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Dounavi M, Mak E, Operto G, Muniz‐Terrera G, Bridgeman K, Koychev I, Malhotra P, Naci L, Lawlor B, Su L, Falcon C, Ritchie K, Ritchie CW, Gispert JD, O'Brien JT. Texture-based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population. Hum Brain Mapp 2024; 45:e26798. [PMID: 39081128 PMCID: PMC11289425 DOI: 10.1002/hbm.26798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 06/06/2024] [Accepted: 07/10/2024] [Indexed: 08/03/2024] Open
Abstract
Brain atrophy and cortical thinning are typically observed in people with Alzheimer's disease (AD) and, to a lesser extent, in those with mild cognitive impairment. In asymptomatic middle-aged apolipoprotein ε4 (ΑPOE4) carriers, who are at higher risk of future AD, study reports are discordant with limited evidence of brain structural differences between carriers and non-carriers of the ε4 allele. Alternative imaging markers with higher sensitivity at the presymptomatic stage, ideally quantified using typically acquired structural MRI scans, would thus be of great benefit for the detection of early disease, disease monitoring and subject stratification. In the present cross-sectional study, we investigated textural properties of T1-weighted 3T MRI scans in relation to APOE4 genotype, age and sex. We pooled together data from the PREVENT-Dementia and ALFA studies focused on midlife healthy populations with dementia risk factors (analysable cohort: 1585 participants; mean age 56.2 ± 7.4 years). Voxel-based and texture (examined features: contrast, entropy, energy, homogeneity) based morphometry was used to identify areas of volumetric and textural differences between APOE4 carriers and non-carriers. Textural maps were generated and were subsequently harmonised using voxel-wise COMBAT. For all analyses, APOE4, sex, age and years of education were used as model predictors. Interactions between APOE4 and age were further examined. There were no group differences in regional brain volume or texture based on APOE4 carriership or when age × APOE4 interactions were examined. Older people tended to have a less homogeneous textural profile in grey and white matter and a more homogeneous profile in the ventricles. A more heterogeneous textural profile was observed for females in areas such as the ventricles, frontal and parietal lobes and for males in the brainstem, cerebellum, precuneus and cingulate. Overall, we have shown the absence of volumetric and textural differences between APOE4 carriers and non-carriers at midlife and have established associations of textural features with ageing and sex.
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Affiliation(s)
- Maria‐Eleni Dounavi
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
| | - Elijah Mak
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
| | - Gregory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - Graciela Muniz‐Terrera
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
- Heritage College of Osteopathic MedicineOhio UniversityAthensOhioUSA
| | - Katie Bridgeman
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
| | | | - Paresh Malhotra
- Division of Brain ScienceImperial College Healthcare NHS TrustUK
| | - Lorina Naci
- Institute of Neuroscience, Trinity College Dublin, University of DublinIreland
| | - Brian Lawlor
- Institute of Neuroscience, Trinity College Dublin, University of DublinIreland
| | - Li Su
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
- Department of NeuroscienceUniversity of SheffieldSheffieldUK
| | - Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - Karen Ritchie
- INSERM and University of MontpellierMontpellierFrance
| | - Craig W. Ritchie
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - John T. O'Brien
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
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2
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Xiong S, Hou N, Tang F, Li J, Deng H. Association of cardiometabolic multimorbidity and adherence to a healthy lifestyle with incident dementia: a large prospective cohort study. Diabetol Metab Syndr 2023; 15:208. [PMID: 37876001 PMCID: PMC10594816 DOI: 10.1186/s13098-023-01186-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 10/09/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND The co-occurrence of cardiometabolic diseases (CMDs) is increasingly prevalent and has been associated with an additive risk of dementia in older adults, but the extent to which this risk can be offset by a healthy lifestyle is unknown. We aimed to examine the associations of cardiometabolic multimorbidity and lifestyle with incident dementia and related brain structural changes. METHODS This prospective study extracted health and lifestyle data from 171 538 UK Biobank participants aged 60 years or older without dementia at baseline between 2006 and 2010 and followed up until July 2021, as well as brain structural data in a nested imaging subsample of 11 972 participants. Cardiometabolic multimorbidity was defined as the presence of two or more CMDs among type 2 diabetes, coronary heart disease, stroke, and hypertension. Lifestyle patterns were determined based on 7 modifiable lifestyle factors including smoking, alcohol consumption, physical activity, diet, sleep duration, sedentary behavior, and social contact. RESULTS Over a median follow-up of 12.3 years, 4479 (2.6%) participants developed dementia. The presence of CMDs was dose-dependently associated with an increased risk of dementia. Compared with participants with no CMDs and a favourable lifestyle, those with ≥ 3 CMDs and an unfavourable lifestyle had a five times greater risk of developing dementia (HR 5.33, 95% CI 4.26-6.66). A significant interaction was found between CMD status and lifestyle (Pinteraction=0.001). The absolute difference in incidence rates of dementia per 1000 person years comparing favourable versus unfavourable lifestyle was - 0.65 (95% CI - 1.02 to - 0.27) among participants with no CMDs and - 5.64 (- 8.11 to - 3.17) among participants with ≥ 3 CMDs, corresponding to a HR of 0.71 (0.58-0.88) and 0.42 (0.28-0.63), respectively. In the imaging subsample, a favourable lifestyle was associated with larger total brain, grey matter, and hippocampus volumes across CMD status. CONCLUSION Our findings suggest that adherence to a healthy lifestyle might substantially attenuate dementia risk and adverse brain structural changes associated with cardiometabolic multimorbidity.
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Affiliation(s)
- Sizheng Xiong
- Department of Vascular Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Ningxin Hou
- Division of Cardiovascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feifei Tang
- Department of Cardiovascular Surgery, Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Li
- Division of Cardiovascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongping Deng
- Department of Vascular Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
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Vermunt L, Sutphen C, Dicks E, de Leeuw DM, Allegri R, Berman SB, Cash DM, Chhatwal JP, Cruchaga C, Day G, Ewers M, Farlow M, Fox NC, Ghetti B, Graff-Radford N, Hassenstab J, Jucker M, Karch CM, Kuhle J, Laske C, Levin J, Masters CL, McDade E, Mori H, Morris JC, Perrin RJ, Preische O, Schofield PR, Suárez-Calvet M, Xiong C, Scheltens P, Teunissen CE, Visser PJ, Bateman RJ, Benzinger TLS, Fagan AM, Gordon BA, Tijms BM. Axonal damage and astrocytosis are biological correlates of grey matter network integrity loss: a cohort study in autosomal dominant Alzheimer disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.21.23287468. [PMID: 37016671 PMCID: PMC10071836 DOI: 10.1101/2023.03.21.23287468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Brain development and maturation leads to grey matter networks that can be measured using magnetic resonance imaging. Network integrity is an indicator of information processing capacity which declines in neurodegenerative disorders such as Alzheimer disease (AD). The biological mechanisms causing this loss of network integrity remain unknown. Cerebrospinal fluid (CSF) protein biomarkers are available for studying diverse pathological mechanisms in humans and can provide insight into decline. We investigated the relationships between 10 CSF proteins and network integrity in mutation carriers (N=219) and noncarriers (N=136) of the Dominantly Inherited Alzheimer Network Observational study. Abnormalities in Aβ, Tau, synaptic (SNAP-25, neurogranin) and neuronal calcium-sensor protein (VILIP-1) preceded grey matter network disruptions by several years, while inflammation related (YKL-40) and axonal injury (NfL) abnormalities co-occurred and correlated with network integrity. This suggests that axonal loss and inflammation play a role in structural grey matter network changes. Key points Abnormal levels of fluid markers for neuronal damage and inflammatory processes in CSF are associated with grey matter network disruptions.The strongest association was with NfL, suggesting that axonal loss may contribute to disrupted network organization as observed in AD.Tracking biomarker trajectories over the disease course, changes in CSF biomarkers generally precede changes in brain networks by several years.
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Shang X, Zhang X, Huang Y, Zhu Z, Zhang X, Liu J, Wang W, Tang S, Yu H, Ge Z, Yang X, He M. Association of a wide range of individual chronic diseases and their multimorbidity with brain volumes in the UK Biobank: A cross-sectional study. EClinicalMedicine 2022; 47:101413. [PMID: 35518119 PMCID: PMC9065617 DOI: 10.1016/j.eclinm.2022.101413] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/27/2022] [Accepted: 04/05/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Little is known regarding associations of conventional and emerging diseases and their multimorbidity with brain volumes. METHODS This cross-sectional study included 36,647 European ancestry individuals aged 44-81 years with brain magnetic resonance imaging data from UK Biobank. Brain volumes were measured between 02 May 2014 and 31 October 2019. General linear regression models were used to associate 57 individual major diseases with brain volumes. Latent class analysis was used to identify multimorbidity patterns. A multimorbidity score for brain volumes was computed based on the estimates for individual groups of diseases. FINDINGS Out of 57 major diseases, 16 were associated with smaller volumes of total brain, 14 with smaller volumes of grey matter, and six with smaller hippocampus volumes, and four major diseases were associated with higher white matter hyperintensity (WMH) load after adjustment for all other diseases. The leading contributors to the variance of total brain volume were hypertension (R2=0·0229), dyslipidemia (0·0190), cataract (0·0176), coronary heart disease (0·0107), and diabetes (0·0077). We identified six major multimorbidity patterns and multimorbidity patterns of cardiometabolic disorders (CMD), and CMD-multiple disorders, and metabolic disorders were independently associated with smaller volumes of total brain (β (95% CI): -6·6 (-8·9, -4·3) ml, -7·3 (-10·4, -4·1) ml, and -10·4 (-13·5, -7·3) ml, respectively), grey matter (-7·1 (-8·5, -5·7) ml, -9·0 (-10·9, -7·1) ml, and -11·8 (-13·6, -9·9) ml, respectively), and higher WMH load (0·23 (0·19, 0·27), 0·25 (0·19, 0·30), and 0·33 (0·27, 0·39), respectively) after adjustment for geographic, socioeconomic, and lifestyle factors (all P-values<0·0001). The percentage of the variance of total brain volume explained by multimorbidity patterns, multimorbidity defined by the number of diseases, and multimorbidity score was 1·2%, 3·1%, and 7·2%, respectively. Associations between CMD-multiple disorders pattern, and metabolic disorders pattern and volumes of total brain, grey matter, and WMH were stronger in men than in women. Associations between multimorbidity and brain volumes were stronger in younger than in older individuals. INTERPRETATION Besides conventional diseases, we found an association between numerous emerging diseases and smaller brain volumes. CMD-related multimorbidity patterns are associated with smaller brain volumes. Men or younger adults with multimorbidity are more in need of care for promoting brain health. These findings are from an association study and will need confirmation. FUNDING The Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075), Science and Technology Program of Guangzhou, China (202,002,020,049).
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Key Words
- AD, Alzheimer’s disease
- APOE4, Apolipoprotein E ε4
- BMI, body mass index
- Brain volume
- CHD, coronary heart disease
- CI, confidence interval
- CKD, chronic kidney disease
- CMD, cardiometabolic disorders
- COPD, chronic obstructive pulmonary disease
- CVD, cardiovascular disease
- FDR, false discovery rate
- Grey matter
- Hippocampus
- Major diseases
- Moderation analysis
- Multimorbidity
- OLS, ordinary least squares
- WMH, white matter hyperintensity
- White matter hyperintensity
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Affiliation(s)
- Xianwen Shang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Centre for Eye Research Australia, The University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC 3002, Australia
- Corresponding authors at: Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China.
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
| | - Yu Huang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Centre for Eye Research Australia, The University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC 3002, Australia
| | - Xiayin Zhang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jiahao Liu
- Centre for Eye Research Australia, The University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC 3002, Australia
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, China
| | - Shulin Tang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
| | - Zongyuan Ge
- Monash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne, VIC 3800, Australia
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
| | - Mingguang He
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China
- Centre for Eye Research Australia, The University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC 3002, Australia
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, China
- Corresponding authors at: Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong 510080, China.
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5
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Luigi L, Silvia I, Viktor W, Wink AM, Mutsaerts HJ, Sven H, Kaj B, O'Brien JT, Giovanni FB, Gael C, Pierre P, Pablo ML, Adam W, Joanna W, Craig R, Gispert JD, Visser PJ, Philip S, Frederik B, Tijms BM. Gray matter network properties show distinct associations with CSF p-tau 181 levels and amyloid status in individuals without dementia. AGING BRAIN 2022; 2:100054. [PMID: 36908898 PMCID: PMC9997148 DOI: 10.1016/j.nbas.2022.100054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 09/21/2022] [Accepted: 10/06/2022] [Indexed: 11/05/2022] Open
Abstract
Gray matter networks are altered with amyloid accumulation in the earliest stage of AD, and are associated with decline throughout the AD spectrum. It remains unclear to what extent gray matter network abnormalities are associated with hyperphosphorylated-tau (p-tau). We studied the relationship of cerebrospinal fluid (CSF) p-tau181 with gray matter networks in non-demented participants from the European Prevention of Alzheimer's Dementia (EPAD) cohort, and studied dependencies on amyloid and cognitive status. Gray matter networks were extracted from baseline structural 3D T1w MRI. P-tau181 and abeta were measured with the Roche cobas Elecsys System. We studied the associations of CSF biomarkers levels with several network's graph properties. We further studied whether the relationships of p-tau 181 and network measures were dependent on amyloid status and cognitive stage (CDR). We repeated these analyses for network properties at a regional level, where we averaged local network values across cubes within each of 116 areas as defined by the automated anatomical labeling (AAL) atlas. Amyloid positivity was associated with higher network size and betweenness centrality, and lower gamma, clustering and small-world coefficients. Higher CSF p-tau 181 levels were related to lower betweenness centrality, path length and lambda coefficients (all p < 0.01). Three-way interactions between p-tau181, amyloid status and CDR were found for path length, lambda and clustering (all p < 0.05): Cognitively unimpaired amyloid-negative participants showed lower path length and lambda values with higher CSF p-tau181 levels. Amyloid-positive participants with impaired cognition demonstrated lower clustering coefficients in association to higher CSF p-tau181 levels. Our results suggest that alterations in gray matter network clustering coefficient is an early and specific event in AD.
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Affiliation(s)
- Lorenzini Luigi
- Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Ingala Silvia
- Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Wottschel Viktor
- Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Alle Meije Wink
- Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Henk Jmm Mutsaerts
- Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Haller Sven
- CIMC - Centre d'Imagerie Médicale de Cornavin, Place de Cornavin 18, 1201 Genève, Switzerland.,Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, PR China
| | - Blennow Kaj
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - John T O'Brien
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge School of Clinical Medicine, Box 189, Cambridge CB2 0QQ, UK
| | - Frisoni B Giovanni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,University Hospitals and University of Geneva, Geneva, Switzerland
| | - Chételat Gael
- Université de Normandie, Unicaen, Inserm, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood-and-Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
| | - Payoux Pierre
- Department of Nuclear Medicine, Toulouse CHU, Purpan University Hospital, Toulouse, France.,Toulouse NeuroImaging Center, University of Toulouse, INSERM, UPS, Toulouse, France
| | - Martinez-Lage Pablo
- Centro de Investigación y Terapias Avanzadas, Neurología, CITA-Alzheimer Foundation, San Sebastián, Spain
| | - Waldman Adam
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.,Department of Brain Sciences, Imperial College London, London, UK
| | - Wardlaw Joanna
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute Centre at the University of Edinburgh, University of Edinburgh, UK
| | - Ritchie Craig
- Centre for Dementia Prevention, The University of Edinburgh, Scotland, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,CIBER Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Pieter Jelle Visser
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Alzheimer Center Limburg, Department of Psychiatry & Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.,Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Scheltens Philip
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Barkhof Frederik
- Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, London, UK
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6
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Drenthen GS, Backes WH, Freeze WM, Jacobs HI, Verheggen IC, van Boxtel MP, Hoff EI, Verhey FR, Jansen JF. Rich-Club Connectivity of the Structural Covariance Network Relates to Memory Processes in Mild Cognitive Impairment and Alzheimer's Disease. J Alzheimers Dis 2022; 89:209-217. [PMID: 35871335 PMCID: PMC9484119 DOI: 10.3233/jad-220175] [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] [Accepted: 06/13/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN). OBJECTIVE To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club. METHODS To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression. RESULTS Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume. CONCLUSION Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy.
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Affiliation(s)
- Gerhard S. Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Whitney M. Freeze
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Heidi I.L. Jacobs
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Gordon Center for Medical Imaging Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Inge C.M. Verheggen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Martin P.J. van Boxtel
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Erik I. Hoff
- Department of Neurology, Zuyderland Medical Centre Heerlen, Heerlen, the Netherlands
| | - Frans R. Verhey
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Jacobus F.A. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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7
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Pelkmans W, Ossenkoppele R, Dicks E, Strandberg O, Barkhof F, Tijms BM, Pereira JB, Hansson O. Tau-related grey matter network breakdown across the Alzheimer's disease continuum. Alzheimers Res Ther 2021; 13:138. [PMID: 34389066 PMCID: PMC8364121 DOI: 10.1186/s13195-021-00876-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 07/09/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND Changes in grey matter covariance networks have been reported in preclinical and clinical stages of Alzheimer's disease (AD) and have been associated with amyloid-β (Aβ) deposition and cognitive decline. However, the role of tau pathology on grey matter networks remains unclear. Based on previously reported associations between tau pathology, synaptic density and brain structural measures, tau-related connectivity changes across different stages of AD might be expected. We aimed to assess the relationship between tau aggregation and grey matter network alterations across the AD continuum. METHODS We included 533 individuals (178 Aβ-negative cognitively unimpaired (CU) subjects, 105 Aβ-positive CU subjects, 122 Aβ-positive patients with mild cognitive impairment, and 128 patients with AD dementia) from the BioFINDER-2 study. Single-subject grey matter networks were extracted from T1-weighted images and graph theory properties including degree, clustering coefficient, path length, and small world topology were calculated. Associations between tau positron emission tomography (PET) values and global and regional network measures were examined using linear regression models adjusted for age, sex, and total intracranial volume. Finally, we tested whether the association of tau pathology with cognitive performance was mediated by grey matter network disruptions. RESULTS Across the whole sample, we found that higher tau load in the temporal meta-ROI was associated with significant changes in degree, clustering, path length, and small world values (all p < 0.001), indicative of a less optimal network organisation. Already in CU Aβ-positive individuals associations between tau burden and lower clustering and path length were observed, whereas in advanced disease stages elevated tau pathology was progressively associated with more brain network abnormalities. Moreover, the association between higher tau load and lower cognitive performance was only partly mediated (9.3 to 9.5%) through small world topology. CONCLUSIONS Our data suggest a close relationship between grey matter network disruptions and tau pathology in individuals with abnormal amyloid. This might reflect a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions with tau pathology, indicative of a more random network topology across different AD stages.
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Affiliation(s)
- Wiesje Pelkmans
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ellen Dicks
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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Buckley RF. Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease. Neurotherapeutics 2021; 18:709-727. [PMID: 33782864 PMCID: PMC8423933 DOI: 10.1007/s13311-021-01026-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/25/2022] Open
Abstract
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying treatment in clinical trials. With recent neuroimaging advances, along with the burgeoning availability of longitudinal neuroimaging data and big-data harmonization approaches, a more comprehensive evaluation of the disease has shed light on the topographical staging and temporal sequencing of the disease. Multimodal imaging approaches have also promoted the development of data-driven models of AD-associated pathological propagation of tau proteinopathies. Studies of autosomal dominant, early sporadic, and late sporadic courses of the disease have shed unique insights into the AD pathological cascade, particularly with regard to genetic vulnerabilities and the identification of potential drug targets. Further, neuroimaging markers of b-amyloid, tau, and neurodegeneration have provided a powerful tool for validation of novel fluid cerebrospinal and plasma markers. This review highlights some of the latest advances in the field of human neuroimaging in AD across these topics, particularly with respect to positron emission tomography and structural and functional magnetic resonance imaging.
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Affiliation(s)
- Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital & Brigham and Women's, Harvard Medical School, Boston, MA, USA.
- Melbourne School of Psychological Sciences and Florey Institutes, University of Melbourne, Melbourne, VIC, Australia.
- Department of Neurology, Massachusetts General Hospital, 149 13th St, Charlestown, MA, 02129, USA.
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