1
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Murray-Smith H, Barker S, Barkhof F, Barnes J, Brown TM, Captur G, R E Cartlidge M, Cash DM, Coath W, Davis D, Dickson JC, Groves J, Hughes AD, James SN, Keshavan A, Keuss SE, King-Robson J, Lu K, Malone IB, Nicholas JM, Rapala A, Scott CJ, Street R, Sudre CH, Thomas DL, Wong A, Wray S, Zetterberg H, Chaturvedi N, Fox NC, Crutch SJ, Richards M, Schott JM. Updating the study protocol: Insight 46 - a longitudinal neuroscience sub-study of the MRC National Survey of Health and Development - phases 2 and 3. BMC Neurol 2024; 24:40. [PMID: 38263061 PMCID: PMC10804658 DOI: 10.1186/s12883-023-03465-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/13/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Although age is the biggest known risk factor for dementia, there remains uncertainty about other factors over the life course that contribute to a person's risk for cognitive decline later in life. Furthermore, the pathological processes leading to dementia are not fully understood. The main goals of Insight 46-a multi-phase longitudinal observational study-are to collect detailed cognitive, neurological, physical, cardiovascular, and sensory data; to combine those data with genetic and life-course information collected from the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort); and thereby contribute to a better understanding of healthy ageing and dementia. METHODS/DESIGN Phase 1 of Insight 46 (2015-2018) involved the recruitment of 502 members of the NSHD (median age = 70.7 years; 49% female) and has been described in detail by Lane and Parker et al. 2017. The present paper describes phase 2 (2018-2021) and phase 3 (2021-ongoing). Of the 502 phase 1 study members who were invited to a phase 2 research visit, 413 were willing to return for a clinic visit in London and 29 participated in a remote research assessment due to COVID-19 restrictions. Phase 3 aims to recruit 250 study members who previously participated in both phases 1 and 2 of Insight 46 (providing a third data time point) and 500 additional members of the NSHD who have not previously participated in Insight 46. DISCUSSION The NSHD is the oldest and longest continuously running British birth cohort. Members of the NSHD are now at a critical point in their lives for us to investigate successful ageing and key age-related brain morbidities. Data collected from Insight 46 have the potential to greatly contribute to and impact the field of healthy ageing and dementia by combining unique life course data with longitudinal multiparametric clinical, imaging, and biomarker measurements. Further protocol enhancements are planned, including in-home sleep measurements and the engagement of participants through remote online cognitive testing. Data collected are and will continue to be made available to the scientific community.
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Affiliation(s)
- Heidi Murray-Smith
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK.
| | - Suzie Barker
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
- Centre for Medical Image Computing, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Thomas M Brown
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Gabriella Captur
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Molly R E Cartlidge
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - David M Cash
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - William Coath
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Daniel Davis
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - John C Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - James Groves
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Josh King-Robson
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Kirsty Lu
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Ian B Malone
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Jennifer M Nicholas
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Alicja Rapala
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Catherine J Scott
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Rebecca Street
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Carole H Sudre
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Centre for Medical Image Computing, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - David L Thomas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Selina Wray
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute, University College London, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Hong, Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
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2
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Kim H, Zhu X, Zhao Y, Bell SA, Gehrman PR, Cohen D, Devanand DP, Goldberg TE, Lee S. Resting-state functional connectivity changes in older adults with sleep disturbance and the role of amyloid burden. Mol Psychiatry 2023; 28:4399-4406. [PMID: 37596355 PMCID: PMC10842478 DOI: 10.1038/s41380-023-02214-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 07/19/2023] [Accepted: 08/02/2023] [Indexed: 08/20/2023]
Abstract
Sleep and related disorders could lead to changes in various brain networks, but little is known about the role of amyloid β (Aβ) burden-a key Alzheimer's disease (AD) biomarker-in the relationship between sleep disturbance and altered resting state functional connectivity (rsFC) in older adults. This cross-sectional study examined the association between sleep disturbance, Aβ burden, and rsFC using a large-scale dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sample included 489 individuals (53.6% cognitively normal, 32.5% mild cognitive impairment, and 13.9% AD) who had completed sleep measures (Neuropsychiatric Inventory), PET Aβ data, and resting-state fMRI scans at baseline. Within and between rsFC of the Salience (SN), the Default Mode (DMN) and the Frontal Parietal network (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance was evaluated using the linear regressions, controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension. Although no significant main effect of sleep disturbance was found on rsFC, a significant interaction term emerged between sleep disturbance and Aβ burden on rsFC of SN (β = 0.11, P = 0.006). Specifically, sleep disturbance was associated with SN hyperconnectivity, only with the presence of Aβ burden. Sleep disturbance may lead to altered connectivity in the SN when Aβ is accumulated in the brain. Individuals with AD pathology may be at increased risk for sleep-related aberrant rsFC; therefore, identifying and treating sleep problems in these individuals may help prevent further disease progression.
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Affiliation(s)
- Hyun Kim
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA.
| | - Xi Zhu
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Division of Anxiety, Mood, Eating, and Related Disorders, New York State Psychiatric Institute, New York, NY, USA
| | - Yiming Zhao
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Sophie A Bell
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Philip R Gehrman
- Department of Psychiatry, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
- Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Daniel Cohen
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
| | - D P Devanand
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Terry E Goldberg
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Anesthesiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
- Division of Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
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3
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Sergio JP, Kundu R, McIntosh RC, Palmero M, Hegde RR, de Dios MA, Clark US. Synergistic effects of high early-life stress exposure and HIV infection on reaction time variability. Front Psychol 2023; 14:1096266. [PMID: 37139000 PMCID: PMC10150076 DOI: 10.3389/fpsyg.2023.1096266] [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: 11/11/2022] [Accepted: 03/29/2023] [Indexed: 05/05/2023] Open
Abstract
Addressing comorbidities contributing to cognitive impairment in people living with HIV (PLWH) remains imperative. Prior studies utilizing reaction time intra-individual variability (RT-IIV), a robust behavioral marker of cognitive dysfunction, demonstrate increased cognitive impairment in adults living with HIV who have high early life stress (ELS) exposure relative to those with low-ELS exposure. Yet, it is unknown whether RT-IIV elevations are due to high-ELS alone or both HIV-status and high-ELS. In the current study, we explore the potential additive effects of HIV and high-ELS exposure on RT-IIV to better characterize the independent and combined effects of these factors on RT-IIV among PLWH. We assessed 59 PLWH and 69 HIV-negative healthy control (HC) participants with either low or high ELS on RT-IIV during a working memory task (1-back). We observed a significant interaction between HIV status and ELS exposure on RT-IIV, PLWH who had experienced high ELS demonstrating RT-IIV elevations relative to all other groups. In addition, RT-IIV was significantly associated with ELS exposure in PLWH, but not in the HC group. We also observed associations between RT-IIV and measures of HIV-disease severity (plasma HIV viral load, nadir CD4) among PLWH. Taken as a whole, these findings provide novel evidence of the combined effects of HIV and high-ELS exposure on RT-IIV, and thus suggest HIV-related and ELS-related neural abnormalities may act in an additive or synergistic manner to affect cognition. Such data warrant further investigation into the neurobiological mechanisms associated with HIV and high-ELS exposure that contribute to increased neurocognitive dysfunction among PLWH.
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Affiliation(s)
- Jordan P. Sergio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Fordham University, New York, NY, United States
| | - Retina Kundu
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Roger C. McIntosh
- Department of Psychology, University of Miami, Miami, FL, United States
| | - Mabel Palmero
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Rachal R. Hegde
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Marcel A. de Dios
- Department of Psychological, Health, and Learning Sciences, University of Houston, Houston, TX, United States
| | - Uraina S. Clark
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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4
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Kim H, Zhu X, Zhao Y, Bell S, Gehrman P, Cohen D, Devanand D, Goldberg T, Lee S. Resting-State Functional Connectivity Changes in Older Adults with Sleep Disturbance and the Role of Amyloid Burden. RESEARCH SQUARE 2023:rs.3.rs-2547880. [PMID: 36798352 PMCID: PMC9934741 DOI: 10.21203/rs.3.rs-2547880/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Sleep and related disorders could lead to changes in various brain networks, but little is known about the role of amyloid β (Aβ) burden-a key Alzheimer's disease (AD) biomarker-in the relationship between sleep disturbance and altered resting state functional connectivity (rsFC) in older adults. This cross-sectional study examined the association between sleep disturbance, Aβ burden, and rsFC using a large-scale dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sample included 489 individuals (53.6% cognitively normal, 32.5% mild cognitive impairment, and 13.9% AD) who had completed sleep measures (Neuropsychiatric Inventory), PET Aβ data, and resting-state fMRI scans at baseline. Within and between rsFC of the Salience (SN), the Default Mode (DMN) and the Frontal Parietal network (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance was evaluated using linear regressions, controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension. Although no significant main effect of sleep disturbance was found on rsFC, a significant interaction term emerged between sleep disturbance and Aβ burden on rsFC of SN (β=0.11, P=0.006). Specifically, sleep disturbance was associated with SN hyperconnectivity, only with the presence of Aβ burden. Sleep disturbance may lead to altered connectivity in the SN when Aβ is accumulated in the brain. Individuals with AD pathology may be at increased risk for sleep-related aberrant rsFC; therefore, identifying and treating sleep problems in these individuals may help prevent further disease progression.
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Affiliation(s)
| | - Xi Zhu
- Columbia University Medical Center
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5
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Meneghini V, Barbosa AR, Lourenço CLM, Borgatto AF. Effects of Exergaming and Resistance Training on Reaction time and Intraindividual Variability in Older Adults: a Randomized Clinical Trial. AGEING INTERNATIONAL 2022. [DOI: 10.1007/s12126-022-09491-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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6
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Lu K, Nicholas JM, Pertzov Y, Grogan J, Husain M, Pavisic IM, James SN, Parker TD, Lane CA, Keshavan A, Keuss SE, Buchanan SM, Murray-Smith H, Cash DM, Malone IB, Sudre CH, Coath W, Wong A, Henley SM, Fox NC, Richards M, Schott JM, Crutch SJ. Dissociable effects of APOE-ε4 and β-amyloid pathology on visual working memory. NATURE AGING 2021; 1:1002-1009. [PMID: 34806027 PMCID: PMC7612005 DOI: 10.1038/s43587-021-00117-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 08/17/2021] [Indexed: 01/21/2023]
Abstract
Although APOE-ε4 carriers are at significantly higher risk of developing Alzheimer's disease than non-carriers1, controversial evidence suggests that APOE-ε4 might confer some advantages, explaining the survival of this gene (antagonistic pleiotropy)2,3. In a population-based cohort born in one week in 1946 (assessed aged 69-71), we assessed differential effects of APOE-ε4 and β-amyloid pathology (quantified using 18F-Florbetapir-PET) on visual working memory (object-location binding). In 398 cognitively normal participants, APOE-ε4 and β-amyloid had opposing effects on object identification, predicting better and poorer recall respectively. ε4-carriers also recalled locations more precisely, with a greater advantage at higher β-amyloid burden. These results provide evidence of superior visual working memory in ε4-carriers, showing that some benefits of this genotype are demonstrable in older age, even in the preclinical stages of Alzheimer's disease.
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Affiliation(s)
- Kirsty Lu
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jennifer M. Nicholas
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Yoni Pertzov
- Department of Psychology, The Hebrew University of Jerusalem, Israel
| | - John Grogan
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Masud Husain
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Department of Experimental Psychology, University of Oxford, UK
| | - Ivanna M. Pavisic
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Thomas D. Parker
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christopher A. Lane
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E. Keuss
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah M. Buchanan
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David M. Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - William Coath
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Susie M.D. Henley
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick C. Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Jonathan M. Schott
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sebastian J. Crutch
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
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Chen APF, Clouston SAP, Kritikos M, Richmond L, Meliker J, Mann F, Santiago-Michels S, Pellecchia AC, Carr MA, Kuan PF, Bromet EJ, Luft BJ. A deep learning approach for monitoring parietal-dominant Alzheimer's disease in World Trade Center responders at midlife. Brain Commun 2021; 3:fcab145. [PMID: 34396105 PMCID: PMC8361422 DOI: 10.1093/braincomms/fcab145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/04/2021] [Accepted: 04/12/2021] [Indexed: 01/21/2023] Open
Abstract
Little is known about the characteristics and causes of early-onset cognitive impairment. Responders to the 2001 New York World Trade Center disaster represent an ageing population that was recently shown to have an excess prevalence of cognitive impairment. Neuroimaging and molecular data demonstrate that a subgroup of affected responders may have a unique form of parietal-dominant Alzheimer's Disease. Recent neuropsychological testing and artificial intelligence approaches have emerged as methods that can be used to identify and monitor subtypes of cognitive impairment. We utilized data from World Trade Center responders participating in a health monitoring program and applied a deep learning approach to evaluate neuropsychological and neuroimaging data to generate a cortical atrophy risk score. We examined risk factors associated with the prevalence and incidence of high risk for brain atrophy in responders who are now at midlife. Training was conducted in a randomly selected two-thirds sample (N = 99) enrolled using of the results of a structural neuroimaging study. Testing accuracy was estimated for each training cycle in the remaining third subsample. After training was completed, the scoring methodology that was generated was applied to longitudinal data from 1441 World Trade Center responders. The artificial neural network provided accurate classifications of these responders in both the testing (Area Under the Receiver Operating Curve, 0.91) and validation samples (Area Under the Receiver Operating Curve, 0.87). At baseline and follow-up, responders identified as having a high risk of atrophy (n = 378) showed poorer cognitive functioning, most notably in domains that included memory, throughput, and variability as compared to their counterparts at low risk for atrophy (n = 1063). Factors associated with atrophy risk included older age [adjusted hazard ratio, 1.045 (95% confidence interval = 1.027-1.065)], increased duration of exposure at the WTC site [adjusted hazard ratio, 2.815 (1.781-4.449)], and a higher prevalence of post-traumatic stress disorder [aHR, 2.072 (1.408-3.050)]. High atrophy risk was associated with an increased risk of all-cause mortality [adjusted risk ratio, 3.19 (1.13-9.00)]. In sum, the high atrophy risk group displayed higher levels of previously identified risk factors and characteristics of cognitive impairment, including advanced age, symptoms of post-traumatic stress disorder, and prolonged duration of exposure to particulate matter. Thus, this study suggests that a high risk of brain atrophy may be accurately monitored using cognitive data.
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Affiliation(s)
- Allen P F Chen
- Medical Scientist Training Program, Department of Neurobiology and Behavior, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794, USA
| | - Sean A P Clouston
- Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11794, USA
- Program in Public Health, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11794, USA
| | - Minos Kritikos
- Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11794, USA
- Program in Public Health, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11794, USA
| | - Lauren Richmond
- Department of Psychology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Jaymie Meliker
- Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11794, USA
- Program in Public Health, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11794, USA
| | - Frank Mann
- Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11794, USA
- Program in Public Health, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11794, USA
| | - Stephanie Santiago-Michels
- Stony Brook World Trade Center Wellness Program, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11725, USA
| | - Alison C Pellecchia
- Stony Brook World Trade Center Wellness Program, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11725, USA
| | - Melissa A Carr
- Stony Brook World Trade Center Wellness Program, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11725, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Evelyn J Bromet
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794, USA
| | - Benjamin J Luft
- Stony Brook World Trade Center Wellness Program, Renaissance School of Medicine at Stony, Brook University, Stony Brook, NY 11725, USA
- Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794, USA
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8
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Lu K, Nicholas JM, Weston PSJ, Stout JC, O’Regan AM, James SN, Buchanan SM, Lane CA, Parker TD, Keuss SE, Keshavan A, Murray-Smith H, Cash DM, Sudre CH, Malone IB, Coath W, Wong A, Richards M, Henley SMD, Fox NC, Schott JM, Crutch SJ. Visuomotor integration deficits are common to familial and sporadic preclinical Alzheimer's disease. Brain Commun 2021; 3:fcab003. [PMID: 33615219 PMCID: PMC7882207 DOI: 10.1093/braincomms/fcab003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 12/04/2020] [Accepted: 12/08/2020] [Indexed: 11/26/2022] Open
Abstract
We investigated whether subtle visuomotor deficits were detectable in familial and sporadic preclinical Alzheimer's disease. A circle-tracing task-with direct and indirect visual feedback, and dual-task subtraction-was completed by 31 individuals at 50% risk of familial Alzheimer's disease (19 presymptomatic mutation carriers; 12 non-carriers) and 390 cognitively normal older adults (members of the British 1946 Birth Cohort, all born during the same week; age range at assessment = 69-71 years), who also underwent β-amyloid-PET/MRI to derive amyloid status (positive/negative), whole-brain volume and white matter hyperintensity volume. We compared preclinical Alzheimer's groups against controls cross-sectionally (mutation carriers versus non-carriers; amyloid-positive versus amyloid-negative) on speed and accuracy of circle-tracing and subtraction. Mutation carriers (mean 7 years before expected onset) and amyloid-positive older adults traced disproportionately less accurately than controls when visual feedback was indirect, and were slower at dual-task subtraction. In the older adults, the same pattern of associations was found when considering amyloid burden as a continuous variable (Standardized Uptake Value Ratio). The effect of amyloid was independent of white matter hyperintensity and brain volumes, which themselves were associated with different aspects of performance: greater white matter hyperintensity volume was also associated with disproportionately poorer tracing accuracy when visual feedback was indirect, whereas larger brain volume was associated with faster tracing and faster subtraction. Mutation carriers also showed evidence of poorer tracing accuracy when visual feedback was direct. This study provides the first evidence of visuomotor integration deficits common to familial and sporadic preclinical Alzheimer's disease, which may precede the onset of clinical symptoms by several years.
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Affiliation(s)
- Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Jennifer M Nicholas
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Philip S J Weston
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Julie C Stout
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Alison M O’Regan
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 7HB, UK
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- UK Dementia Research Institute at University College London, London, UK
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 7HB, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EU, UK
- Department of Medical Physics, University College London, London, WC1E 7JE, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 7HB, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 7HB, UK
| | - Susie M D Henley
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- UK Dementia Research Institute at University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
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