1
|
Jensen DE, Ebmeier KP, Akbaraly T, Jansen MG, Singh-Manoux A, Kivimäki M, Zsoldos E, Klein-Flügge MC, Suri S. The association of longitudinal diet and waist-to-hip ratio from midlife to old age with hippocampus connectivity and memory in old age: a cohort study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.570778. [PMID: 38168259 PMCID: PMC10760001 DOI: 10.1101/2023.12.12.570778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Epidemiological studies suggest lifestyle factors may reduce the risk of dementia. However, few studies have examined the association of diet and waist-to-hip ratio with hippocampus connectivity. In the Whitehall II Imaging Sub-study, we examined longitudinal changes in diet quality in 512 participants and waist-to-hip ratio in 665 participants. Diet quality was measured using the Alternative Health Eating Index-2010 assessed three times across 11 years between ages 48 and 60 years, and waist-to-hip ratio five times over 21 years between ages 48 and 68 years. Brain imaging and cognitive tests were performed at age 70±5 years. We measured white matter using diffusion tensor imaging and hippocampal functional connectivity using resting-state functional magnetic resonance imaging. In addition to associations of diet and waist-to-hip ratio with brain imaging measures, we tested whether associations between diet, waist-to-hip ratio and cognitive performance were mediated by brain connectivity. We found better diet quality in midlife and improvements in diet over mid-to-late life were associated with higher hippocampal functional connectivity to the occipital lobe and cerebellum, and better white matter integrity as measured by higher fractional anisotropy and lower diffusivity. Higher waist-to-hip ratio in midlife was associated with higher mean and radial diffusivity and lower fractional anisotropy in several tracts including the inferior longitudinal fasciculus and cingulum. Associations between midlife waist-to-hip ratio, working memory and executive function were partially mediated by radial diffusivity. All associations were independent of age, sex, education, and physical activity. Our findings highlight the importance of maintaining a good diet and a healthy waist-to-hip ratio in midlife to maintain brain health in later life. Future interventional studies for the improvement of dietary and metabolic health should target midlife for the prevention of cognitive decline in old age.
Collapse
|
2
|
Franco-O´Byrne D, Gonzalez-Gomez R, Morales Sepúlveda JP, Vergara M, Ibañez A, Huepe D. The impact of loneliness and social adaptation on depressive symptoms: Behavioral and brain measures evidence from a brain health perspective. Front Psychol 2023; 14:1096178. [PMID: 37077845 PMCID: PMC10108715 DOI: 10.3389/fpsyg.2023.1096178] [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: 11/11/2022] [Accepted: 02/21/2023] [Indexed: 03/16/2023] Open
Abstract
Introduction Early detection of depression is a cost-effective way to prevent adverse outcomes on brain physiology, cognition, and health. Here we propose that loneliness and social adaptation are key factors that can anticipate depressive symptoms. Methods We analyzed data from two separate samples to evaluate the associations between loneliness, social adaptation, depressive symptoms, and their neural correlates. Results For both samples, hierarchical regression models on self-reported data showed that loneliness and social adaptation have negative and positive effects on depressive symptoms. Moreover, social adaptation reduces the impact of loneliness on depressive symptoms. Structural connectivity analysis showed that depressive symptoms, loneliness, and social adaptation share a common neural substrate. Furthermore, functional connectivity analysis demonstrated that only social adaptation was associated with connectivity in parietal areas. Discussion Altogether, our results suggest that loneliness is a strong risk factor for depressive symptoms while social adaptation acts as a buffer against the ill effects of loneliness. At the neuroanatomical level, loneliness and depression may affect the integrity of white matter structures known to be associated to emotion dysregulation and cognitive impairment. On the other hand, socio-adaptive processes may protect against the harmful effects of loneliness and depression. Structural and functional correlates of social adaptation could indicate a protective role through long and short-term effects, respectively. These findings may aid approaches to preserve brain health via social participation and adaptive social behavior.
Collapse
Affiliation(s)
- Daniel Franco-O´Byrne
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Raul Gonzalez-Gomez
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Juan Pablo Morales Sepúlveda
- Pontificia Universidad Católica de Chile Programa de Doctorado en Neurociencias Centro Interdisciplinario de Neurocienciass, Santiago, Chile
- Facultad de Educación Psicología y Familia, Universidad Finis Terrae, Santiago, Chile
| | - Mayte Vergara
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Agustin Ibañez
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| |
Collapse
|
3
|
Zaretskaya N, Fink E, Arsenovic A, Ischebeck A. Fast and functionally specific cortical thickness changes induced by visual stimulation. Cereb Cortex 2023; 33:2823-2837. [PMID: 35780393 DOI: 10.1093/cercor/bhac244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
Structural characteristics of the human brain serve as important markers of brain development, aging, disease progression, and neural plasticity. They are considered stable properties, changing slowly over time. Multiple recent studies reported that structural brain changes measured with magnetic resonance imaging (MRI) may occur much faster than previously thought, within hours or even minutes. The mechanisms behind such fast changes remain unclear, with hemodynamics as one possible explanation. Here we investigated the functional specificity of cortical thickness changes induced by a flickering checkerboard and compared them to blood oxygenation level-dependent (BOLD) functional MRI activity. We found that checkerboard stimulation led to a significant thickness increase, which was driven by an expansion at the gray-white matter boundary, functionally specific to V1, confined to the retinotopic representation of the checkerboard stimulus, and amounted to 1.3% or 0.022 mm. Although functional specificity and the effect size of these changes were comparable to those of the BOLD signal in V1, thickness effects were substantially weaker in V3. Furthermore, a comparison of predicted and measured thickness changes for different stimulus timings suggested a slow increase of thickness over time, speaking against a hemodynamic explanation. Altogether, our findings suggest that visual stimulation can induce structural gray matter enlargement measurable with MRI.
Collapse
Affiliation(s)
- Natalia Zaretskaya
- Department of Cognitive Psychology and Neuroscience, Institute of Psychology, University of Graz, Universitaetsplatz 2, 8010 Graz, Austria
- BioTechMed-Graz, Mozartgasse 12, 8010 Graz, Austria
| | - Erik Fink
- Department of Cognitive Psychology and Neuroscience, Institute of Psychology, University of Graz, Universitaetsplatz 2, 8010 Graz, Austria
| | - Ana Arsenovic
- Department of Cognitive Psychology and Neuroscience, Institute of Psychology, University of Graz, Universitaetsplatz 2, 8010 Graz, Austria
- BioTechMed-Graz, Mozartgasse 12, 8010 Graz, Austria
| | - Anja Ischebeck
- Department of Cognitive Psychology and Neuroscience, Institute of Psychology, University of Graz, Universitaetsplatz 2, 8010 Graz, Austria
- BioTechMed-Graz, Mozartgasse 12, 8010 Graz, Austria
| |
Collapse
|
4
|
Bermejo PE, Dorado R, Zea-Sevilla MA. Role of Citicoline in Patients With Mild Cognitive Impairment. Neurosci Insights 2023; 18:26331055231152496. [PMID: 36818199 PMCID: PMC9936398 DOI: 10.1177/26331055231152496] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 01/06/2023] [Indexed: 02/18/2023] Open
Abstract
The term mild cognitive impairment (MCI) defines an intermediate state between normal aging and dementia. Vascular cognitive impairment refers to a decline in cognitive function that is caused by or associated with vascular disease and comprises all the spectrum of cognitive impairments, from MCI of vascular origin to vascular dementia. One of the available treatments for cognitive impairment is cytidine diphosphate-choline (CDP-Choline), or citicoline. The objective of the present manuscript is to provide complete evidence about the efficacy of citicoline for MCI, especially of vascular origin, but also due to other neurodegenerative disorders. Citicoline is a pharmaceutical product constituted by the combination of 2 natural molecules (cytidine and choline) and is marketed as a food supplement. It has been proposed to provide neuroprotective effects through diverse mechanisms of action. Taking into account the available literature, citicoline has shown a consistent improvement in cognitive function in patients with MCI, especially of vascular origin. Moreover, it provides beneficial effects on vascular, Alzheimer, and mixed dementias, stroke sequelae, intracerebral hemorrhages, traumatic brain injuries, and neurodegenerative diseases. Long-term treatment with citicoline has also been demonstrated to be well-tolerated and has not been associated with severe adverse events. Citicoline is a safe, well-tolerated, and promising agent with evidenced neuroprotective properties.
Collapse
Affiliation(s)
- Pedro E Bermejo
- University Hospital Puerta de Hierro-Majadahonda, Madrid, Spain,Instituto Neurológico Beremia, Madrid, Spain,Pedro E Bermejo, Department of Neurology, University Hospital Puerta de Hierro-Majadahonda, C/Joaquín Rodrigo, 1, Majadahonda 28222, Madrid, Spain.
| | | | | |
Collapse
|
5
|
Jansen MG, Griffanti L, Mackay CE, Anatürk M, Melazzini L, Lange AMGD, Filippini N, Zsoldos E, Wiegertjes K, Leeuw FED, Singh-Manoux A, Kivimäki M, Ebmeier KP, Suri S. Association of cerebral small vessel disease burden with brain structure and cognitive and vascular risk trajectories in mid-to-late life. J Cereb Blood Flow Metab 2022; 42:600-612. [PMID: 34610763 PMCID: PMC8943617 DOI: 10.1177/0271678x211048411] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We characterize the associations of total cerebral small vessel disease (SVD) burden with brain structure, trajectories of vascular risk factors, and cognitive functions in mid-to-late life. Participants were 623 community-dwelling adults from the Whitehall II Imaging Sub-study with multi-modal MRI (mean age 69.96, SD = 5.18, 79% men). We used linear mixed-effects models to investigate associations of SVD burden with up to 25-year retrospective trajectories of vascular risk and cognitive performance. General linear modelling was used to investigate concurrent associations with grey matter (GM) density and white matter (WM) microstructure, and whether these associations were modified by cognitive status (Montreal Cognitive Asessment [MoCA] scores of < 26 vs. ≥ 26). Severe SVD burden in older age was associated with higher mean arterial pressure throughout midlife (β = 3.36, 95% CI [0.42-6.30]), and faster cognitive decline in letter fluency (β = -0.07, 95% CI [-0.13--0.01]), and verbal reasoning (β = -0.05, 95% CI [-0.11--0.001]). Moreover, SVD burden was related to lower GM volumes in 9.7% of total GM, and widespread WM microstructural decline (FWE-corrected p < 0.05). The latter association was most pronounced in individuals who demonstrated cognitive impairments on MoCA (MoCA < 26; F3,608 = 2.14, p = 0.007). These findings highlight the importance of managing midlife vascular health to preserve brain structure and cognitive function in old age.
Collapse
Affiliation(s)
- Michelle G Jansen
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.,Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ludovica Griffanti
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK
| | - Clare E Mackay
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK
| | - Melis Anatürk
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Centre for Medical Image Computing, Department of Computer Science, 4919University College London, University College London, London, UK
| | - Luca Melazzini
- Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Ann-Marie G de Lange
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Department of Psychology, 6305University of Oslo, University of Oslo, Oslo, Norway
| | | | - Enikő Zsoldos
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Archana Singh-Manoux
- Department of Epidemiology and Public Health, 4919University College London, University College London, London, UK.,INSERM, Epidemiology of Ageing and Neurogenerative Diseases, Université de Paris, Paris, France
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, 4919University College London, University College London, London, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK
| | - Sana Suri
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK
| |
Collapse
|
6
|
Andersen E, Casteigne B, Chapman WD, Creed A, Foster F, Lapins A, Shatz R, Sawyer RP. Diagnostic biomarkers in Alzheimer’s disease. Biomark Neuropsychiatry 2021. [DOI: 10.1016/j.bionps.2021.100041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
|
7
|
Koponen M, Rajamaki B, Lavikainen P, Bell JS, Taipale H, Tanskanen A, Hartikainen S, Tolppanen AM. Antipsychotic Use and Risk of Stroke Among Community-Dwelling People With Alzheimer's Disease. J Am Med Dir Assoc 2021; 23:1059-1065.e4. [PMID: 34717887 DOI: 10.1016/j.jamda.2021.09.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Antipsychotic use for neuropsychiatric symptoms in Alzheimer's disease (AD) is common despite the increased risk of cardiovascular events and mortality. There is limited and inconsistent evidence on the possible risk of stroke. We assessed whether antipsychotic initiation increases the risk of stroke in people with a verified diagnosis of AD and whether there is a difference in stroke risk between the 2 most commonly used antipsychotics, risperidone and quetiapine. DESIGN Register-based exposure-matched cohort study. SETTING AND PARTICIPANTS The Medication Use and Alzheimer's Disease (MEDALZ) cohort included 70,718 community-dwelling people with AD in Finland during 2005-2011. People with previous strokes were excluded. METHODS For each incident antipsychotic user (n = 20,467), 1 nonuser was matched according to sex, age, and time since AD diagnosis. Analyses were conducted with inverse probability of treatment-weighted (IPTW) Cox proportional hazards models. RESULTS Compared with nonuse, antipsychotic use was associated with an increased risk of stroke within 60 days of antipsychotic initiation [IPTW hazard ratio (HR) 1.73, 95% confidence interval (CI) 1.32-2.28]. However, there was no significant overall association between antipsychotic use and the risk of stroke (IPTW HR 1.09, 95% CI 0.98-1.22). There was no difference in stroke risk between risperidone and quetiapine (IPTW HR 1.12, 95% CI 0.91-1.37). CONCLUSIONS AND IMPLICATIONS Stroke risk is increased shortly after antipsychotic initiation in people with AD, suggesting that even short-term use of antipsychotics should be avoided if possible. If antipsychotics are prescribed, effectiveness and safety should be assessed soon after initiation and treatment limited to the shortest possible duration.
Collapse
Affiliation(s)
- Marjaana Koponen
- Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; School of Pharmacy, University of Eastern Finland, Kuopio, Finland; Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia
| | - Blair Rajamaki
- Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
| | - Piia Lavikainen
- Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - J Simon Bell
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia; Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Heidi Taipale
- Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; School of Pharmacy, University of Eastern Finland, Kuopio, Finland; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland
| | - Antti Tanskanen
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland; Public Health Evaluation and Projection, National Institute for Health and Welfare, Helsinki, Finland
| | - Sirpa Hartikainen
- Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Anna-Maija Tolppanen
- Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| |
Collapse
|
8
|
Suri S, Bulte D, Chiesa ST, Ebmeier KP, Jezzard P, Rieger SW, Pitt JE, Griffanti L, Okell TW, Craig M, Chappell MA, Blockley NP, Kivimäki M, Singh-Manoux A, Khir AW, Hughes AD, Deanfield JE, Jensen DEA, Green SF, Sigutova V, Jansen MG, Zsoldos E, Mackay CE. Study Protocol: The Heart and Brain Study. Front Physiol 2021; 12:643725. [PMID: 33868011 PMCID: PMC8046163 DOI: 10.3389/fphys.2021.643725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/03/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND It is well-established that what is good for the heart is good for the brain. Vascular factors such as hypertension, diabetes, and high cholesterol, and genetic factors such as the apolipoprotein E4 allele increase the risk of developing both cardiovascular disease and dementia. However, the mechanisms underlying the heart-brain association remain unclear. Recent evidence suggests that impairments in vascular phenotypes and cerebrovascular reactivity (CVR) may play an important role in cognitive decline. The Heart and Brain Study combines state-of-the-art vascular ultrasound, cerebrovascular magnetic resonance imaging (MRI) and cognitive testing in participants of the long-running Whitehall II Imaging cohort to examine these processes together. This paper describes the study protocol, data pre-processing and overarching objectives. METHODS AND DESIGN The 775 participants of the Whitehall II Imaging cohort, aged 65 years or older in 2019, have received clinical and vascular risk assessments at 5-year-intervals since 1985, as well as a 3T brain MRI scan and neuropsychological tests between 2012 and 2016 (Whitehall II Wave MRI-1). Approximately 25% of this cohort are selected for the Heart and Brain Study, which involves a single testing session at the University of Oxford (Wave MRI-2). Between 2019 and 2023, participants will undergo ultrasound scans of the ascending aorta and common carotid arteries, measures of central and peripheral blood pressure, and 3T MRI scans to measure CVR in response to 5% carbon dioxide in air, vessel-selective cerebral blood flow (CBF), and cerebrovascular lesions. The structural and diffusion MRI scans and neuropsychological battery conducted at Wave MRI-1 will also be repeated. Using this extensive life-course data, the Heart and Brain Study will examine how 30-year trajectories of vascular risk throughout midlife (40-70 years) affect vascular phenotypes, cerebrovascular health, longitudinal brain atrophy and cognitive decline at older ages. DISCUSSION The study will generate one of the most comprehensive datasets to examine the longitudinal determinants of the heart-brain association. It will evaluate novel physiological processes in order to describe the optimal window for managing vascular risk in order to delay cognitive decline. Ultimately, the Heart and Brain Study will inform strategies to identify at-risk individuals for targeted interventions to prevent or delay dementia.
Collapse
Affiliation(s)
- Sana Suri
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Daniel Bulte
- Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Scott T. Chiesa
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Klaus P. Ebmeier
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | - Peter Jezzard
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Sebastian W. Rieger
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jemma E. Pitt
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Ludovica Griffanti
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Thomas W. Okell
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Martin Craig
- Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - Michael A. Chappell
- Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | | | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Archana Singh-Manoux
- Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Paris, France
| | - Ashraf W. Khir
- Mechanical Engineering, Brunel University London, Uxbridge, United Kingdom
| | - Alun D. Hughes
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - John E. Deanfield
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Daria E. A. Jensen
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Sebastian F. Green
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | - Veronika Sigutova
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | - Michelle G. Jansen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Enikő Zsoldos
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Clare E. Mackay
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
9
|
Suri S, Chiesa ST, Zsoldos E, Mackay CE, Filippini N, Griffanti L, Mahmood A, Singh-Manoux A, Shipley MJ, Brunner EJ, Kivimäki M, Deanfield JE, Ebmeier KP. Associations between arterial stiffening and brain structure, perfusion, and cognition in the Whitehall II Imaging Sub-study: A retrospective cohort study. PLoS Med 2020; 17:e1003467. [PMID: 33373359 PMCID: PMC7771705 DOI: 10.1371/journal.pmed.1003467] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 11/13/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Aortic stiffness is closely linked with cardiovascular diseases (CVDs), but recent studies suggest that it is also a risk factor for cognitive decline and dementia. However, the brain changes underlying this risk are unclear. We examined whether aortic stiffening during a 4-year follow-up in mid-to-late life was associated with brain structure and cognition in the Whitehall II Imaging Sub-study. METHODS AND FINDINGS The Whitehall II Imaging cohort is a randomly selected subset of the ongoing Whitehall II Study, for which participants have received clinical follow-ups for 30 years, across 12 phases. Aortic pulse wave velocity (PWV) was measured in 2007-2009 (Phase 9) and at a 4-year follow-up in 2012-2013 (Phase 11). Between 2012 and 2016 (Imaging Phase), participants received a multimodal 3T brain magnetic resonance imaging (MRI) scan and cognitive tests. Participants were selected if they had no clinical diagnosis of dementia and no gross brain structural abnormalities. Voxel-based analyses were used to assess grey matter (GM) volume, white matter (WM) microstructure (fractional anisotropy (FA) and diffusivity), white matter lesions (WMLs), and cerebral blood flow (CBF). Cognitive outcomes were performance on verbal memory, semantic fluency, working memory, and executive function tests. Of 542 participants, 444 (81.9%) were men. The mean (SD) age was 63.9 (5.2) years at the baseline Phase 9 examination, 68.0 (5.2) at Phase 11, and 69.8 (5.2) at the Imaging Phase. Voxel-based analysis revealed that faster rates of aortic stiffening in mid-to-late life were associated with poor WM microstructure, viz. lower FA, higher mean, and radial diffusivity (RD) in 23.9%, 11.8%, and 22.2% of WM tracts, respectively, including the corpus callosum, corona radiata, superior longitudinal fasciculus, and corticospinal tracts. Similar voxel-wise associations were also observed with follow-up aortic stiffness. Moreover, lower mean global FA was associated with faster rates of aortic stiffening (B = -5.65, 95% CI -9.75, -1.54, Bonferroni-corrected p < 0.0125) and higher follow-up aortic stiffness (B = -1.12, 95% CI -1.95, -0.29, Bonferroni-corrected p < 0.0125). In a subset of 112 participants who received arterial spin labelling scans, faster aortic stiffening was also related to lower cerebral perfusion in 18.4% of GM, with associations surviving Bonferroni corrections in the frontal (B = -10.85, 95% CI -17.91, -3.79, p < 0.0125) and parietal lobes (B = -12.75, 95% CI -21.58, -3.91, p < 0.0125). No associations with GM volume or WMLs were observed. Further, higher baseline aortic stiffness was associated with poor semantic fluency (B = -0.47, 95% CI -0.76 to -0.18, Bonferroni-corrected p < 0.007) and verbal learning outcomes (B = -0.36, 95% CI -0.60 to -0.12, Bonferroni-corrected p < 0.007). As with all observational studies, it was not possible to infer causal associations. The generalisability of the findings may be limited by the gender imbalance, high educational attainment, survival bias, and lack of ethnic and socioeconomic diversity in this cohort. CONCLUSIONS Our findings indicate that faster rates of aortic stiffening in mid-to-late life were associated with poor brain WM microstructural integrity and reduced cerebral perfusion, likely due to increased transmission of pulsatile energy to the delicate cerebral microvasculature. Strategies to prevent arterial stiffening prior to this point may be required to offer cognitive benefit in older age. TRIAL REGISTRATION ClinicalTrials.gov NCT03335696.
Collapse
Affiliation(s)
- Sana Suri
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Scott T. Chiesa
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Enikő Zsoldos
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Clare E. Mackay
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Nicola Filippini
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Ludovica Griffanti
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Abda Mahmood
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | - Archana Singh-Manoux
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
- Inserm U1153, Epidemiology of Ageing and Neurodegenerative diseases, Université de Paris, Paris, France
| | - Martin J. Shipley
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Eric J. Brunner
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - John E. Deanfield
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Klaus P. Ebmeier
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
10
|
The role of transcranial sonography in differentiation of dementia subtypes: an introduction of a new diagnostic method. Neurol Sci 2020; 42:275-283. [DOI: 10.1007/s10072-020-04566-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 07/02/2020] [Indexed: 12/21/2022]
|
11
|
Determining the effects of LLD and MCI on brain decline according to machine learning and a structural covariance network analysis. J Psychiatr Res 2020; 126:43-54. [PMID: 32416386 DOI: 10.1016/j.jpsychires.2020.04.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/21/2020] [Accepted: 04/27/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Late-life depression (LLD) and mild cognitive impairment (MCI) are risk factors for Alzheimer disease (AD). However, the interactive effect between LLD and MCI in the progression to AD remains unknown. The purpose of this research is to clarify whether this interaction exists and determined the characteristics of the structural change patterns in LLD and MCI. METHOD To address this question, a total 225 participants (91 with intact cognitive function (IC), 34 with MCI, 35 with LLD-IC, 47 with LLD-MCI and 18 with AD) were recruited for the current study and their T1 scanning were acquired. Machine learning was applied to estimate the brain's age gap according to grey matter information (thickness and volume was calculated based on the Human Connectome Project Multi-Modal Parcellation version 1.0 and the Desikan atlas). A structural covariance network (SCN) was constructed based on grey matter volume. Rich-club analysis, global network properties and the Jaccard distance were utilized to describe the topological features in each cohort. Their cognitive functions (executive function, processing speed and memory) were evaluated by a full-scale battery of neuropsychological tests. RESULT The interactive effect between LLD and MCI was detected through the brain age gap. The estimated age was positively correlated with processing speed and memory in LLD and non-LLD subjects. In the SCN analysis, the rich-club coefficient and global network properties were disrupted in the MCI group, but remained normal in the LLD-IC, LLD-MCI and AD groups. There was a significant discrepancy in brain structural change patterns between the AD and other cohorts by the Jaccard distance. CONCLUSION The application of machine learning reflects that synergies between LLD and MCI could increase the risk of developing AD. According to the SCN, the structural coordination was disrupted in MCI and was kept normal in the other cohorts, while the discrepancies in brain structural change patterns appeared in AD. Overall, the brain age gap could be a potential predictor of AD, and the Jaccard distance has the potential to be a new type of SCN analysis indicator.
Collapse
|
12
|
Wassenaar TM, Yaffe K, van der Werf YD, Sexton CE. Associations between modifiable risk factors and white matter of the aging brain: insights from diffusion tensor imaging studies. Neurobiol Aging 2019; 80:56-70. [PMID: 31103633 PMCID: PMC6683729 DOI: 10.1016/j.neurobiolaging.2019.04.006] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 03/26/2019] [Accepted: 04/05/2019] [Indexed: 01/13/2023]
Abstract
There is increasing interest in factors that may modulate white matter (WM) breakdown and, consequentially, age-related cognitive and behavioral deficits. Recent diffusion tensor imaging studies have examined the relationship of such factors with WM microstructure. This review summarizes the evidence regarding the relationship between WM microstructure and recognized modifiable factors, including hearing loss, hypertension, diabetes, obesity, smoking, depressive symptoms, physical (in) activity, and social isolation, as well as sleep disturbances, diet, cognitive training, and meditation. Current cross-sectional evidence suggests a clear link between loss of WM integrity (lower fractional anisotropy and higher mean diffusivity) and hypertension, obesity, diabetes, and smoking; a relationship that seems to hold for hearing loss, social isolation, depressive symptoms, and sleep disturbances. Physical activity, cognitive training, diet, and meditation, on the other hand, may protect WM with aging. Preliminary evidence from cross-sectional studies of treated risk factors suggests that modification of factors could slow down negative effects on WM microstructure. Careful intervention studies are needed for this literature to contribute to public health initiatives going forward. Both aging and dementia are associated with breakdown of white matter (WM) microstructure. We review a range of modifiable factors that could prevent or slow age-related WM decline. Risk factors are consistently related with lower fractional anisotropy and higher mean diffusivity. Treatment of risk factors may slow WM decline. Careful longitudinal and intervention studies are now needed.
Collapse
Affiliation(s)
- Thomas M Wassenaar
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, John Radcliffe Hospital, UK
| | - Kristine Yaffe
- Departments of Psychiatry, Neurology, and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Ysbrand D van der Werf
- Department of Anatomy and Neurosciences, VU University Medical Center, MC, Amsterdam, the Netherlands
| | - Claire E Sexton
- Department of Neurology, Global Brain Health Institute, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA; Department of Psychiatry, Wellcome Centre for Integrative Neuroscience, Oxford Centre for Human Brain Activity, University of Oxford, John Radcliffe Hospital, UK.
| |
Collapse
|
13
|
Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI. NEUROIMAGE-CLINICAL 2019; 22:101718. [PMID: 30827922 PMCID: PMC6543025 DOI: 10.1016/j.nicl.2019.101718] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004). Conclusions FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.
Collapse
|
14
|
Feis RA, Bouts MJRJ, Panman JL, Jiskoot LC, Dopper EGP, Schouten TM, de Vos F, van der Grond J, van Swieten JC, Rombouts SARB. Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI. Neuroimage Clin 2018; 20:188-196. [PMID: 30094168 PMCID: PMC6072645 DOI: 10.1016/j.nicl.2018.07.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/29/2018] [Accepted: 07/15/2018] [Indexed: 11/30/2022]
Abstract
Background Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005). Conclusions FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.
Collapse
Key Words
- (bv)FTD, (behavioural variant) Frontotemporal dementia
- (rs-f)MRI, (resting-state functional) Magnetic resonance imaging
- 3DT1w, 3-dimensional T1-weighted
- AUC, Area under the receiver operating characteristics curve
- AxD, Axial diffusivity
- C9orf72, Chromosome 9 open reading frame 72
- C9orf72, human
- DTI, Diffusion tensor imaging
- DWI, Diffusion-weighted imaging
- Diffusion Tensor Imaging
- FA, Fractional anisotropy
- FCor, Full correlations
- Frontotemporal dementia
- GM, Grey matter
- GMD, Grey matter density
- GRN protein, human
- GRN, Progranulin
- ICA, Independent component analysis
- MAPT protein, human
- MAPT, Microtubule-associated protein Tau
- MD, Mean diffusivity
- MMSE, Mini-mental state examination
- Multimodal MRI
- Pcor, Sparse L1-regularised partial correlations
- RD, Radial diffusivity
- ROC, Receiver operating characteristics
- Resting-state functional MRI
- TBSS, Tract-based spatial statistics
- WM, White matter
- WMD, White matter density
- classification
- machine learning
Collapse
Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands.
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands.
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands.
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands; Alzheimer Centre & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, the Netherlands.
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands.
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands; Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, the Netherlands.
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| |
Collapse
|
15
|
Palesi F, De Rinaldis A, Vitali P, Castellazzi G, Casiraghi L, Germani G, Bernini S, Anzalone N, Ramusino MC, Denaro FM, Sinforiani E, Costa A, Magenes G, D'Angelo E, Gandini Wheeler-Kingshott CAM, Micieli G. Specific Patterns of White Matter Alterations Help Distinguishing Alzheimer's and Vascular Dementia. Front Neurosci 2018; 12:274. [PMID: 29922120 PMCID: PMC5996902 DOI: 10.3389/fnins.2018.00274] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 04/09/2018] [Indexed: 12/19/2022] Open
Abstract
Alzheimer disease (AD) and vascular dementia (VaD) together represent the majority of dementia cases. Since their neuropsychological profiles often overlap and white matter lesions are observed in elderly subjects including AD, differentiating between VaD and AD can be difficult. Characterization of these different forms of dementia would benefit by identification of quantitative imaging biomarkers specifically sensitive to AD or VaD. Parameters of microstructural abnormalities derived from diffusion tensor imaging (DTI) have been reported to be helpful in differentiating between dementias, but only few studies have used them to compare AD and VaD with a voxelwise approach. Therefore, in this study a whole brain statistical analysis was performed on DTI data of 93 subjects (31 AD, 27 VaD, and 35 healthy controls—HC) to identify specific white matter patterns of alteration in patients affected by VaD and AD with respect to HC. Parahippocampal tracts were found to be mainly affected in AD, while VaD showed more spread white matter damages associated with thalamic radiations involvement. The genu of the corpus callosum was predominantly affected in VaD, while the splenium was predominantly affected in AD revealing the existence of specific patterns of alteration useful in distinguishing between VaD and AD. Therefore, DTI parameters of these regions could be informative to understand the pathogenesis and support the etiological diagnosis of dementia. Further studies on larger cohorts of subjects, characterized for brain amyloidosis, will allow to confirm and to integrate the present findings and, furthermore, to elucidate the mechanisms of mixed dementia. These steps will be essential to translate these advances to clinical practice.
Collapse
Affiliation(s)
- Fulvia Palesi
- Department of Physics, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Andrea De Rinaldis
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Paolo Vitali
- Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Gloria Castellazzi
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Letizia Casiraghi
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Giancarlo Germani
- Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Sara Bernini
- Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Matteo Cotta Ramusino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Federica M Denaro
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Elena Sinforiani
- Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Giovanni Magenes
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Egidio D'Angelo
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Giuseppe Micieli
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
| |
Collapse
|
16
|
Chouliaras L, Pishva E, Haapakoski R, Zsoldos E, Mahmood A, Filippini N, Burrage J, Mill J, Kivimäki M, Lunnon K, Ebmeier KP. Peripheral DNA methylation, cognitive decline and brain aging: pilot findings from the Whitehall II imaging study. Epigenomics 2018; 10:585-595. [PMID: 29692214 PMCID: PMC6021930 DOI: 10.2217/epi-2017-0132] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: The present study investigated the link between peripheral DNA methylation (DNAm), cognitive impairment and brain aging. Methods: We tested the association between blood genome-wide DNAm profiles using the Illumina 450K arrays, cognitive dysfunction and brain MRI measures in selected participants of the Whitehall II imaging sub-study. Results: Eight differentially methylated regions were associated with cognitive impairment. Accelerated aging based on the Hannum epigenetic clock was associated with mean diffusivity and global fractional anisotropy. We also identified modules of co-methylated loci associated with white matter hyperintensities. These co-methylation modules were enriched among pathways relevant to β-amyloid processing and glutamatergic signaling. Conclusion: Our data support the notion that blood DNAm changes may have utility as a biomarker for cognitive dysfunction and brain aging.
Collapse
Affiliation(s)
- Leonidas Chouliaras
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK.,Current: Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Ehsan Pishva
- University of Exeter Medical School, RILD, University of Exeter, Barrack Road, Exeter, UK.,Department of Psychiatry & Neuropsychology, School for Mental Health & Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Rita Haapakoski
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK.,Department of Epidemiology & Public Health, University College London, London, UK
| | - Eniko Zsoldos
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Abda Mahmood
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Nicola Filippini
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Joe Burrage
- University of Exeter Medical School, RILD, University of Exeter, Barrack Road, Exeter, UK
| | - Jonathan Mill
- University of Exeter Medical School, RILD, University of Exeter, Barrack Road, Exeter, UK
| | - Mika Kivimäki
- Department of Epidemiology & Public Health, University College London, London, UK.,Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Katie Lunnon
- University of Exeter Medical School, RILD, University of Exeter, Barrack Road, Exeter, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| |
Collapse
|
17
|
Abstract
BACKGROUND Alzheimer's Disease (AD) and Vascular Dementia (VaD) are the most common causes of dementia in older people. Both diseases appear to have similar clinical symptoms, such as deficits in attention and executive function, but specific cognitive domains are affected. Current cohort studies have shown a close relationship between αβ deposits and age-related macular degeneration (Johnson et al., 2002; Ratnayaka et al., 2015). Additionally, a close link between the thinning of the retinal nerve fiber (RNFL) and AD patients has been described, while it has been proposed that AD patients suffer from a non-specific type of color blindness (Pache et al., 2003). METHODS Our study included 103 individuals divided into three groups: A healthy control group (n = 35), AD (n = 32) according to DSM-IV-TR, NINCDS-ADRDA criteria, and VaD (n = 36) based on ΝΙΝDS-AIREN, as well as Magnetic Resonance Imaging (MRI) results. The severity of patient's cognitive impairment, was measured with the Mini-Mental State Examination (MMSE) and was classified according to the Reisberg global deterioration scale (GDS). Visual perception was examined using the Ishihara plates: "Ishihara Color Vision Test - 38 Plate." RESULTS The three groups were not statistically different for demographic data (age, gender, and education). The Ishihara color blindness test has a sensitivity of 80.6% and a specificity of 87.5% to discriminate AD and VaD patients when an optimal (32.5) cut-off value of performance is used. CONCLUSIONS Ishihara Color Vision Test - 38 Plate is a promising potential method as an easy and not time-consuming screening test for the differential diagnosis of dementia between AD and VaD.
Collapse
|
18
|
Gangolli M, Holleran L, Hee Kim J, Stein TD, Alvarez V, McKee AC, Brody DL. Quantitative validation of a nonlinear histology-MRI coregistration method using generalized Q-sampling imaging in complex human cortical white matter. Neuroimage 2017; 153:152-167. [PMID: 28365421 DOI: 10.1016/j.neuroimage.2017.03.059] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 03/24/2017] [Accepted: 03/29/2017] [Indexed: 12/14/2022] Open
Abstract
Advanced diffusion MRI methods have recently been proposed for detection of pathologies such as traumatic axonal injury and chronic traumatic encephalopathy which commonly affect complex cortical brain regions. However, radiological-pathological correlations in human brain tissue that detail the relationship between the multi-component diffusion signal and underlying pathology are lacking. We present a nonlinear voxel based two dimensional coregistration method that is useful for matching diffusion signals to quantitative metrics of high resolution histological images. When validated in ex vivo human cortical tissue at a 250×250×500 μm spatial resolution, the method proved robust in correlations between generalized q-sampling imaging and histologically based white matter fiber orientations, with r=0.94 for the primary fiber direction and r=0.88 for secondary fiber direction in each voxel. Importantly, however, the correlation was substantially worse with reduced spatial resolution or with fiber orientations derived using a diffusion tensor model. Furthermore, we have detailed a quantitative histological metric of white matter fiber integrity termed power coherence capable of distinguishing architecturally complex but intact white matter from disrupted white matter regions. These methods may allow for more sensitive and specific radiological-pathological correlations of neurodegenerative diseases affecting complex gray and white matter.
Collapse
Affiliation(s)
- Mihika Gangolli
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Laurena Holleran
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joong Hee Kim
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Thor D Stein
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
| | - Victor Alvarez
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
| | - Ann C McKee
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
| | - David L Brody
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA.
| |
Collapse
|
19
|
Oppedal K, Engan K, Eftestøl T, Beyer M, Aarsland D. Classifying Alzheimer's disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.10.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
20
|
The neuropathology and cerebrovascular mechanisms of dementia. J Cereb Blood Flow Metab 2016; 36:172-86. [PMID: 26174330 PMCID: PMC4758551 DOI: 10.1038/jcbfm.2015.164] [Citation(s) in RCA: 238] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2015] [Revised: 06/12/2015] [Accepted: 06/15/2015] [Indexed: 12/23/2022]
Abstract
The prevalence of dementia is increasing in our aging population at an alarming rate. Because of the heterogeneity of clinical presentation and complexity of disease neuropathology, dementia classifications remain controversial. Recently, the National Plan to address Alzheimer’s Disease prioritized Alzheimer’s disease-related dementias to include: Alzheimer’s disease, dementia with Lewy bodies, frontotemporal dementia, vascular dementia, and mixed dementias. While each of these dementing conditions has their unique pathologic signature, one common etiology shared among all these conditions is cerebrovascular dysfunction at some point during the disease process. The goal of this comprehensive review is to summarize the current findings in the field and address the important contributions of cerebrovascular, physiologic, and cellular alterations to cognitive impairment in these human dementias. Specifically, evidence will be presented in support of small-vessel disease as an underlying neuropathologic hallmark of various dementias, while controversial findings will also be highlighted. Finally, the molecular mechanisms shared among all dementia types including hypoxia, oxidative stress, mitochondrial bioenergetics, neuroinflammation, neurodegeneration, and blood–brain barrier permeability responsible for disease etiology and progression will be discussed.
Collapse
|
21
|
Chilla GS, Tan CH, Xu C, Poh CL. Diffusion weighted magnetic resonance imaging and its recent trend-a survey. Quant Imaging Med Surg 2015; 5:407-22. [PMID: 26029644 DOI: 10.3978/j.issn.2223-4292.2015.03.01] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 01/15/2015] [Indexed: 12/14/2022]
Abstract
Since its inception in 1985, diffusion weighted magnetic resonance imaging has been evolving and is becoming instrumental in diagnosis and investigation of tissue functions in various organs including brain, cartilage, and liver. Even though brain related pathology and/or investigation remains as the main application, diffusion weighted magnetic resonance imaging (DWI) is becoming a standard in oncology and in several other applications. This review article provides a brief introduction of diffusion weighted magnetic resonance imaging, challenges involved and recent advancements.
Collapse
Affiliation(s)
- Geetha Soujanya Chilla
- 1 School of Chemical & Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore ; 2 Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Cher Heng Tan
- 1 School of Chemical & Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore ; 2 Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Chenjie Xu
- 1 School of Chemical & Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore ; 2 Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Chueh Loo Poh
- 1 School of Chemical & Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore ; 2 Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| |
Collapse
|
22
|
The Relationship Between Atrophy and Hypometabolism: Is It Regionally Dependent in Dementias? Curr Neurol Neurosci Rep 2015; 15:44. [DOI: 10.1007/s11910-015-0562-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|