitive Impairment. J Alzheimers Dis 2022; 88:743-755. [DOI: 10.3233/jad-220248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The olfactory system is affected early in Alzheimer’s disease and olfactory loss can already be observed in patients with mild cognitive impairment (MCI). Olfactory training is effective for improving olfactory and cognitive function by stimulating the olfactory pathway, but its effect on patients with MCI remains unclear. Objective: The aim of this randomized, prospective, controlled, blinded study was to assess whether a 4-month period of olfactory training (frequent short-term sniffing various odors) may have an effect on olfactory function, cognitive function, and morphology of medial temporal lobe (MTL) subregions and olfactory bulb in MCI patients. Methods: A total of thirty-seven MCI patients were randomly assigned to the training group or a placebo group, which were performed twice a day for 4 months. Olfactory assessments, cognitive tests and magnetic resonance imaging were performed at the baseline and follow-up period. Results: After the training, there was an increase in odor discrimination, and increased cortical thickness of bilateral hippocampus (CA23DG and CA1) and mean MTL. Additionally, the change of olfactory score was positively associated with change of volume of olfactory bulb and hippocampus; the change of global cognition was positively associated with change of cortical thickness of hippocampus, entorhinal cortex and mean MTL; the change of cortical thickness of entorhinal cortex was positively associated with change of executive function. Conclusion: Olfactory training was associated with an increase in cortical thickness of the hippocampus but not olfactory bulb volume in patients with MCI. Olfactory training may serve as an early intervention of preventing hippocampal atrophy.
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Affiliation(s)
| | - Ben Chen
- Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Dresden, Germany
- Memory Clinic, Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, Guangdong Province, China
| | - Melanie Espin
- Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Dresden, Germany
| | | | | | - Dmitriy Desser
- Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Dresden, Germany
| | | | - Moritz D. Brandt
- Department of Neurology, TU Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Markus Donix
- Department of Psychiatry, TU Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Thomas Hummel
- Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Dresden, Germany
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191
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Functional Imaging for Neurodegenerative Diseases. Presse Med 2022; 51:104121. [PMID: 35490910 DOI: 10.1016/j.lpm.2022.104121] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 03/13/2022] [Accepted: 04/11/2022] [Indexed: 12/16/2022] Open
Abstract
Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.
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192
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Lower Posterior Cingulate N-Acetylaspartate to Creatine Level in Early Detection of Biologically Defined Alzheimer's Disease. Brain Sci 2022; 12:brainsci12060722. [PMID: 35741606 PMCID: PMC9220959 DOI: 10.3390/brainsci12060722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/18/2022] [Accepted: 05/28/2022] [Indexed: 02/06/2023] Open
Abstract
Alzheimer’s disease (AD) was recently defined as a biological construct to reflect neuropathologic status, and both abnormal amyloid and tau are required for a diagnosis of AD. We aimed to determine the proton MR spectroscopic (1H-MRS) patterns of the posterior cingulate in biologically defined AD. A total of 68 participants were included in this study, comprising 37 controls, 16 early AD, and 15 late AD, who were classified according to their amyloid and tau status and presence of hippocampal atrophy. Compared with controls, early AD showed lower N-acetylaspartate (NAA)/creatine (Cr) (p = 0.003), whereas late AD showed lower NAA/Cr and higher myoInositol (mI)/Cr (all with p < 0.05). Lower NAA/Cr correlated with a greater global amyloid load (r = −0.47, p < 0.001) and tau load (r = −0.51, p < 0.001) and allowed a discrimination of early AD from controls (p < 0.001). Subgroup analysis showed that NAA/Cr also allowed a differentiation of early AD from controls in the cognitively unimpaired subjects, with an area under the receiver operating characteristics curve, sensitivity, and specificity of 0.96, 100%, and 83.8%, respectively. Lower posterior cingulate NAA levels may help to inform underlying neuropathologic changes in the early stage of AD.
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193
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Chen B, Marquez-Nostra B, Belitzky E, Toyonaga T, Tong J, Huang Y, Cai Z. PET Imaging in Animal Models of Alzheimer’s Disease. Front Neurosci 2022; 16:872509. [PMID: 35685772 PMCID: PMC9171374 DOI: 10.3389/fnins.2022.872509] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
The successful development and translation of PET imaging agents targeting β-amyloid plaques and hyperphosphorylated tau tangles have allowed for in vivo detection of these hallmarks of Alzheimer’s disease (AD) antemortem. Amyloid and tau PET have been incorporated into the A/T/N scheme for AD characterization and have become an integral part of ongoing clinical trials to screen patients for enrollment, prove drug action mechanisms, and monitor therapeutic effects. Meanwhile, preclinical PET imaging in animal models of AD can provide supportive information for mechanistic studies. With the recent advancement of gene editing technologies and AD animal model development, preclinical PET imaging in AD models will further facilitate our understanding of AD pathogenesis/progression and the development of novel treatments. In this study, we review the current state-of-the-art in preclinical PET imaging using animal models of AD and suggest future research directions.
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194
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Bahsoun MA, Khan MU, Mitha S, Ghazvanchahi A, Khosravani H, Jabehdar Maralani P, Tardif JC, Moody AR, Tyrrell PN, Khademi A. FLAIR MRI biomarkers of the normal appearing brain matter are related to cognition. Neuroimage Clin 2022; 34:102955. [PMID: 35180579 PMCID: PMC8857609 DOI: 10.1016/j.nicl.2022.102955] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 01/04/2023]
Abstract
Normal appearing brain matter (NABM) biomarkers in FLAIR MRI are related to cognition. NABM texture in FLAIR MRI is correlated to mean diffusivity (MD) in dMRI. Analysis conducted on large multicentre FLAIR MRI dataset: 1400 subjects, 87 centers. NABM biomarkers vary differently across age and MoCA categories. Biomarkers showed differences in patients with AD dementia and vascular disease.
A novel biomarker panel was proposed to quantify macro and microstructural biomarkers from the normal-appearing brain matter (NABM) in multicentre fluid-attenuation inversion recovery (FLAIR) MRI. The NABM is composed of the white and gray matter regions of the brain, with the lesions and cerebrospinal fluid removed. The primary hypothesis was that NABM biomarkers from FLAIR MRI are related to cognitive outcome as determined by MoCA score. There were three groups of features designed for this task based on 1) texture: microstructural integrity (MII), macrostructural damage (MAD), microstructural damage (MID), 2) intensity: median, skewness, kurtosis and 3) volume: NABM to ICV volume ratio. Biomarkers were extracted from over 1400 imaging volumes from more than 87 centres and unadjusted ANOVA analysis revealed significant differences in means of the MII, MAD, and NABM volume biomarkers across all cognitive groups. In an adjusted ANCOVA model, a significant relationship between MoCA categories was found that was dependent on subject age for MII, MAD, intensity, kurtosis and NABM volume biomarkers. These results demonstrate that structural brain changes in the NABM are related to cognitive outcome (with different relationships depending on the age of the subjects). Therefore these biomarkers have high potential for clinical translation. As a secondary hypothesis, we investigated whether texture features from FLAIR MRI can quantify microstructural changes related to how “structured” or “damaged” the tissue is. Based on correlation analysis with diffusion weighted MRI (dMRI), it was shown that FLAIR MRI texture biomarkers (MII and MAD) had strong correlations to mean diffusivity (MD) which is related to tissue degeneration in the GM and WM regions. As FLAIR MRI is routinely collected for clinical neurological examinations, novel biomarkers from FLAIR MRI could be used to supplement current clinical biomarkers and for monitoring disease progression. Biomarkers could also be used to stratify patients into homogeneous disease subgroups for clinical trials, or to learn more about mechanistic development of dementia disease.
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Affiliation(s)
- M-A Bahsoun
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - M U Khan
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - S Mitha
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - A Ghazvanchahi
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - H Khosravani
- Hurvitz Brain Sciences Program Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - J-C Tardif
- Montreal Heart Institute, Montreal, QU, Canada; Department of Medicine, Université de Montréal, QU, Canada
| | - A R Moody
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - P N Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada; Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - A Khademi
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada; Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, Toronto, ON, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST), a partnership between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada
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195
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Francesca F, Caitlin A, Sarah L, Robyn GL. Antroquinonol administration in animal preclinical studies for Alzheimer's disease (AD): A new avenue for modifying progression of AD pathophysiology. Brain Behav Immun Health 2022; 21:100435. [PMID: 35252893 PMCID: PMC8892093 DOI: 10.1016/j.bbih.2022.100435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/28/2022] [Accepted: 02/22/2022] [Indexed: 11/28/2022] Open
Abstract
Despite the rise of Alzheimer's disease (AD) in an ageing population, no cure is currently available for this disorder. This study assessed the role of a natural compound, Antroquinonol, in modifying the progression of AD when administered at the start and/or before appearance of symptoms and when the disease was well established, in a transgenic animal model. Antroquinonol was administered daily for 8 weeks, in 11 week (early stage) and 9 month (late stage) male transgenic mice (3 times Transgenic mice PS1M146V, APPSwe, and tauP301L, 3 Tg XAD) and their respective aged controls. Behavioural testing (including Elevated Plus Maze Watermaze, Recognition object testing and Y maze) was performed at the end of the drug administration. In addition AD biomarkers (Amyloid beta 42 (Aβ42), tau and phospho-tau levels), oxidative stress and inflammatory markers, were assessed in tested mice brains after their sacrifice at the end of the treatment. When administered before the start of symptoms at 11 weeks, Antroquinonol treatment at 34 mg/kg (D2) and more consistently at 75 mg/kg (D3), had a significant effect on reducing systemic inflammatory markers (Interleukin 1, IL-1β and TNF-α) and AD biomarker (Amyloid Beta 42, Aβ42 and tau) levels in the brain. The reduction of behavioural impairment reported for 3TgXAD mice was observed significantly for the D3 drug dose only and for all behavioural tests, when administered at 11 weeks. Similarly, beneficial effects of Antroquinonol (at higher dose D3) were noted in the transgenic mice in terms of AD biomarkers (tau and phosphorylated-tau), systemic inflammatory (IL-1β), brain anti-inflammatory (Nrf2) and oxidative (3-Nitrotyrosine, 3NT) markers. Improvement of memory impairment was also reported when Antroquinonol (D3) was administered at late stage (9 months). Since Antroquinonol has been used without adverse effects in previous successful clinical trials, this drug may offer a new avenue of treatment to modify AD development and progression.
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Affiliation(s)
- Fernandez Francesca
- School of Behavioural and Health Sciences, Faculty of Health Sciences, Australian Catholic University, 1100 Nudgee Rd, Banyo, Queensland, 4014, Australia
- Centre for Genomics and Personalised Medicine, Genomics Research Centre, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, QLD, 4059, Australia
| | - Aust Caitlin
- School of Behavioural and Health Sciences, Faculty of Health Sciences, Australian Catholic University, 1100 Nudgee Rd, Banyo, Queensland, 4014, Australia
- Centre for Genomics and Personalised Medicine, Genomics Research Centre, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, QLD, 4059, Australia
| | - Lye Sarah
- School of Behavioural and Health Sciences, Faculty of Health Sciences, Australian Catholic University, 1100 Nudgee Rd, Banyo, Queensland, 4014, Australia
- Centre for Genomics and Personalised Medicine, Genomics Research Centre, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, QLD, 4059, Australia
| | - Griffiths Lyn Robyn
- Centre for Genomics and Personalised Medicine, Genomics Research Centre, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, QLD, 4059, Australia
- Corresponding author. Centre for Genomics and Personalised Health Genomics Research Centre, Queensland University of Technology, Australia.
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196
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Cavedo E, Tran P, Thoprakarn U, Martini JB, Movschin A, Delmaire C, Gariel F, Heidelberg D, Pyatigorskaya N, Ströer S, Krolak-Salmon P, Cotton F, Dos Santos CL, Dormont D. Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®. Eur Radiol 2022; 32:2949-2961. [PMID: 34973104 PMCID: PMC9038894 DOI: 10.1007/s00330-021-08385-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 09/15/2021] [Accepted: 10/21/2021] [Indexed: 12/05/2022]
Abstract
OBJECTIVES QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. METHODS Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. RESULTS The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. CONCLUSIONS QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. KEY POINTS • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.
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Affiliation(s)
- Enrica Cavedo
- Qynapse SAS, 130 rue de Lourmel, 75015, Paris, France.
| | - Philippe Tran
- Qynapse SAS, 130 rue de Lourmel, 75015, Paris, France
- Equipe-Projet ARAMIS, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Centre Inria de Paris, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Faculté de Médecine Sorbonne Université, Paris, France
| | | | | | | | | | - Florent Gariel
- Department of Neuroradiology, University Hospital of Bordeaux, Bordeaux, France
| | - Damien Heidelberg
- Faculty of Medicine, Claude-Bernard Lyon 1 University, 69000, Lyon, France
- Service de Radiologie and Laboratoire d'anatomie de Rockefeller, centre hospitalier Lyon Sud, hospices civils de Lyon, 69000, Lyon, France
| | - Nadya Pyatigorskaya
- Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
| | - Sébastian Ströer
- Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
| | - Pierre Krolak-Salmon
- Clinical and Research Memory Centre of Lyon, Hospices Civils de Lyon, Lyon, France
- University of Lyon, Lyon, France
- INSERM, U1028; UMR CNRS 5292, Lyon Neuroscience Research Center, Lyon, France
| | - Francois Cotton
- Radiology Department, centre hospitalier Lyon-Sud, hospices civils de Lyon, 69310, Pierre-Bénite, France
- Inserm U1044, CNRS UMR 5220, CREATIS, Université Lyon-1, 69100, Villeurbanne, France
| | | | - Didier Dormont
- Equipe-Projet ARAMIS, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Centre Inria de Paris, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Faculté de Médecine Sorbonne Université, Paris, France
- Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
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Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, D’Arcy RC, Song X. Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing Res Rev 2022; 77:101614. [PMID: 35358720 DOI: 10.1016/j.arr.2022.101614] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 03/02/2022] [Accepted: 03/24/2022] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. METHODS A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. RESULTS 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. DISCUSSION The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.
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198
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Esmaili Z, Naseh M, Karimi F, Moosavi M. A stereological study reveals nanoscale-alumina induces cognitive dysfunction in mice related to hippocampal structural changes. Neurotoxicology 2022; 91:245-253. [DOI: 10.1016/j.neuro.2022.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/24/2022] [Accepted: 05/16/2022] [Indexed: 11/25/2022]
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Yin X, Zhang X, Zhang J, Yang W, Sun X, Zhang H, Gao Z, Jiang H. High-Resolution Digital Panorama of Multiple Structures in Whole Brain of Alzheimer's Disease Mice. Front Neurosci 2022; 16:870520. [PMID: 35516801 PMCID: PMC9067162 DOI: 10.3389/fnins.2022.870520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 03/11/2022] [Indexed: 11/24/2022] Open
Abstract
Simultaneously visualizing Amyloid-β (Aβ) plaque with its surrounding brain structures at the subcellular level in the intact brain is essential for understanding the complex pathology of Alzheimer's disease, but is still rarely achieved due to the technical limitations. Combining the micro-optical sectioning tomography (MOST) system, whole-brain Nissl staining, and customized image processing workflow, we generated a whole-brain panorama of Alzheimer's disease mice without specific labeling. The workflow employed the steps that include virtual channel splitting, feature enhancement, iso-surface rendering, direct volume rendering, and feature fusion to extract and reconstruct the different signals with distinct gray values and morphologies. Taking advantage of this workflow, we found that the denser-distribution areas of Aβ plaques appeared with relatively more somata and smaller vessels, but show a dissimilar distributing pattern with nerve tracts. In addition, the entorhinal cortex and adjacent subiculum regions present the highest density and biggest diameter of plaques. The neuronal processes in the vicinity of these Aβ plaques showed significant structural alternation such as bending or abrupt branch ending. The capillaries inside or adjacent to the plaques were observed with abundant distorted micro-vessels and abrupt ending. Depicting Aβ plaques, somata, nerve processes and tracts, and blood vessels simultaneously, this panorama enables us for the first time, to analyze how the Aβ plaques interact with capillaries, somata, and processes at a submicron resolution of 3D whole-brain scale, which reveals potential pathological effects of Aβ plaques from a new cross-scale view. Our approach opens a door to routine systematic studies of complex interactions among brain components in mouse models of Alzheimer's disease.
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Affiliation(s)
- Xianzhen Yin
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Lingang Laboratory, Shanghai, China
- *Correspondence: Xianzhen Yin
| | - Xiaochuan Zhang
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Jingjing Zhang
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Weicheng Yang
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
| | - Xian Sun
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Haiyan Zhang
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Haiyan Zhang
| | - Zhaobing Gao
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Zhongshan Institute of Drug Discovery, Institution for Drug Discovery Innovation, Chinese Academy of Science, Zhongshan, China
- Zhaobing Gao
| | - Hualiang Jiang
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Lingang Laboratory, Shanghai, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
- School of Life Science and Technology, Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China
- Hualiang Jiang
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Goyzueta-Mamani LD, Chávez-Fumagalli MA, Alvarez-Fernandez K, Aguilar-Pineda JA, Nieto-Montesinos R, Davila Del-Carpio G, Vera-Lopez KJ, Lino Cardenas CL. Alzheimer's Disease: A Silent Pandemic - A Systematic Review on the Situation and Patent Landscape of the Diagnosis. Recent Pat Biotechnol 2022; 16:355-378. [PMID: 35400333 DOI: 10.2174/1872208316666220408114129] [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: 07/16/2021] [Revised: 01/13/2022] [Accepted: 02/17/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is characterized by cognitive impairment, tau protein deposits, and amyloid beta plaques. AD impacted 44 million people in 2016, and it is estimated to affect 100 million people by 2050. AD is disregarded as a pandemic compared with other diseases. To date, there is no effective treatment or diagnosis. OBJECTIVE We aimed to discuss the current tools used to diagnose COVID-19, to point out their potential to be adapted for AD diagnosis, and to review the landscape of existing patents in the AD field and future perspectives for AD diagnosis. METHOD We carried out a scientific screening following a research strategy in PubMed; Web of Science; the Derwent Innovation Index; the KCI-Korean Journal Database; SciELO; the Russian Science Citation index; and the CDerwent, EDerwent, and MDerwent index databases. RESULTS A total of 326 from 6,446 articles about AD and 376 from 4,595 articles about COVID-19 were analyzed. Of these, AD patents were focused on biomarkers and neuroimaging with no accurate, validated diagnostic methods, and only 7% of kit development patents were found. In comparison, COVID-19 patents were 60% about kit development for diagnosis; they are highly accurate and are now commercialized. CONCLUSION AD is still neglected and not recognized as a pandemic that affects the people and economies of all nations. There is a gap in the development of AD diagnostic tools that could be filled if the interest and effort that has been invested to tackle the COVID-19 emergency could also be applied for innovation.
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Affiliation(s)
- Luis Daniel Goyzueta-Mamani
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigacion, Universidad Catolica de Santa Maria, Arequipa, Peru
| | - Miguel Angel Chávez-Fumagalli
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigacion, Universidad Catolica de Santa Maria, Arequipa, Peru
| | - Karla Alvarez-Fernandez
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigacion, Universidad Catolica de Santa Maria, Arequipa, Peru
| | - Jorge A Aguilar-Pineda
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigacion, Universidad Catolica de Santa Maria, Arequipa, Peru
| | - Rita Nieto-Montesinos
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigacion, Universidad Catolica de Santa Maria, Arequipa, Peru
| | - Gonzalo Davila Del-Carpio
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigacion, Universidad Catolica de Santa Maria, Arequipa, Peru
| | - Karin J Vera-Lopez
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigacion, Universidad Catolica de Santa Maria, Arequipa, Peru
| | - Christian L Lino Cardenas
- Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
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