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Rachmadi MF, Byra M, Skibbe H. A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI. Comput Biol Med 2024; 174:108414. [PMID: 38599072 DOI: 10.1016/j.compbiomed.2024.108414] [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: 10/17/2023] [Revised: 03/16/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024]
Abstract
In this study, we introduce "instance loss functions", a new family of loss functions designed to enhance the training of neural networks in the instance-level segmentation and detection of objects in biomedical image data, particularly those of varied numbers and sizes. Intended to be utilized conjointly with traditional loss functions, these proposed functions, prioritize object instances over pixel-by-pixel comparisons. The specific functions, the instance segmentation loss (Linstance), the instance center loss (Lcenter), the false instance rate loss (Lfalse), and the instance proximity loss (Lproximity), serve distinct purposes. Specifically, Linstance improves instance-wise segmentation quality, Lcenter enhances segmentation quality of small instances, Lfalse minimizes the rate of false and missed detections across varied instance sizes, and Lproximity improves detection quality by pulling predicted instances towards the ground truth instances. Through the task of segmenting white matter hyperintensities (WMH) in brain MRI, we benchmarked our proposed instance loss functions, both individually and in combination via an ensemble inference models approach, against traditional pixel-level loss functions. Data were sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the WMH Segmentation Challenge datasets, which exhibit significant variation in WMH instance sizes. Empirical evaluations demonstrate that combining two instance-level loss functions through ensemble inference models outperforms models using other loss function on both the ADNI and WMH Segmentation Challenge datasets for the segmentation and detection of WMH instances. Further, applying these functions to the segmentation of nuclei in histopathology images demonstrated their effectiveness and generalizability beyond WMH, improving performance even in contexts with less severe instance imbalance.
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Affiliation(s)
- Muhammad Febrian Rachmadi
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan; Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia.
| | - Michal Byra
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan; Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan
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Öksüz N, Ghouri R, Taşdelen B, Uludüz D, Özge A. Mild Cognitive Impairment Progression and Alzheimer's Disease Risk: A Comprehensive Analysis of 3553 Cases over 203 Months. J Clin Med 2024; 13:518. [PMID: 38256652 PMCID: PMC10817043 DOI: 10.3390/jcm13020518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/04/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024] Open
Abstract
This study aimed to elucidate the long-term progression of mild cognitive impairment (MCI) within a comprehensive longitudinal dataset, distinguish it from healthy aging, explore the influence of a dementia subtype on this progression, and identify potential contributing factors. Patients with prodromal and preclinical cases underwent regular neuropsychological assessments utilizing various tools. The study included a total of 140 participants with MCI, categorized into Alzheimer's disease (AD) and non-AD subtypes. Our dataset revealed an overall progression rate of 92.8% from MCI to the clinical stage of dementia during the follow-up period, with an annual rate of 15.7%. Notably, all prodromal cases of Lewy body dementia/Parkinson's disease (LBD/PDD) and frontotemporal dementia (FTD) advanced to clinical stages, whereas 7% of vascular dementia (VaD) cases and 8.4% of AD cases remained in the prodromal stage throughout follow-up. Furthermore, we observed a faster progression rate in MCI-AD cases compared to non-AD sufferers (53.9% vs. 35.5%, Entropy: 0.850). This study revealed significant cognitive changes in individuals with MCI over time. The mini-mental state examination (MMSE), global deterioration scale (GDS), and calculation tests were the most effective tests for evaluation of MCI. These findings may offer valuable insights for the development of personalized interventions and management strategies for individuals with MCI.
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Affiliation(s)
- Nevra Öksüz
- Department of Neurology, School of Medicine, Mersin University, Mersin 33110, Turkey; (N.Ö.); (R.G.)
| | - Reza Ghouri
- Department of Neurology, School of Medicine, Mersin University, Mersin 33110, Turkey; (N.Ö.); (R.G.)
| | - Bahar Taşdelen
- Department of Biostatistics, School of Medicine, Mersin University, Mersin 33110, Turkey;
| | - Derya Uludüz
- Department of Neurology, Brain 360 Holistic Approach Center, İstanbul 34353, Turkey;
| | - Aynur Özge
- Department of Neurology, School of Medicine, Mersin University, Mersin 33110, Turkey; (N.Ö.); (R.G.)
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Almeida MF, Farizatto KLG, Almeida RS, Bahr BA. Lifestyle strategies to promote proteostasis and reduce the risk of Alzheimer's disease and other proteinopathies. Ageing Res Rev 2024; 93:102162. [PMID: 38070831 DOI: 10.1016/j.arr.2023.102162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/31/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023]
Abstract
Unhealthy lifestyle choices, poor diet, and aging can have negative influences on cognition, gradually increasing the risk for mild cognitive impairment (MCI) and the continuum comprising early dementia. Aging is the greatest risk factor for age-related dementias such as Alzheimer's disease, and the aging process is known to be influenced by life events that can positively or negatively affect age-related diseases. Remarkably, life experiences that make the brain vulnerable to dementia, such as seizure episodes, neurotoxin exposures, metabolic disorders, and trauma-inducing events (e.g. traumatic injuries or mild neurotrauma from a fall or blast exposure), have been associated with negative effects on proteostasis and synaptic integrity. Functional compromise of the autophagy-lysosomal pathway, a major contributor to proteostasis, has been implicated in Alzheimer's disease, Parkinson's disease, obesity-related pathology, Huntington's disease, as well as in synaptic degeneration which is the best correlate of cognitive decline. Correspondingly, pharmacological and non-pharmacological strategies that positively modulate lysosomal proteases are recognized as synaptoprotective through degradative clearance of pathogenic proteins. Here, we discuss life-associated vulnerabilities that influence key hallmarks of brain aging and the increased burden of age-related dementias. Additionally, we discuss exercise and diet among the lifestyle strategies that regulate proteostasis as well as synaptic integrity, leading to evident prevention of cognitive deficits during brain aging in pre-clinical models.
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Affiliation(s)
- Michael F Almeida
- Biotechnology Research and Training Center, University of North Carolina - Pembroke, Pembroke, NC 28372, USA; Department of Biology, University of North Carolina - Pembroke, Pembroke, NC 28372, USA; Department of Biology & Marine Biology, and the Integrative, Comparative & Marine Biology Program, University of North Carolina - Wilmington, Wilmington, NC 28409, USA
| | - Karen L G Farizatto
- Biotechnology Research and Training Center, University of North Carolina - Pembroke, Pembroke, NC 28372, USA
| | - Renato S Almeida
- Department of Biosciences, University of Taubate, Taubate, SP 12020-270, Brazil
| | - Ben A Bahr
- Biotechnology Research and Training Center, University of North Carolina - Pembroke, Pembroke, NC 28372, USA; Department of Biology, University of North Carolina - Pembroke, Pembroke, NC 28372, USA.
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Sundar U, Mukhopadhyay A, Raghavan S, Debata I, Menon RN, Kesavadas C, Shah N, Adsul BB, Joshi AR, Tejas J. Evaluation of 'Normal' Cognitive Functions and Correlation With MRI Volumetry: Towards a Definition of Vascular Cognitive Impairment. Cureus 2023; 15:e49461. [PMID: 38152804 PMCID: PMC10751464 DOI: 10.7759/cureus.49461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/26/2023] [Indexed: 12/29/2023] Open
Abstract
Introduction It is important to establish criteria to define vascular cognitive impairment (VCI) in India as VCI is an image-based diagnosis and magnetic resonance imaging (MRI) changes resulting from age with prevalent vascular risk factors may confound MRI interpretation. The objective of this study was to establish normative community data for MRI volumetry including white matter hyperintensity volume (WMHV), correlated with age-stratified cognitive scores and vascular risk factors (VRFs), in adults aged 40 years and above. Methods We screened 2651 individuals without known neurological morbidity, living in Mumbai and nearby rural areas, using validated Marathi translations of Kolkata Cognitive Battery (KCB) and geriatric depression score (GDS). We stratified 1961 persons with GDS ≤9 by age and cognitive score, and randomly selected 10% from each subgroup for MRI brain volumetry. Crude volumes were standardized to reflect percentage of intracranial volume. Results MRI volumetry studies were done in 199 individuals (F/M = 90/109; 73 with body mass index (BMI) ≥25; 44 hypertensives; 29 diabetics; mean cognitive score 76.3). Both grey and white matter volumes decreased with increasing age. WMHV increased with age and hypertension. Grey matter volume (GMV) decreased with increasing WMHV. Positive predictors of cognition included standardized hippocampal volume (HCV), urban living, education, and BMI, while WMHV and age were negative predictors. Urban dwellers had higher cognitive scores than rural, and, paradoxically, smaller HCV. Conclusion In this study of MRI volumetry correlated with age, cognitive scores and VRFs, increasing age and WMHV predicted lower cognitive scores, whereas urban living and hippocampal volume predicted higher scores. Age and WMHV also correlated with decreasing GMV. Further study is warranted into sociodemographic and biological factors that mutually influence cognition and brain volumes, including nutritional and endocrine factors, especially at lower cognitive score bands. In this study, at the lower KCB score bins, the lack of laboratory data pertaining to nutritional and endocrine deficiencies is a drawback that reflects the logistical limitations of screening large populations at the community level. Our volumetric data which is age and cognition stratified, and takes into account the vascular risk factors associated, nevertheless constitutes important baseline data for the Indian population. Our findings could possibly contribute to the formulation of baseline criteria for defining VCI in India and could help in early diagnosis and control of cognitive decline and its key risk factors.
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Affiliation(s)
- Uma Sundar
- Department of Medicine, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, IND
| | - Amita Mukhopadhyay
- Department of Hospital and Health Management, Institute of Health Management Research Bangalore, Bengaluru, IND
| | - Sheelakumari Raghavan
- Department of Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, IND
| | - Ipsita Debata
- Department of Community and Family Medicine, Kalinga Institute of Medical Sciences, Bhubaneswar, IND
| | - Ramshekhar N Menon
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, IND
| | - Chandrasekharan Kesavadas
- Department of Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, IND
| | - Nilesh Shah
- Department of Psychiatry, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, IND
| | - Balkrishna B Adsul
- Department of Community Medicine, Hinduhrudaysamrat Balasaheb Thackarey Medical College and Dr RN Cooper Municipal General Hospital, Mumbai, IND
| | - Anagha R Joshi
- Department of Radiology, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, IND
| | - Janardhan Tejas
- Department of Forensic Medicine and Toxicology, Karpaga Vinayaga Institute of Medical Sciences and Research Center, Chengalpattu, IND
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Gong Z, Bilgel M, Kiely M, Triebswetter C, Ferrucci L, Resnick SM, Spencer RG, Bouhrara M. Lower myelin content is associated with more rapid cognitive decline among cognitively unimpaired individuals. Alzheimers Dement 2023; 19:3098-3107. [PMID: 36720000 PMCID: PMC10387505 DOI: 10.1002/alz.12968] [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: 10/15/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 02/01/2023]
Abstract
INTRODUCTION The influence of myelination on longitudinal changes in cognitive performance remains unclear. METHODS For each participant (N = 123), longitudinal cognitive scores were calculated. Myelin content was probed using myelin water fraction (MWF) or longitudinal relaxation rate (R1 ); both are MRI measures sensitive to myelin, with MWF being specific. RESULTS Lower MWF was associated with steeper declines in executive function (p < .02 in all regions) and lower R1 was associated with steeper declines in verbal fluency (p < .03 in all regions). Additionally, lower R1 was associated with steeper declines in executive function (p < .02 in all regions) and memory (p < .04 in occipital and cerebral white matter) but did not survive Bonferroni correction. DISCUSSION We demonstrate significant relationships between myelin content and the rates of change in cognitive performance among cognitively normal individuals. These findings highlight the importance of myelin in cognitive functioning and suggest MWF and R1 as imaging biomarkers to predict cognitive changes.
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Affiliation(s)
- Zhaoyuan Gong
- Magnetic Resonance Physics of Aging and Dementia (MRPAD) Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Murat Bilgel
- Brain Aging and Behavior Section, NIA, NIH, Baltimore, Maryland, USA
| | - Matthew Kiely
- Magnetic Resonance Physics of Aging and Dementia (MRPAD) Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Curtis Triebswetter
- Magnetic Resonance Physics of Aging and Dementia (MRPAD) Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, NIA, NIH, Baltimore, Maryland, USA
| | - Susan M Resnick
- Brain Aging and Behavior Section, NIA, NIH, Baltimore, Maryland, USA
| | - Richard G Spencer
- Magnetic Resonance Imaging and Spectroscopy Section, NIA, NIH, Baltimore, Maryland, USA
| | - Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia (MRPAD) Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA
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Schwarz G, Kanber B, Prados F, Browning S, Simister R, Jäger HR, Ambler G, Gandini Wheeler-Kingshott CAM, Werring DJ. Whole-brain diffusion tensor imaging predicts 6-month functional outcome in acute intracerebral haemorrhage. J Neurol 2023; 270:2640-2648. [PMID: 36806785 PMCID: PMC10129992 DOI: 10.1007/s00415-023-11592-7] [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: 10/22/2022] [Revised: 01/25/2023] [Accepted: 01/27/2023] [Indexed: 02/23/2023]
Abstract
INTRODUCTION Small vessel disease (SVD) causes most spontaneous intracerebral haemorrhage (ICH) and is associated with widespread microstructural brain tissue disruption, which can be quantified via diffusion tensor imaging (DTI) metrics: mean diffusivity (MD) and fractional anisotropy (FA). Little is known about the impact of whole-brain microstructural alterations after SVD-related ICH. We aimed to investigate: (1) association between whole-brain DTI metrics and functional outcome after ICH; and (2) predictive ability of these metrics compared to the pre-existing ICH score. METHODS Sixty-eight patients (38.2% lobar) were retrospectively included. We assessed whole-brain DTI metrics (obtained within 5 days after ICH) in cortical and deep grey matter and white matter. We used univariable logistic regression to assess the associations between DTI and clinical-radiological variables and poor outcome (modified Rankin Scale > 2). We determined the optimal predictive variables (via LASSO estimation) in: model 1 (DTI variables only), model 2 (DTI plus non-DTI variables), model 3 (DTI plus ICH score). Optimism-adjusted C-statistics were calculated for each model and compared (likelihood ratio test) against the ICH score. RESULTS Deep grey matter MD (OR 1.04 [95% CI 1.01-1.07], p = 0.010) and white matter MD (OR 1.11 [95% CI 1.01-1.23], p = 0.044) were associated (univariate analysis) with poor outcome. Discrimination values for model 1 (0.67 [95% CI 0.52-0.83]), model 2 (0.71 [95% CI 0.57-0.85) and model 3 (0.66 [95% CI 0.52-0.82]) were all significantly higher than the ICH score (0.62 [95% CI 0.49-0.75]). CONCLUSION Our exploratory study suggests that whole-brain microstructural disruption measured by DTI is associated with poor 6-month functional outcome after SVD-related ICH. Whole-brain DTI metrics performed better at predicting recovery than the existing ICH score.
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Affiliation(s)
- G Schwarz
- Neurologia-Stroke Unit ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, London, UK
| | - B Kanber
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, UCL, London, UK
- National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK
| | - F Prados
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, UCL, London, UK
- National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK
- E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - S Browning
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, London, UK
| | - R Simister
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, London, UK
| | - H R Jäger
- Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London, UK
| | - G Ambler
- Department of Statistical Science, University College London, Gower Street, London, UK
| | - C A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - D J Werring
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, London, UK.
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Neural signatures for the n-back task with different loads: An event-related potential study. Biol Psychol 2023; 177:108485. [PMID: 36621664 DOI: 10.1016/j.biopsycho.2023.108485] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 12/31/2022] [Accepted: 01/04/2023] [Indexed: 01/07/2023]
Abstract
The n-back task is widely used in working memory (WM) research. However, it remains unclear how the electrophysiological correlates of WM processes, the P2, N2, P300, and negative slow wave (NSW), are affected by differences in load. Specifically, while previous work has examined the P300, less attention has been paid to the other components assessing the load of the n-back paradigm. The present study aims to investigate whether other sub-processes in WM (such as inhibitory control) are as sensitive to n-back load changes as the update process by observing changes in the above event-related potential (ERP) components. The results showed poorer behavioral performance with increasing WM load. Greater NSW and smaller P300 amplitudes were elicited by n-back task with a higher load compared to that with lower load. In contrast, there was no significant effect of the n-back load on the amplitudes of P2 and N2. These findings suggest that the updating process and the maintenance process are sensitive to the n-back load change. Therefore, changes in the updating and maintenance processes should be considered when using the n-back task to manipulate the WM load in experiments. The present study may contribute to the understanding of the complexity of WM loads. Additionally, a theoretical basis for follow-up research to explore ways of improving WM performance with high load is provided.
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Clancy U, Radakovic R, Doubal F, Hernández MDCV, Maniega SM, Taylor AM, Corley J, Chappell FM, Russ TC, Cox SR, Bastin ME, Deary IJ, Wardlaw JM. Are neuropsychiatric symptoms a marker of small vessel disease progression in older adults? Evidence from the Lothian Birth Cohort 1936. Int J Geriatr Psychiatry 2023; 38:e5855. [PMID: 36490272 PMCID: PMC10108049 DOI: 10.1002/gps.5855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 11/21/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Neuropsychiatric symptoms could form part of an early cerebral small vessel disease prodrome that is detectable before stroke or dementia onset. We aimed to identify whether apathy, depression, anxiety, and subjective memory complaints associate with longitudinal white matter hyperintensity (WMH) progression. METHODS Community-dwelling older adults from the observational Lothian Birth Cohort 1936 attended three visits at mean ages 73, 76, and 79 years, repeating MRI, Mini-Mental State Examination, neuropsychiatric (Dimensional Apathy Scale, Hospital Anxiety and Depression Scale), and subjective memory symptoms. We ran regression and mixed-effects models for symptoms and normalised WMH volumes (cube root of WMH:ICV × 10). RESULTS At age 73, 76, and 79, m = 672, n = 476, and n = 382 participants attended MRI respectively. Worse apathy at age 79 was associated with WMH volume increase (β = 0.27, p = 0.04) in the preceding 6 years. A 1SD increase in apathy score at age 79 associated with a 0.17 increase in WMH (β = 0.17 normalised WMH percent ICV, p = 0.009). In apathy subscales, executive (β = 0.13, p = 0.05) and emotional (β = 0.13, p = 0.04) scores associated with increasing WMH more than initiation scores (β = 0.11, p = 0.08). Increasing WMH also associated with age (β = 0.40, p = 0.002) but not higher depression (β = -0.01, p = 0.78), anxiety (β = 0.05, p = 0.13) scores, or subjective memory complaints (β = 1.12, p = 0.75). CONCLUSIONS Apathy independently associates with preceding longitudinal WMH progression, while depression, anxiety, and subjective memory complaints do not. Patients with apathy should be considered for enrolment to small vessel disease trials.
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Affiliation(s)
- Una Clancy
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute at The University of EdinburghEdinburghUK
| | - Ratko Radakovic
- Department of Clinical Psychology and Psychological TherapiesUniversity of East AngliaNorwichUK
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
- Alzheimer Scotland Dementia Research CentreUniversity of EdinburghEdinburghUK
- Euan MacDonald Centre for MND ResearchUniversity of EdinburghEdinburghUK
| | - Fergus Doubal
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute at The University of EdinburghEdinburghUK
| | - Maria del C. Valdés Hernández
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute at The University of EdinburghEdinburghUK
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Susana Muñoz Maniega
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute at The University of EdinburghEdinburghUK
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Adele M. Taylor
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
| | - Janie Corley
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
| | - Francesca M. Chappell
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute at The University of EdinburghEdinburghUK
| | - Tom C. Russ
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
- Alzheimer Scotland Dementia Research CentreUniversity of EdinburghEdinburghUK
- Division of PsychiatryCentre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Simon R. Cox
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Mark E. Bastin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute at The University of EdinburghEdinburghUK
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Ian J. Deary
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
- Alzheimer Scotland Dementia Research CentreUniversity of EdinburghEdinburghUK
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute at The University of EdinburghEdinburghUK
- Lothian Birth CohortsDepartment of PsychologyUniversity of EdinburghEdinburghUK
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
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Röhrig L, Sperber C, Bonilha L, Rorden C, Karnath HO. Right hemispheric white matter hyperintensities improve the prediction of spatial neglect severity in acute stroke. Neuroimage Clin 2022; 36:103265. [PMID: 36451368 PMCID: PMC9723300 DOI: 10.1016/j.nicl.2022.103265] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/12/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022]
Abstract
White matter hyperintensities (WMH) are frequently observed in brain scans of elderly people. They are associated with an increased risk of stroke, cognitive decline, and dementia. However, it is unknown yet if measures of WMH provide information that improve the understanding of poststroke outcome compared to only state-of-the-art stereotaxic structural lesion data. We implemented high-dimensional machine learning models, based on support vector regression, to predict the severity of spatial neglect in 103 acute right hemispheric stroke patients. We found that (1) the additional information of right hemispheric or bilateral voxel-based topographic WMH extent indeed yielded a significant improvement in predicting acute neglect severity (compared to the voxel-based stroke lesion map alone). (2) Periventricular WMH appeared more relevant for prediction than deep subcortical WMH. (3) Among different measures of WMH, voxel-based maps as measures of topographic extent allowed more accurate predictions compared to the use of traditional ordinally assessed visual rating scales (Fazekas-scale, Cardiovascular Health Study-scale). In summary, topographic WMH appear to be a valuable clinical imaging biomarker for predicting the severity of cognitive deficits and bears great potential for rehabilitation guidance of acute stroke patients.
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Affiliation(s)
- Lisa Röhrig
- Division of Neuropsychology, Center of Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Christoph Sperber
- Division of Neuropsychology, Center of Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| | - Christopher Rorden
- Department of Psychology, University of South Carolina, Columbia, SC 29208, USA
| | - Hans-Otto Karnath
- Division of Neuropsychology, Center of Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany; Department of Psychology, University of South Carolina, Columbia, SC 29208, USA.
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Schilter M, Epstein A, Vynckier J, Mujanovic A, Belachew NF, Beyeler M, Siepen B, Goeldlin M, Scutelnic A, Seiffge DJ, Jung S, Gralla J, Dobrocky T, Arnold M, Kaesmacher J, Fischer U, Meinel TR. Chronic cerebral infarctions and white matter lesions link to long-term survival after a first ischemic event: A cohort study. J Neuroimaging 2022; 32:1134-1141. [PMID: 35922890 PMCID: PMC9804158 DOI: 10.1111/jon.13033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/15/2022] [Accepted: 07/23/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND AND PURPOSE To investigate the association of different phenotypes, count, and locations of chronic covert brain infarctions (CBI) with long-term mortality in patients with first-ever manifest acute ischemic stroke (AIS) or transient ischemic attack (TIA). Additionally, to analyze their potential interaction with white matter hyperintensities (WMH) and predictive value in addition to established mortality scores. METHODS Single-center cohort study including consecutive patients with first-ever AIS or TIA with available MRI imaging from January 2015 to December 2017. Blinded raters adjudicated CBI phenotypes and WMH (age-related white matter changes score) according to established definitions. We compared Cox regression models including prespecified established predictors of mortality using Harrell's C and likelihood ratio tests. RESULTS A total of 2236 patients (median [interquartile range] age: 71 [59-80] years, 43% female, National Institutes of Health Stroke Scale: 2 [1-6], median follow-up: 1436 days, 21% death during follow-up) were included. Increasing WMH (per point adjusted Hazard Ratio [aHR] = 1.29 [1.14-1.45]), but not CBI (aHR = 1.21 [0.99-1.49]), were independently associated with mortality. Neither CBI phenotype, count, nor location was associated with mortality and there was no multiplicative interaction between CBI and WMH (p > .1). As compared to patients without CBI or WMH, patients with moderate or severe WMH and additional CBI had the highest hazards of death (aHR = 1.62 [1.23-2.13]). The Cox regression model including CBI and WMH had a small but significant increment in Harrell's C when compared to the model including 14 clinical variables (0.831 vs. 0.827, p < .001). DISCUSSION WMH represent a strong surrogate biomarker of long-term mortality in first-ever manifest AIS or TIA patients. CBI phenotypes, count, and location seem less relevant. Incorporation of CBI and WMH slightly improves predictive capacity of established risk scores.
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Affiliation(s)
- Marina Schilter
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Alessandra Epstein
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Jan Vynckier
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Adnan Mujanovic
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Nebiyat Filate Belachew
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Morin Beyeler
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Bernhard Siepen
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Martina Goeldlin
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Adrian Scutelnic
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - David Julian Seiffge
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Simon Jung
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Jan Gralla
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Tomas Dobrocky
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Marcel Arnold
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Johannes Kaesmacher
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Urs Fischer
- Department of NeurologyBasel University Hospital, University of BaselBernSwitzerland
| | - Thomas Raphael Meinel
- Department of Neurology, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
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11
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Backhouse EV, Shenkin SD, McIntosh AM, Bastin ME, Whalley HC, Valdez Hernandez M, Muñoz Maniega S, Harris MA, Stolicyn A, Campbell A, Steele D, Waiter GD, Sandu AL, Waymont JMJ, Murray AD, Cox SR, de Rooij SR, Roseboom TJ, Wardlaw JM. Early life predictors of late life cerebral small vessel disease in four prospective cohort studies. Brain 2021; 144:3769-3778. [PMID: 34581779 PMCID: PMC8719837 DOI: 10.1093/brain/awab331] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 06/12/2021] [Accepted: 07/07/2021] [Indexed: 11/12/2022] Open
Abstract
Development of cerebral small vessel disease, a major cause of stroke and dementia, may be influenced by early life factors. It is unclear whether these relationships are independent of each other, of adult socio-economic status or of vascular risk factor exposures. We examined associations between factors from birth (ponderal index, birth weight), childhood (IQ, education, socio-economic status), adult small vessel disease, and brain volumes, using data from four prospective cohort studies: STratifying Resilience And Depression Longitudinally (STRADL) (n = 1080; mean age = 59 years); the Dutch Famine Birth Cohort (n = 118; mean age = 68 years); the Lothian Birth Cohort 1936 (LBC1936; n = 617; mean age = 73 years), and the Simpson's cohort (n = 110; mean age = 78 years). We analysed each small vessel disease feature individually and summed to give a total small vessel disease score (range 1-4) in each cohort separately, then in meta-analysis, adjusted for vascular risk factors and adult socio-economic status. Higher birth weight was associated with fewer lacunes [odds ratio (OR) per 100 g = 0.93, 95% confidence interval (CI) = 0.88 to 0.99], fewer infarcts (OR = 0.94, 95% CI = 0.89 to 0.99), and fewer perivascular spaces (OR = 0.95, 95% CI = 0.91 to 0.99). Higher childhood IQ was associated with lower white matter hyperintensity burden (OR per IQ point = 0.99, 95% CI 0.98 to 0.998), fewer infarcts (OR = 0.98, 95% CI = 0.97 to 0.998), fewer lacunes (OR = 0.98, 95% CI = 0.97 to 0.999), and lower total small vessel disease burden (OR = 0.98, 95% CI = 0.96 to 0.999). Low education was associated with more microbleeds (OR = 1.90, 95% CI = 1.33 to 2.72) and lower total brain volume (mean difference = -178.86 cm3, 95% CI = -325.07 to -32.66). Low childhood socio-economic status was associated with fewer lacunes (OR = 0.62, 95% CI = 0.40 to 0.95). Early life factors are associated with worse small vessel disease in later life, independent of each other, vascular risk factors and adult socio-economic status. Risk for small vessel disease may originate in early life and provide a mechanistic link between early life factors and risk of stroke and dementia. Policies investing in early child development may improve lifelong brain health and contribute to the prevention of dementia and stroke in older age.
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Affiliation(s)
- Ellen V Backhouse
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- MRC UK Dementia Research Institute at the University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Susan D Shenkin
- Geriatric Medicine, Usher Institute, The University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), Institute of Neuroscience and Psychology, Glasgow G12 8QB, UK
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Heather C Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Maria Valdez Hernandez
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), Institute of Neuroscience and Psychology, Glasgow G12 8QB, UK
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), Institute of Neuroscience and Psychology, Glasgow G12 8QB, UK
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Mathew A Harris
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Archie Campbell
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Douglas Steele
- Division of Imaging Sciences and Technology, Medical School, University of Dundee, Dundee, DD1 9SY, UK
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Anca-Larisa Sandu
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Jennifer M J Waymont
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), Institute of Neuroscience and Psychology, Glasgow G12 8QB, UK
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Simon R Cox
- Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Susanne R de Rooij
- Department of Epidemiology and Data Science, Amsterdam University, Medical Centres, University of Amsterdam, The Netherlands
| | - Tessa J Roseboom
- Department of Epidemiology and Data Science, Amsterdam University, Medical Centres, University of Amsterdam, The Netherlands
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- MRC UK Dementia Research Institute at the University of Edinburgh, Edinburgh, EH16 4SB, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), Institute of Neuroscience and Psychology, Glasgow G12 8QB, UK
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
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12
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Jiménez-Sánchez L, Hamilton OKL, Clancy U, Backhouse EV, Stewart CR, Stringer MS, Doubal FN, Wardlaw JM. Sex Differences in Cerebral Small Vessel Disease: A Systematic Review and Meta-Analysis. Front Neurol 2021; 12:756887. [PMID: 34777227 PMCID: PMC8581736 DOI: 10.3389/fneur.2021.756887] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/04/2021] [Indexed: 01/12/2023] Open
Abstract
Background: Cerebral small vessel disease (SVD) is a common cause of stroke, mild cognitive impairment, dementia and physical impairments. Differences in SVD incidence or severity between males and females are unknown. We assessed sex differences in SVD by assessing the male-to-female ratio (M:F) of recruited participants and incidence of SVD, risk factor presence, distribution, and severity of SVD features. Methods: We assessed four recent systematic reviews on SVD and performed a supplementary search of MEDLINE to identify studies reporting M:F ratio in covert, stroke, or cognitive SVD presentations (registered protocol: CRD42020193995). We meta-analyzed differences in sex ratios across time, countries, SVD severity and presentations, age and risk factors for SVD. Results: Amongst 123 relevant studies (n = 36,910 participants) including 53 community-based, 67 hospital-based and three mixed studies published between 1989 and 2020, more males were recruited in hospital-based than in community-based studies [M:F = 1.16 (0.70) vs. M:F = 0.79 (0.35), respectively; p < 0.001]. More males had moderate to severe SVD [M:F = 1.08 (0.81) vs. M:F = 0.82 (0.47) in healthy to mild SVD; p < 0.001], and stroke presentations where M:F was 1.67 (0.53). M:F did not differ for recent (2015-2020) vs. pre-2015 publications, by geographical region, or age. There were insufficient sex-stratified data to explore M:F and risk factors for SVD. Conclusions: Our results highlight differences in male-to-female ratios in SVD severity and amongst those presenting with stroke that have important clinical and translational implications. Future SVD research should report participant demographics, risk factors and outcomes separately for males and females. Systematic Review Registration: [PROSPERO], identifier [CRD42020193995].
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Affiliation(s)
- Lorena Jiménez-Sánchez
- Translational Neuroscience PhD Programme, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Olivia K. L. Hamilton
- Translational Neuroscience PhD Programme, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Dementia Research Centre in the UK Dementia Research Institute, Edinburgh, United Kingdom
| | - Una Clancy
- Edinburgh Dementia Research Centre in the UK Dementia Research Institute, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Ellen V. Backhouse
- Edinburgh Dementia Research Centre in the UK Dementia Research Institute, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Catriona R. Stewart
- Edinburgh Dementia Research Centre in the UK Dementia Research Institute, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael S. Stringer
- Edinburgh Dementia Research Centre in the UK Dementia Research Institute, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Fergus N. Doubal
- Edinburgh Dementia Research Centre in the UK Dementia Research Institute, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M. Wardlaw
- Edinburgh Dementia Research Centre in the UK Dementia Research Institute, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom
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13
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Ibitoye RT, Castro P, Desowska A, Cooke J, Edwards AE, Guven O, Arshad Q, Murdin L, Kaski D, Bronstein AM. Small vessel disease disrupts EEG postural brain networks in 'unexplained dizziness in the elderly'. Clin Neurophysiol 2021; 132:2751-2762. [PMID: 34583117 PMCID: PMC8559782 DOI: 10.1016/j.clinph.2021.07.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 06/15/2021] [Accepted: 07/25/2021] [Indexed: 11/28/2022]
Abstract
Unexplained dizziness in the elderly may result from
cerebral small vessel disease. Dizzy elderly patients differed from controls in EEG
power when standing. EEG power when standing correlated with subjective
(perceived) instability.
Objective To examine the hypothesis that small vessel disease
disrupts postural networks in older adults with unexplained dizziness in the
elderly (UDE). Methods Simultaneous electroencephalography and postural sway
measurements were undertaken in upright, eyes closed standing, and sitting
postures (as baseline) in 19 younger adults, 33 older controls and 36 older
patients with UDE. Older adults underwent magnetic resonance imaging to
determine whole brain white matter hyperintensity volumes, a measure of small
vessel disease. Linear regression was used to estimate the effect of instability
on electroencephalographic power and connectivity. Results Ageing increased theta and alpha desynchronisation on
standing. In older controls, delta and gamma power increased, and theta and
alpha power reduced with instability. Dizzy older patients had higher white
matter hyperintensity volumes and more theta desynchronisation during periods of
instability. White matter hyperintensity volume and delta power during periods
of instability were correlated, positively in controls but negatively in dizzy
older patients. Delta power correlated with subjective dizziness and
instability. Conclusions Neural resource demands of postural control increase
with age, particularly in patients with UDE, driven by small vessel
disease. Significance EEG correlates of postural control saturate in older
adults with UDE, offering a neuro-physiological basis to this common
syndrome.
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Affiliation(s)
- R T Ibitoye
- Neuro-otology Unit, Imperial College London, London, UK; The Computational, Cognitive and Clinical Neuroimaging Laboratory (C3NL), Imperial College London, London, UK
| | - P Castro
- Neuro-otology Unit, Imperial College London, London, UK
| | - A Desowska
- The Computational, Cognitive and Clinical Neuroimaging Laboratory (C3NL), Imperial College London, London, UK
| | - J Cooke
- Neuro-otology Unit, Imperial College London, London, UK
| | - A E Edwards
- Neuro-otology Unit, Imperial College London, London, UK
| | - O Guven
- Neuro-otology Unit, Imperial College London, London, UK
| | - Q Arshad
- Neuro-otology Unit, Imperial College London, London, UK; inAmind Laboratory, Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
| | - L Murdin
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - D Kaski
- Neuro-otology Unit, Imperial College London, London, UK; Department of Clinical and Movement Neurosciences, University College London, London, UK
| | - A M Bronstein
- Neuro-otology Unit, Imperial College London, London, UK.
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14
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Benveniste H, Nedergaard M. Cerebral small vessel disease: A glymphopathy? Curr Opin Neurobiol 2021; 72:15-21. [PMID: 34407477 DOI: 10.1016/j.conb.2021.07.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/09/2021] [Accepted: 07/14/2021] [Indexed: 12/23/2022]
Abstract
Small vessel disease (SVD) is a common instigator of dementia in the aging population. The hallmarks of SVD are enlargement of the perivascular spaces and white matter hyperintensities. The latter represents local fluid accumulation in white matter that either subsides or develops into lacunar infarcts. We here propose that failure of brain fluid transport-via the glymphatic system-plays a key role in initiation and progression of SVD. Our major case for this concept is that perivascular spaces are utilized as waterways for influx of cerebrospinal fluid. Stagnation of glymphatic transport may drive loss of brain fluid homeostasis leading to transient white matter edema, perivascular dilation, and ultimately demyelination. This review will discuss how glymphatic rodent studies of hypertension and diabetes have provided new insight into the pathogenesis of SVD.
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Affiliation(s)
- Helene Benveniste
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Maiken Nedergaard
- Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark; Center for Translational Neuromedicine, University of Rochester Medical Center, Rochester, NY, 14642, USA.
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15
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Porcu M, Cocco L, Cocozza S, Pontillo G, Operamolla A, Defazio G, Suri JS, Brunetti A, Saba L. The association between white matter hyperintensities, cognition and regional neural activity in healthy subjects. Eur J Neurosci 2021; 54:5427-5443. [PMID: 34327745 DOI: 10.1111/ejn.15403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 07/03/2021] [Accepted: 07/24/2021] [Indexed: 11/29/2022]
Abstract
White matter hyperintensities (WMH) are common findings that can be found in physiological ageing. Several studies suggest that the disruption of white matter tracts included in WMH could induce abnormal functioning of the respective linked cortical structures, with consequent repercussion on the cerebral functions, included the cognitive sphere. In this cross-sectional research, we analysed the effects of the total WMH burden (tWMHb) on resting-state functional magnetic resonance imaging (rs-fMRI) and cognition. Functional and structural MR data, as well as the scores of the trail making test subtests A (TMT-A) and B (TMT-B) of 75 healthy patients, were extracted from the public available Leipzig Study for Mind-Body-Emotion Interactions dataset. tWMHb was extracted from structural data. Spearman's correlation analyses were made for investigating correlations between WMHb and the scores of the cognitive tests. The fractional amplitude of low-frequency fluctuations (fALFF) method was applied for analysing the rs-fMRI data, adopting a multiple regression model for studying the effects of tWMHb on brain activity. Three different subanalyses were conducted using different statistical methods. We observed statistically significant correlations between WMHb and the scores of the cognitive tests. The fALFF analysis revealed that tWMHb is associated with the reduction of regional neural activity of several brain areas (in particular the prefrontal cortex, precuneus and cerebellar crus I/II). We conclude that our findings clarify better the relationships between WMH and cognitive impairment, evidencing that tWMHb is associated with impairments of the neurocognitive function in healthy subjects by inducing a diffuse reduction of the neural activity.
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Affiliation(s)
- Michele Porcu
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
| | - Luigi Cocco
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
| | - Sirio Cocozza
- Department of Neuroradiology, University of Napoli "Federico II", Napoli, Italy
| | - Giuseppe Pontillo
- Department of Neuroradiology, University of Napoli "Federico II", Napoli, Italy
| | | | - Giovanni Defazio
- Department of Neurology, University of Cagliari, Cagliari, Italy
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, California, USA
| | - Arturo Brunetti
- Department of Neuroradiology, University of Napoli "Federico II", Napoli, Italy
| | - Luca Saba
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
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16
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Hillary RF, Stevenson AJ, Cox SR, McCartney DL, Harris SE, Seeboth A, Higham J, Sproul D, Taylor AM, Redmond P, Corley J, Pattie A, Hernández MDCV, Muñoz-Maniega S, Bastin ME, Wardlaw JM, Horvath S, Ritchie CW, Spires-Jones TL, McIntosh AM, Evans KL, Deary IJ, Marioni RE. An epigenetic predictor of death captures multi-modal measures of brain health. Mol Psychiatry 2021; 26:3806-3816. [PMID: 31796892 PMCID: PMC8550950 DOI: 10.1038/s41380-019-0616-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 11/14/2019] [Accepted: 11/20/2019] [Indexed: 11/08/2022]
Abstract
Individuals of the same chronological age exhibit disparate rates of biological ageing. Consequently, a number of methodologies have been proposed to determine biological age and primarily exploit variation at the level of DNA methylation (DNAm). A novel epigenetic clock, termed 'DNAm GrimAge' has outperformed its predecessors in predicting the risk of mortality as well as many age-related morbidities. However, the association between DNAm GrimAge and cognitive or neuroimaging phenotypes remains unknown. We explore these associations in the Lothian Birth Cohort 1936 (n = 709, mean age 73 years). Higher DNAm GrimAge was strongly associated with all-cause mortality over the eighth decade (Hazard Ratio per standard deviation increase in GrimAge: 1.81, P < 2.0 × 10-16). Higher DNAm GrimAge was associated with lower age 11 IQ (β = -0.11), lower age 73 general cognitive ability (β = -0.18), decreased brain volume (β = -0.25) and increased brain white matter hyperintensities (β = 0.17). There was tentative evidence for a longitudinal association between DNAm GrimAge and cognitive decline from age 70 to 79. Sixty-nine of 137 health- and brain-related phenotypes tested were significantly associated with GrimAge. Adjusting all models for childhood intelligence attenuated to non-significance a small number of associations (12/69 associations; 6 of which were cognitive traits), but not the association with general cognitive ability (33.9% attenuation). Higher DNAm GrimAge associates with lower cognitive ability and brain vascular lesions in older age, independently of early-life cognitive ability. This epigenetic predictor of mortality associates with different measures of brain health and may aid in the prediction of age-related cognitive decline.
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Affiliation(s)
- Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Anne Seeboth
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Jon Higham
- Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Duncan Sproul
- Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Adele M Taylor
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Janie Corley
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Alison Pattie
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Maria Del C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Susana Muñoz-Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, USA
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Tara L Spires-Jones
- UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
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17
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Tang X, Jiang L, Luo Y, Fan H, Song L, Liu P, Chen Y. Leukoaraiosis and acute ischemic stroke. Eur J Neurosci 2021; 54:6202-6213. [PMID: 34331366 DOI: 10.1111/ejn.15406] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/26/2021] [Accepted: 07/26/2021] [Indexed: 11/30/2022]
Abstract
Ischaemic stroke is characterized by high morbidity, high disability rate, high mortality and high recurrence rate, which can have a grave impact on the quality of life of the patients and consequently becomes an economic burden on their families and society. With the developments in imaging technology in recent years, patients with acute cerebral infarction are predominantly more likely to be diagnosed with leukoaraiosis (LA). LA is a common degenerative disease of the nervous system, which is related to cognitive decline, depression, abnormal gait, ischaemic stroke and atherosclerosis. The aetiology of LA is not clear and there is no gold standard for imaging assessment. Related studies have shown that LA has an adverse effect on the prognosis of cerebral infarction, but some experts have contrary beliefs. Hence, we undertook the present review of the literature on the mechanism and the effect of LA on the prognosis of patients with acute ischaemic stroke.
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Affiliation(s)
- Xiaojia Tang
- Department of Rehabilitation Medicine, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou City, China
| | - Li Jiang
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou City, China
| | - Yuhan Luo
- Health Management Center, People's Hospital of Deyang City, Deyang City, China
| | - Hongyang Fan
- Department of Neurology, Xuzhou Medical University Affiliated Hospital of Lianyungang, Lianyungang City, China
| | - Lilong Song
- Department of Neurology, Shanghai Fourth People's Hospital, Shanghai City, China
| | - Peipei Liu
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou City, China
| | - Yingzhu Chen
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou City, China
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18
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Buyanova IS, Arsalidou M. Cerebral White Matter Myelination and Relations to Age, Gender, and Cognition: A Selective Review. Front Hum Neurosci 2021; 15:662031. [PMID: 34295229 PMCID: PMC8290169 DOI: 10.3389/fnhum.2021.662031] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 06/02/2021] [Indexed: 12/22/2022] Open
Abstract
White matter makes up about fifty percent of the human brain. Maturation of white matter accompanies biological development and undergoes the most dramatic changes during childhood and adolescence. Despite the advances in neuroimaging techniques, controversy concerning spatial, and temporal patterns of myelination, as well as the degree to which the microstructural characteristics of white matter can vary in a healthy brain as a function of age, gender and cognitive abilities still exists. In a selective review we describe methods of assessing myelination and evaluate effects of age and gender in nine major fiber tracts, highlighting their role in higher-order cognitive functions. Our findings suggests that myelination indices vary by age, fiber tract, and hemisphere. Effects of gender were also identified, although some attribute differences to methodological factors or social and learning opportunities. Findings point to further directions of research that will improve our understanding of the complex myelination-behavior relation across development that may have implications for educational and clinical practice.
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Affiliation(s)
- Irina S. Buyanova
- Neuropsy Lab, HSE University, Moscow, Russia
- Center for Language and Brain, HSE University, Moscow, Russia
| | - Marie Arsalidou
- Neuropsy Lab, HSE University, Moscow, Russia
- Cognitive Centre, Sirius University of Science and Technology, Sochi, Russia
- Department of Psychology, York University, Toronto, ON, Canada
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19
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Valdés Hernández MDC, Ballerini L, Glatz A, Muñoz Maniega S, Gow AJ, Bastin ME, Starr JM, Deary IJ, Wardlaw JM. Perivascular spaces in the centrum semiovale at the beginning of the 8th decade of life: effect on cognition and associations with mineral deposition. Brain Imaging Behav 2021; 14:1865-1875. [PMID: 31250262 PMCID: PMC7572330 DOI: 10.1007/s11682-019-00128-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Brain iron deposits (IDs) are indicative of microvessel dysfunction which may predispose to small vessel disease (SVD) brain damage and worsen cognition later in life. Visible perivascular spaces in the centrum semiovale (CSO-PVS) are SVD features linked with microvessel dysfunction. We examined possible associations of CSO-PVS volume and count with brain IDs and cognitive abilities in 700 community-dwelling individuals from the Lothian Birth Cohort 1936 who underwent detailed cognitive testing and multimodal brain MRI at mean age 72.7 years. Brain IDs were assessed automatically followed by manual editing. PVS were automatically assessed in the centrum semiovale and deep corona radiata supraventricular. General factors of overall cognitive function (g), processing speed (g-speed) and memory (g-memory) were used in the analyses. Median (IQR) volumes of IDs and CSO-PVS expressed as a percentage of intracranial volume were 0.0021 (0.011) and 0.22 (0.13)% respectively. Median count of CSO-PVS was 410 (IQR = 201). Total volumes of CSO-PVS and ID, adjusted for head size, were correlated (Spearman ρ = 0.13, p < 0.001). CSO-PVS volume, despite being correlated with all three cognitive measures, was only associated with g-memory (B = -114.5, SE = 48.35, p = 0.018) in general linear models, adjusting for age, sex, vascular risk factors, childhood intelligence and white matter hyperintensity volume. The interaction of CSO-PVS count with diabetes (B = -0.0019, SE = 0.00093, p = 0.041) and volume with age (B = 1.57, SE = 0.67, p = 0.019) were also associated with g-memory. Linear regression models did not replicate these associations. Therefore, it does not seem that CSO-PVS burden is directly associated with general cognitive ability in older age.
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Affiliation(s)
- Maria Del C Valdés Hernández
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK. .,Dementia Research Institute, University of Edinburgh, 49 Little France Crescent, Chancellor's Building FU-427, Edinburgh, EH16 4SB, UK. .,Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK. .,Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh Campus, David Brewster Building (Room 2.63A), Edinburgh, EH14 4AS, UK.
| | - Lucia Ballerini
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK.,Dementia Research Institute, University of Edinburgh, 49 Little France Crescent, Chancellor's Building FU-427, Edinburgh, EH16 4SB, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Andreas Glatz
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK
| | - Susana Muñoz Maniega
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK.,Dementia Research Institute, University of Edinburgh, 49 Little France Crescent, Chancellor's Building FU-427, Edinburgh, EH16 4SB, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Alan J Gow
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh Campus, David Brewster Building (Room 2.63A), Edinburgh, EH14 4AS, UK
| | - Mark E Bastin
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Alzheimer Scotland Dementia Research Centre, Department of Psychology (Room G24), University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Joanna M Wardlaw
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK.,Dementia Research Institute, University of Edinburgh, 49 Little France Crescent, Chancellor's Building FU-427, Edinburgh, EH16 4SB, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Row Fogo Centre for Ageing and the Brain, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK
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20
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Liu J, Ke X, Lai Q. Increased tortuosity of bilateral distal internal carotid artery is associated with white matter hyperintensities. Acta Radiol 2021; 62:515-523. [PMID: 32551801 DOI: 10.1177/0284185120932386] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Although the pathophysiology of white matter hyperintensities remains unclear, we can recently explore the possible relationship with white matter hyperintensities by using quantitative parameter. PURPOSE To demonstrate the relationship between bilateral distal internal carotid arterial tortuosity and total brain white matter hyperintensities volume in elderly individuals. MATERIAL AND METHODS A total of 345 patients (age > 65 years) with brain magnetic resonance (MR) examinations were retrospectively included (44.1% men; mean age = 72.1 ± 6.25 years; 55.9% ≥ 70 years). We measured the Tortuosity Index (TI) of the bilateral distal internal carotid artery and basilar artery on MR angiography imaging, and white matter hyperintensities volume on fluid-attenuated inversion recovery MR sequence. Multiple linear regression was used to assess the association of the TI with quantitatively derived brain white matter hyperintensity volume, after adjusting for demographics (age, sex), vascular risk factors (hypertension, diabetes, heart disease), and vessel diameters, total intracranial volume (TIV). RESULTS Increased tortuosity of bilateral distal internal carotid artery was associated with greater burden of white matter hyperintensity volume (right: β = 11.223, P = 0.016; left: β = 20.701, P < 0.001). This relationship was independent of age and hypertension, both of which have been considered the strongest risk factors for white matter hyperintensities. CONCLUSION Our results suggest that tortuosity of the bilateral distal internal carotid artery is associated with white matter hyperintensities, independent of age and hypertension.
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Affiliation(s)
- Jiyang Liu
- Department of Medical Imaging, The Second Affiliated Hospital of Fujian Medical University, Quanzhou City, Fujian Province, PR China
| | - Xiaoting Ke
- Department of Medical Imaging, The Second Affiliated Hospital of Fujian Medical University, Quanzhou City, Fujian Province, PR China
| | - Qingquan Lai
- Department of Medical Imaging, The Second Affiliated Hospital of Fujian Medical University, Quanzhou City, Fujian Province, PR China
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21
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Sanderson-Cimino M, Panizzon MS, Elman JA, Tu X, Gustavson DE, Puckett O, Cross K, Notestine R, Hatton SN, Eyler LT, McEvoy LK, Hagler DJ, Neale MC, Gillespie NA, Lyons MJ, Franz CE, Fennema-Notestine C, Kremen WS. Periventricular and deep abnormal white matter differ in associations with cognitive performance at midlife. Neuropsychology 2021; 35:252-264. [PMID: 33970659 PMCID: PMC8500190 DOI: 10.1037/neu0000718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Objective: Abnormal white matter (AWM) on magnetic resonance imaging is associated with cognitive performance in older adults. We explored cognitive associations with AWM during late-midlife. Method: Participants were community-dwelling men (n = 242; M = 61.90 years; range = 56-66). Linear-mixed effects regression models examined associations of total, periventricular, and deep AWM with cognitive performance, controlling for multiple comparisons. Models considering specific cognitive domains controlled for current general cognitive ability (GCA). We hypothesized that total AWM would be associated with worse processing speed, executive function, and current GCA; deep AWM would correlate with GCA and periventricular AWM would relate to specific cognitive abilities. We also assessed the potential influence of cognitive reserve by examining a moderation effect of early life (mean age of 20) cognition. Results: Greater total and deep AWM were associated with poorer current GCA. Periventricular AWM was associated with worse executive function, working memory, and episodic memory. When periventricular and deep AWM were modeled simultaneously, both retained their respective significant associations with cognitive performance. Cognitive reserve did not moderate associations. Conclusions: Our findings suggest that AWM contributes to poorer cognitive function in late-midlife. Examining only total AWM may obscure the potential differential impact of regional AWM. Separating total AWM into subtypes while controlling for current GCA revealed a dissociation in relationships with cognitive performance; deep AWM was associated with nonspecific cognitive ability whereas periventricular AWM was associated with specific frontal-related abilities and memory. Management of vascular or other risk factors that may increase the risk of AWM should begin during or before early late-midlife. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Mark Sanderson-Cimino
- Joint Doctoral Program in Clinical Psychology, San Diego State/University of California
- Center for Behavior Genetics of Aging, University of California
| | - Matthew S. Panizzon
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
| | - Jeremy A. Elman
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
| | - Xin Tu
- Family Medicine and Public Health, University of California
| | - Daniel E. Gustavson
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
- Department of Medicine, Vanderbilt University Medical Center
| | - Olivia Puckett
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
| | | | - Randy Notestine
- Department of Psychiatry University of California
- Computational and Applied Statistics Laboratory (CASL) at the San Diego Supercomputer Center
| | - Sean N Hatton
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
- Department of Neurosciences, University of California
| | - Lisa T. Eyler
- Department of Psychiatry University of California
- Mental Illness Research, Education, And Clinical Center, Veterans Affairs San Diego Healthcare System
| | - Linda K. McEvoy
- Department of Radiology, University of California, San Diego
| | | | - Michael C. Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University
| | - Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University
| | - Carol E. Franz
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
| | - Christine Fennema-Notestine
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
- Department of Radiology, University of California, San Diego
| | - William S. Kremen
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
- Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System
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22
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Hostettler IC, Schwarz G, Ambler G, Wilson D, Banerjee G, Seiffge DJ, Shakeshaft C, Lunawat S, Cohen H, Yousry TA, Al-Shahi Salman R, Lip GYH, Brown MM, Muir KW, Houlden H, Jäger HR, Werring DJ. Cerebral Small Vessel Disease and Functional Outcome Prediction After Intracerebral Hemorrhage. Neurology 2021; 96:e1954-e1965. [PMID: 33627495 DOI: 10.1212/wnl.0000000000011746] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 01/08/2021] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To determine whether CT-based cerebral small vessel disease (SVD) biomarkers are associated with 6-month functional outcome after intracerebral hemorrhage (ICH) and whether these biomarkers improve the performance of the preexisting ICH prediction score. METHODS We included 864 patients with acute ICH from a multicenter, hospital-based prospective cohort study. We evaluated CT-based SVD biomarkers (white matter hypodensities [WMH], lacunes, brain atrophy, and a composite SVD burden score) and their associations with poor 6-month functional outcome (modified Rankin Scale score >2). The area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow test were used to assess discrimination and calibration of the ICH score with and without SVD biomarkers. RESULTS In multivariable models (adjusted for ICH score components), WMH presence (odds ratio [OR] 1.52, 95% confidence interval [CI] 1.12-2.06), cortical atrophy presence (OR 1.80, 95% CI 1.19-2.73), deep atrophy presence (OR 1.66, 95% CI 1.17-2.34), and severe atrophy (either deep or cortical) (OR 1.94, 95% CI 1.36-2.74) were independently associated with poor functional outcome. For the revised ICH score, the AUROC was 0.71 (95% CI 0.68-0.74). Adding SVD markers did not significantly improve ICH score discrimination; for the best model (adding severe atrophy), the AUROC was 0.73 (95% CI 0.69-0.76). These results were confirmed when lobar and nonlobar ICH were considered separately. CONCLUSIONS The ICH score has acceptable discrimination for predicting 6-month functional outcome after ICH. CT biomarkers of SVD are associated with functional outcome, but adding them does not significantly improve ICH score discrimination. TRIAL REGISTRATION INFORMATION ClinicalTrials.gov Identifier: NCT02513316.
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Affiliation(s)
- Isabel C Hostettler
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Ghil Schwarz
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Gareth Ambler
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Duncan Wilson
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Gargi Banerjee
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - David J Seiffge
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Clare Shakeshaft
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Surabhika Lunawat
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Hannah Cohen
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Tarek A Yousry
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Rustam Al-Shahi Salman
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Gregory Y H Lip
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Martin M Brown
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Keith W Muir
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Henry Houlden
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - Hans Rolf Jäger
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London
| | - David J Werring
- From the Stroke Research Centre (I.C.H., G.S., D.W., G.B., D.J.S., C.S., S.L., M.M.B., D.J.W.), University College London, Queen Square Institute of Neurology; Department of Neurology (G.S.), Stroke Unit, San Raffaele Hospital, Milan, Italy; Department of Statistical Science (G.A.), University College London, Gower Street, UK; Department of Neurology and Stroke Center (D.J.S.), Inselspital, Bern, Switzerland; Haemostasis Research Unit (H.C.), Department of Haematology, University College London, Chenies Mews; Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit (T.A.Y., H.R.J.), Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London; Centre for Clinical Brain Sciences (R.A.-S.S.), School of Clinical Sciences, University of Edinburgh; Liverpool Centre for Cardiovascular Science (G.Y.H.L.), Liverpool Heart and Chest Hospital, University of Liverpool; Institute of Neuroscience & Psychology (K.W.M.), University of Glasgow, Queen Elizabeth University Hospital, Glasgow; and Department of Molecular Neuroscience (H.H.), UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery, Queen Square, London.
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23
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Venkatesh A, Stark SM, Stark CEL, Bennett IJ. Age- and memory- related differences in hippocampal gray matter integrity are better captured by NODDI compared to single-tensor diffusion imaging. Neurobiol Aging 2020; 96:12-21. [PMID: 32905951 PMCID: PMC7722017 DOI: 10.1016/j.neurobiolaging.2020.08.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 07/24/2020] [Accepted: 08/03/2020] [Indexed: 12/30/2022]
Abstract
Single-tensor diffusion imaging (DTI) has traditionally been used to assess integrity of white matter. For example, we previously showed that integrity of limbic white matter tracts declines in healthy aging and relates to episodic memory performance. However, multi-compartment diffusion models may be more informative about microstructural properties of gray matter. The current study examined hippocampal gray matter integrity using both single-tensor and multi-compartment (neurite orientation dispersion and density imaging, NODDI) diffusion imaging. Younger (20-38 years) and older (59-84 years) adults also completed the Mnemonic Similarity Task to measure mnemonic discrimination performance. Results revealed age-related declines in both single-tensor (lower fractional anisotropy, higher mean diffusivity) and multi-compartment (higher restricted, hindered and free diffusion) measures of hippocampal gray matter integrity. As expected, NODDI measures (hindered and free diffusion) captured more age-related variance than DTI measures. Moreover, mnemonic discrimination of highly similar lure items in memory was related to hippocampal gray matter integrity in younger but not older adults. These findings support the notion that age-related differences in gray matter integrity are better captured by multi-compartment versus single-tensor diffusion models and show that the relationship between mnemonic discrimination and hippocampal gray matter integrity is moderated by age.
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Affiliation(s)
- Anu Venkatesh
- Department of Neuroscience, University of California Riverside, Riverside, CA, USA.
| | - Shauna M Stark
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, USA
| | - Craig E L Stark
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, USA
| | - Ilana J Bennett
- Department of Neuroscience, University of California Riverside, Riverside, CA, USA; Department of Psychology, University of California Riverside, Riverside, CA, USA
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24
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Zarnani K, Smith SM, Alfaro-Almagro F, Fagerlund B, Lauritzen M, Rostrup E, Nichols TE. Discovering correlates of age-related decline in a healthy late-midlife male birth cohort. Aging (Albany NY) 2020; 12:16709-16743. [PMID: 32913141 PMCID: PMC7521526 DOI: 10.18632/aging.103345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 05/01/2020] [Indexed: 01/24/2023]
Abstract
Studies exploring age-related brain and cognitive change have identified substantial heterogeneity among individuals, but the underlying reasons for the differential trajectories remain largely unknown. We investigated cross-sectional and longitudinal associations between brain-imaging phenotypes (IDPs) and cognitive ability, and how these relations may be modified by common risk and protective factors. Participants were recruited from the 1953 Danish Male Birth Cohort (N=123), a longitudinal study of cognitive and brain ageing. Childhood IQ and socio-demographic factors are available for these participants who have been assessed regularly on multiple IDPs and behavioural factors in midlife. Using Pearson correlations and canonical correlation analysis (CCA), we explored the relation between 454 IDPs and 114 behavioural variables. CCA identified a single mode of population covariation coupling cross-subject longitudinal changes in brain structure to changes in cognitive performance and to a range of age-related covariates (r=0.92, Pcorrected < 0.001). Specifically, this CCA-mode indicated that; decreases in IQ and speed assessed tasks, higher rates of familial myocardial infarct, less physical activity, and poorer mental health are associated with larger decreases in whole brain grey matter and white matter. We found no evidence supporting the role of baseline scores as predictors of impending brain and behavioural change in late-midlife.
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Affiliation(s)
- Kiyana Zarnani
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Glostrup, Denmark
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Birgitte Fagerlund
- Center for Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Lauritzen
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Neurophysiology, Rigshospitalet-Glostrup, Denmark
| | - Egill Rostrup
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
- Center for Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Denmark
| | - Thomas E. Nichols
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Big Data Institute, Li Ka Shing, Centre For Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK
- Department of Statistics, University of Warwick, Coventry, UK
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25
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Elliott ML. MRI-based biomarkers of accelerated aging and dementia risk in midlife: how close are we? Ageing Res Rev 2020; 61:101075. [PMID: 32325150 DOI: 10.1016/j.arr.2020.101075] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/10/2020] [Accepted: 04/15/2020] [Indexed: 01/18/2023]
Abstract
The global population is aging, leading to an increasing burden of age-related neurodegenerative disease. Efforts to intervene against age-related dementias in older adults have generally proven ineffective. These failures suggest that a lifetime of brain aging may be difficult to reverse once widespread deterioration has occurred. To test interventions in younger populations, biomarkers of brain aging are needed that index subtle signs of accelerated brain deterioration that are part of the putative pathway to dementia. Here I review potential MRI-based biomarkers that could connect midlife brain aging to later life dementia. I survey the literature with three questions in mind, 1) Does the biomarker index age-related changes across the lifespan? 2) Does the biomarker index cognitive ability and cognitive decline? 3) Is the biomarker sensitive to known risk factors for dementia? I find that while there is preliminary support for some midlife MRI-based biomarkers for accelerated aging, the longitudinal research that would best answer these questions is still in its infancy and needs to be further developed. I conclude with suggestions for future research.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology and Neuroscience, Duke University, 2020 West Main Street, Suite 030, Durham, NC, 27701, USA.
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26
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d'Arbeloff T, Elliott ML, Knodt AR, Melzer TR, Keenan R, Ireland D, Ramrakha S, Poulton R, Anderson T, Caspi A, Moffitt TE, Hariri AR. White matter hyperintensities are common in midlife and already associated with cognitive decline. Brain Commun 2019; 1:fcz041. [PMID: 31894208 PMCID: PMC6928390 DOI: 10.1093/braincomms/fcz041] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 09/25/2019] [Accepted: 10/23/2019] [Indexed: 01/04/2023] Open
Abstract
White matter hyperintensities proliferate as the brain ages and are associated with increased risk for cognitive decline as well as Alzheimer’s disease and related dementias. As such, white matter hyperintensities have been targeted as a surrogate biomarker in intervention trials with older adults. However, it is unclear at what stage of aging white matter hyperintensities begin to relate to cognition and if they may be a viable target for early prevention. In the Dunedin Study, a population-representative cohort followed since birth, we measured white matter hyperintensities in 843 45-year-old participants using T2-weighted magnetic resonance imaging and we assessed cognitive decline from childhood to midlife. We found that white matter hyperintensities were common at age 45 and that white matter hyperintensity volume was modestly associated with both lower childhood (ß = −0.08, P = 0.013) and adult IQ (ß=−0.15, P < 0.001). Moreover, white matter hyperintensity volume was associated with greater cognitive decline from childhood to midlife (ß=−0.09, P < 0.001). Our results demonstrate that a link between white matter hyperintensities and early signs of cognitive decline is detectable decades before clinical symptoms of dementia emerge. Thus, white matter hyperintensities may be a useful surrogate biomarker for identifying individuals in midlife at risk for future accelerated cognitive decline and selecting participants for dementia prevention trials.
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Affiliation(s)
- Tracy d'Arbeloff
- Department of Psychology & Neuroscience, Duke University, Durham, NC 27708, USA
| | - Maxwell L Elliott
- Department of Psychology & Neuroscience, Duke University, Durham, NC 27708, USA
| | - Annchen R Knodt
- Department of Psychology & Neuroscience, Duke University, Durham, NC 27708, USA
| | - Tracy R Melzer
- New Zealand Brain Research Institute, 66 Stewart Street, Christchurch 8011, New Zealand.,Department of Medicine, University of Otago, 2 Riccarton Avenue, Christchurch 8011, New Zealand
| | - Ross Keenan
- New Zealand Brain Research Institute, 66 Stewart Street, Christchurch 8011, New Zealand.,Christchurch Radiology Group, 6/242 Ferry Road, Waltham, Christchurch 8011, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Tim Anderson
- New Zealand Brain Research Institute, 66 Stewart Street, Christchurch 8011, New Zealand.,Department of Medicine, University of Otago, 2 Riccarton Avenue, Christchurch 8011, New Zealand
| | - Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Durham, NC 27708, USA.,Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London SE5 8AF, UK.,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA
| | - Terrie E Moffitt
- Department of Psychology & Neuroscience, Duke University, Durham, NC 27708, USA.,Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London SE5 8AF, UK.,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA
| | - Ahmad R Hariri
- Department of Psychology & Neuroscience, Duke University, Durham, NC 27708, USA
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27
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Rachmadi MF, Valdés-Hernández MDC, Li H, Guerrero R, Meijboom R, Wiseman S, Waldman A, Zhang J, Rueckert D, Wardlaw J, Komura T. Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images. Comput Med Imaging Graph 2019; 79:101685. [PMID: 31846826 DOI: 10.1016/j.compmedimag.2019.101685] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 09/02/2019] [Accepted: 11/13/2019] [Indexed: 01/29/2023]
Abstract
We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered "normal". Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions' segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.
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Affiliation(s)
- Muhammad Febrian Rachmadi
- School of Informatics, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | | | - Hongwei Li
- Computing, School of Science and Engineering, University of Dundee, Dundee, UK; Department of Informatics, Technical University of Munich, Germany
| | | | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Stewart Wiseman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Adam Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Jianguo Zhang
- Computing, School of Science and Engineering, University of Dundee, Dundee, UK; Department of Computer Science and Engineering, Southern University of Science and Technology, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, China
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Taku Komura
- School of Informatics, University of Edinburgh, Edinburgh, UK
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28
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Yueniwati Y, Wangsadjaja C, Yulidani IG, Rianawati SB, Al Rasyid H. The Role of Brain Magnetic Resonance Imaging (MRI) as an Early Detector of Cognitive Impairment. J Neurosci Rural Pract 2019; 9:350-353. [PMID: 30069090 PMCID: PMC6050795 DOI: 10.4103/jnrp.jnrp_542_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Background: Along with the increase of the health and prosperity level will affect the life expectancy in Indonesia, there has also been an increase in degenerative disease cases. One of the problems arises is cognitive impairment. The mild version of this impairment is often associated with the increase risk that will eventually lead to dementia. Therefore, early detection of this impairment is necessary. Objective: This study is aimed at proving the correlation between Fazekas scale on brain MRI and MoCA-Ina score in defining the degree of cognitive impairment. Methods: This study employed observational analytic design and cross sectional study for its data collection method. The Fazekas scale on brain MRI of 32 patients was read by 3 radiologist, while the MoCA-Ina scoring was done by a competent neurologist. Both tests were done double blindly. Later on, the correlation between Fazekas scale and MoCA-Ina score would be assessed using Spearman Correlation. Results: Statistical calculation conducted using Spearman Correlation reveals that the coefficient is -0.519 with significant score (P) 0.002, which is smaller than α: 0.05. Therefore, it can be concluded that there is a strong negative correlation between Fazekas scale and MoCA-Ina score. Conclusion: Fazekas scale evaluation on brain MRI is necessary to be performed as it helps predicting the decline of one's cognitive function, so that an early therapy can be acted upon to prevent dementia in the future.
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Affiliation(s)
- Yuyun Yueniwati
- Department of Radiology, Faculty of Medicine, University of Brawijaya, Malang, Indonesia
| | - Charles Wangsadjaja
- Department of Radiology, Faculty of Medicine, University of Brawijaya, Malang, Indonesia
| | - Islana Gadis Yulidani
- Department of Radiology, Faculty of Medicine, University of Brawijaya, Malang, Indonesia
| | - Sri Budhi Rianawati
- Department of Neurology, Faculty of Medicine, University of Brawijaya, Malang, Indonesia
| | - Harun Al Rasyid
- Department of Public Health, Faculty of Medicine, University of Brawijaya, Malang, Indonesia
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29
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Huo YC, Li Q, Zhang WY, Zou N, Li R, Huang SY, Wang HQ, Song KY, Zhang RR, Qin XY. Total Small Vessel Disease Burden Predicts Functional Outcome in Patients With Acute Ischemic Stroke. Front Neurol 2019; 10:808. [PMID: 31447754 PMCID: PMC6691043 DOI: 10.3389/fneur.2019.00808] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 07/15/2019] [Indexed: 02/01/2023] Open
Abstract
Background: Cerebral small vessel disease (SVD) is generally considered as a cause of stroke, disability, gait disturbances, vascular cognitive impairment, and dementia. The aim of this study was to investigate whether the total SVD burden can be used to predict functional outcome in patients with acute ischemic stroke. Methods: From April 2017 to January 2018, consecutive patients with acute ischemic stroke who underwent baseline MRI scan were evaluated. The functional outcome was assessed using the modified Rankin Scale (mRS) at 90 days and defined as i) excellent outcome (mRS ≤ 1) and ii) good outcome (mRS ≤ 2). Brain MRI was performed and assessed for lacunes, white matter hyperintensities (WMH), and enlarged perivascular spaces (EPVS). The total SVD burden was calculated based on lacunes, WMH, and EPVS and then summed up to generate an ordinal “total SVD burden” (range 0–3). Bivariate logistic regression models were used to identify the association between SVD and functional outcome. Results: A total of 416 patients were included in the final analysis; 44.0, 33.4, 19.2, and 3.4% of the patients had 0, 1, 2, and 3 features of SVD, respectively. In regard to individual SVD feature, lacunes (OR: 0.48, 95% CI: 0.32–0.71; OR: 0.49, 95% CI: 0.31–0.77) and WMH (OR: 0.53, 95% CI: 0.34–0.82; OR: 0.53, 95% CI: 0.33–0.85) were negatively associated with excellent outcome and good outcome. As to the total burden of SVD, three SVD features had strongest negative associations with functional outcomes (excellent outcome, OR: 0.13, 95% CI: 0.03–0.48; good outcome, OR: 0.18, 95% CI: 0.06–0.54). After adjustment for potential confounders, a high SVD burden (3 features, OR: 0.07, 95% CI: 0.01–0.41) and the score of total SVD burden (OR: 0.64, 95% CI: 0.44–0.93) remained negatively associated with excellent outcome. Conclusion: Total SVD burden negatively associated with functional outcome at 3 months in patients with acute ischemic stroke and is superior to individual SVD feature in prediction of functional outcome. MRI-based assessment of total SVD burden is highly valuable in clinical management of stroke victims and could help guide the allocation of resources to improve outcome.
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Affiliation(s)
- Ying-Chao Huo
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Qi Li
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Wen-Yu Zhang
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Ning Zou
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Rui Li
- Division of Life Sciences and Medicine, Department of Neurology, The First Affiliated Hospital, University of Science and Technology of China, Hefei, China
| | - Si-Yuan Huang
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Hui-Qi Wang
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Kai-Yi Song
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Rong-Rong Zhang
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Xin-Yue Qin
- Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
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Muñoz Maniega S, Meijboom R, Chappell FM, Valdés Hernández MDC, Starr JM, Bastin ME, Deary IJ, Wardlaw JM. Spatial Gradient of Microstructural Changes in Normal-Appearing White Matter in Tracts Affected by White Matter Hyperintensities in Older Age. Front Neurol 2019; 10:784. [PMID: 31404147 PMCID: PMC6673707 DOI: 10.3389/fneur.2019.00784] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 07/08/2019] [Indexed: 01/08/2023] Open
Abstract
Background and Purpose: White matter hyperintensities (WMH) are commonly seen on structural MRI of older adults and are a manifestation of underlying and adjacent tissue damage. WMH may contribute to cortical disconnection and cognitive dysfunction, but it is unclear how WMH affect intersecting or nearby white matter tract integrity. This study investigated the effects of WMH on tract microstructure by determining the spatial distribution of water diffusion characteristics in white matter tract areas adjacent to both intersecting and nearby WMH. Methods: We used diffusion and structural MRI data from 52 representative participants from the Lothian Birth Cohort 1936 (72.2 ± 0.7 years) including a range of WMH burden. We segmented WMH, reconstructed 18 main white mater tracts using automated quantitative tractography and identified intersections between tracts and WMH. We measured mean diffusivity (MD) and fractional anisotropy (FA) in tract tissue at 2 mm incremental distances from tract-intersecting and non-intersecting (nearby) WMH. Results: We observed a spatial gradient of FA and MD abnormalities for most white matter tracts which diminished with a similar distance pattern for tract-intersecting and nearby WMH. Overall, FA was higher, while MD was lower around nearby WMH compared with tract-intersecting WMH. However, for some tracts, FA was lower in areas immediately surrounding nearby WMH, although with faster normalization than in FA values surrounding tract-intersecting WMH. Conclusion: WMH have similar effects on tract infrastructure, whether they be intersecting or nearby. However, the observed differences in tract water diffusion properties around WMH suggest that degenerative processes in small vessel disease may propagate further along the tract for intersecting WMH, while in some areas of the brain there is a larger and more localized accumulation of axonal damage in tract tissue nearby a non-connected WMH. Longitudinal studies should address differential effects of intersecting vs. nearby WMH progression and how they contribute to cognitive aging.
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Affiliation(s)
- Susana Muñoz Maniega
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Rozanna Meijboom
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
- Department of Radiology and Nuclear Medicine, Erasmus MC–University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Francesca M. Chappell
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Maria del C. Valdés Hernández
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - John M. Starr
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E. Bastin
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Ian J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M. Wardlaw
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
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Booth T, Dykiert D, Corley J, Gow AJ, Morris Z, Muñoz Maniega S, Royle NA, Del C Valdés Hernández M, Starr JM, Penke L, Bastin ME, Wardlaw JM, Deary IJ. Reaction time variability and brain white matter integrity. Neuropsychology 2019; 33:642-657. [PMID: 31246073 PMCID: PMC6683973 DOI: 10.1037/neu0000483] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Objective: Mean speed of responding is the most commonly used measure in the assessment of reaction time (RT). An alternative measure is intraindividual variability (IIV): the inconsistency of responding across multiple trials of a test. IIV has been suggested as an important indicator of central nervous system functioning, and as such, there has been increasing interest in the associations between IIV and brain imaging metrics. Results however, have been inconsistent. The present seeks to provide a comprehensive evaluation of the associations between a variety of measures of brain white matter integrity and individual differences in choice RT (CRT) IIV. Method: MRI brain scans of members of the Lothian Birth Cohort 1936 were assessed to obtain measures of the volume and severity of white matter hyperintensities, and the integrity of brain white matter tracts. CRT was assessed with a 4 CRT task on a separate occasion. Data were analyzed using multiple regression (N range = 358–670). Results: Greater volume of hyperintensities and more severe hyperintensities in frontal regions were associated with higher CRT IIV. White matter tract integrity, as assessed by both fractional anisotropy and mean diffusivity, showed the smallest effect sizes in associations with CRT IIV. Associations with hyperintensities were attenuated and no longer significant after controlling for M CRT. Conclusions: Taken together, the results of the present study suggested that IIV was not incrementally predictive of white matter integrity over mean speed. This is in contrast to previous reports, and highlights the need for further study. Variability in speeded cognitive test performance has been argued to be a potential early marker of cognitive decline and progression into mild cognitive impairment in aging. Evidence as to the robustness of the relationship, and the potential neurological underpinnings is varied. Our results suggest that average speeded performance, not variability, may be more reliably related to various measures of the brain. These findings are in contrast to much of the extant literature, highlighting the need for further research.
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Affiliation(s)
- Tom Booth
- Centre for Cognitive Ageing and Cognitive Epidemiology
| | | | - Janie Corley
- Centre for Cognitive Ageing and Cognitive Epidemiology
| | - Alan J Gow
- Centre for Cognitive Ageing and Cognitive Epidemiology
| | - Zoe Morris
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, The University of Edinburgh
| | | | | | | | | | - Lars Penke
- Centre for Cognitive Ageing and Cognitive Epidemiology
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology
| | - Joanna M Wardlaw
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, The University of Edinburgh
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology
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32
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McGrory S, Ballerini L, Doubal FN, Staals J, Allerhand M, Valdes-Hernandez MDC, Wang X, MacGillivray T, Doney ASF, Dhillon B, Starr JM, Bastin ME, Trucco E, Deary IJ, Wardlaw JM. Retinal microvasculature and cerebral small vessel disease in the Lothian Birth Cohort 1936 and Mild Stroke Study. Sci Rep 2019; 9:6320. [PMID: 31004095 PMCID: PMC6474900 DOI: 10.1038/s41598-019-42534-x] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 03/28/2019] [Indexed: 01/06/2023] Open
Abstract
Research has suggested that the retinal vasculature may act as a surrogate marker for diseased cerebral vessels. Retinal vascular parameters were measured using Vessel Assessment and Measurement Platform for Images of the Retina (VAMPIRE) software in two cohorts: (i) community-dwelling older subjects of the Lothian Birth Cohort 1936 (n = 603); and (ii) patients with recent minor ischaemic stroke of the Mild Stroke Study (n = 155). Imaging markers of small vessel disease (SVD) (white matter hyperintensities [WMH] on structural MRI, visual scores and volume; perivascular spaces; lacunes and microbleeds), and vascular risk measures were assessed in both cohorts. We assessed associations between retinal and brain measurements using structural equation modelling and regression analysis. In the Lothian Birth Cohort 1936 arteriolar fractal dimension accounted for 4% of the variance in WMH load. In the Mild Stroke Study lower arteriolar fractal dimension was associated with deep WMH scores (odds ratio [OR] 0.53; 95% CI, 0.32–0.87). No other retinal measure was associated with SVD. Reduced fractal dimension, a measure of vascular complexity, is related to SVD imaging features in older people. The results provide some support for the use of the retinal vasculature in the study of brain microvascular disease.
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Affiliation(s)
- Sarah McGrory
- VAMPIRE project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. .,Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | - Lucia Ballerini
- VAMPIRE project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Fergus N Doubal
- VAMPIRE project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Julie Staals
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands.,Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Mike Allerhand
- Department of Psychology, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | | | - Xin Wang
- VAMPIRE project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Tom MacGillivray
- VAMPIRE project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Alex S F Doney
- Division of Cardiovascular and Diabetes Medicine, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, UK
| | - Baljean Dhillon
- VAMPIRE project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.,Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK.,UK Dementia Research Institute at the University of Edinburgh, Chancellor's Building, Edinburgh, UK
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Ortiz-Ramón R, Valdés Hernández MDC, González-Castro V, Makin S, Armitage PA, Aribisala BS, Bastin ME, Deary IJ, Wardlaw JM, Moratal D. Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images. Comput Med Imaging Graph 2019; 74:12-24. [PMID: 30921550 PMCID: PMC6553681 DOI: 10.1016/j.compmedimag.2019.02.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/11/2019] [Accepted: 02/27/2019] [Indexed: 12/18/2022]
Abstract
Radiomics in conventionally segmented tissues can identify MRI scans that had a stroke. Patient’s advanced age can negatively influence classification results. Feature selection and stroke subtype influence but do not determine accuracy. Stroke subtype cannot be ascertained from texture analysis in brain tissues.
Background The differential quantification of brain atrophy, white matter hyperintensities (WMH) and stroke lesions is important in studies of stroke and dementia. However, the presence of stroke lesions is usually overlooked by automatic neuroimage processing methods and the-state-of-the-art deep learning schemes, which lack sufficient annotated data. We explore the use of radiomics in identifying whether a brain magnetic resonance imaging (MRI) scan belongs to an individual that had a stroke or not. Materials and methods We used 1800 3D sets of MRI data from three prospective studies: one of stroke mechanisms and two of cognitive ageing, evaluated 114 textural features in WMH, cerebrospinal fluid, deep grey and normal-appearing white matter, and attempted to classify the scans using a random forest and support vector machine classifiers with and without feature selection. We evaluated the discriminatory power of each feature independently in each population and corrected the result against Type 1 errors. We also evaluated the influence of clinical parameters in the classification results. Results Subtypes of ischaemic strokes (i.e. lacunar vs. cortical) cannot be discerned using radiomics, but the presence of a stroke-type lesion can be ascertained with accuracies ranging from 0.7 < AUC < 0.83. Feature selection, tissue type, stroke subtype and MRI sequence did not seem to determine the classification results. From all clinical variables evaluated, age correlated with the proportion of images classified correctly using either different or the same descriptors (Pearson r = 0.31 and 0.39 respectively, p < 0.001). Conclusions Texture features in conventionally automatically segmented tissues may help in the identification of the presence of previous stroke lesions on an MRI scan, and should be taken into account in transfer learning strategies of the-state-of-the-art deep learning schemes.
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Affiliation(s)
- Rafael Ortiz-Ramón
- Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain
| | - Maria Del C Valdés Hernández
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
| | - Victor González-Castro
- Department of Electric Systems and Automatics Engineering, Universidad de León, León, Spain
| | - Stephen Makin
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Paul A Armitage
- Department of Cardiovascular Sciences, University of Sheffield, Sheffield, UK
| | - Benjamin S Aribisala
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Department of Computer Science, Lagos State University, Lagos, Nigeria
| | - Mark E Bastin
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - David Moratal
- Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain
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Georgakis MK, Duering M, Wardlaw JM, Dichgans M. WMH and long-term outcomes in ischemic stroke. Neurology 2019; 92:e1298-e1308. [DOI: 10.1212/wnl.0000000000007142] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 11/13/2018] [Indexed: 11/15/2022] Open
Abstract
ObjectiveTo investigate the relationship between baseline white matter hyperintensities (WMH) in patients with ischemic stroke and long-term risk of dementia, functional impairment, recurrent stroke, and mortality.MethodsFollowing the Meta-analysis of Observational Studies in Epidemiology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO protocol: CRD42018092857), we systematically searched Medline and Scopus for cohort studies of ischemic stroke patients examining whether MRI- or CT-assessed WMH at baseline are associated with dementia, functional impairment, recurrent stroke, and mortality at 3 months or later poststroke. We extracted data and evaluated study quality with the Newcastle–Ottawa scale. We pooled relative risks (RR) for the presence and severity of WMH using random-effects models.ResultsWe included 104 studies with 71,298 ischemic stroke patients. Moderate/severe WMH at baseline were associated with increased risk of dementia (RR 2.17, 95% confidence interval [CI] 1.72–2.73), cognitive impairment (RR 2.29, 95% CI 1.48–3.54), functional impairment (RR 2.21, 95% CI 1.83–2.67), any recurrent stroke (RR 1.65, 95% CI 1.36–2.01), recurrent ischemic stroke (RR 1.90, 95% CI 1.26–2.88), all-cause mortality (RR 1.72, 95% CI 1.47–2.01), and cardiovascular mortality (RR 2.02, 95% CI 1.44–2.83). The associations followed dose-response patterns for WMH severity and were consistent for both MRI- and CT-defined WMH. The results remained stable in sensitivity analyses adjusting for age, stroke severity, and cardiovascular risk factors, in analyses of studies scoring high in quality, and in analyses adjusted for publication bias.ConclusionsPresence and severity of WMH are associated with substantially increased risk of dementia, functional impairment, stroke recurrence, and mortality after ischemic stroke. WMH may aid clinical prognostication and the planning of future clinical trials.
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Naomaitai Ameliorated Brain Damage in Rats with Vascular Dementia by PI3K/PDK1/AKT Signaling Pathway. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2019; 2019:2702068. [PMID: 30867669 PMCID: PMC6379870 DOI: 10.1155/2019/2702068] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 11/21/2018] [Accepted: 12/13/2018] [Indexed: 12/30/2022]
Abstract
Background/Aims Naomaitai can improve blood perfusion and ameliorate the damage in the paraventricular white matter. This study was focused on observing the neuroprotective effect of Naomaitai on the vascular dementia of rat and exploring the action mechanism of PI3K/PDK1/AKT signaling pathway. Methods A vascular dementia model of rats was established by permanent, bilateral common carotid artery occlusion. Rats' behavior was tested by Neurological deficit score and the Morris water maze. The pathology and apoptosis were detected through HE staining and TUNEL assay. Myelin sheath loss and nerve fiber damage were detected by LFB staining. Inflammatory factors, oxidative stress, and brain damage markers were detected through ELISA. The expression of apoptosis-related proteins and PI3K/PDK1/AKT signaling pathway related proteins were measured by western blot. The expressions of PI3K, PDK1, AKT, and MBP in paraventricular white matter cells were detected by immunofluorescence. Results Naomaitai treatment decreased neurological function score in rats with vascular dementia, ameliorated paraventricular white matter damage caused by long-term hypoxia, and hypoperfusion reduced the brain injury markers S-100β and NSE contents, suppressed inflammatory reaction and oxidative stress, reduced IL-1β, IL-6, TNF-α, and MDA contents, and remarkably increased IL-10 and SOD contents. TUNEL and western blot assay showed that Naomaitai treatment decreased neuronal cell apoptosis, increased Bcl-2 expression, and reduced caspase-3 and Bax expression. Furthermore, we found Naomaitai inhibited PI3K and PDK1 expression and activated phosphorylated AKT protein in rats with vascular dementia. However, the protective effect of Naomatai in rats with vascular dementia was inhibited, and expression of PI3K signaling pathway-related proteins was blocked after administration of PI3K inhibitor. Conclusion Naomaitai can ameliorate brain damage in rats with vascular dementia, inhibit neuronal apoptosis, and have anti-inflammatory and antioxidative stress effects, which may be regulated by the PI3K/PDK1/AKT signaling pathway.
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Deary IJ, Ritchie SJ, Muñoz Maniega S, Cox SR, Valdés Hernández MC, Luciano M, Starr JM, Wardlaw JM, Bastin ME. Brain Peak Width of Skeletonized Mean Diffusivity (PSMD) and Cognitive Function in Later Life. Front Psychiatry 2019; 10:524. [PMID: 31402877 PMCID: PMC6676305 DOI: 10.3389/fpsyt.2019.00524] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/03/2019] [Indexed: 11/13/2022] Open
Abstract
It is suggested that the brain's peak width of skeletonized water mean diffusivity (PSMD) is a neuro-biomarker of processing speed, an important aspect of cognitive aging. We tested whether PSMD is more strongly correlated with processing speed than with other cognitive domains, and more strongly than other structural brain MRI indices. Participants were 731 Lothian Birth Cohort 1936 members, mean age = 73 years (SD = 0.7); analytical sample was 656-680. Cognitive domains tested were as follows: processing speed (5 tests), visuospatial (3), memory (3), and verbal (3). Brain-imaging variables included PSMD, white matter diffusion parameters, hyperintensity volumes, gray and white matter volumes, and perivascular spaces. PSMD was significantly associated with processing speed (-0.27), visuospatial ability (-0.23), memory ability (-0.17), and general cognitive ability (-0.25); comparable correlations were found with other brain-imaging measures. In a multivariable model with the other imaging variables, PSMD provided independent prediction of visuospatial ability and general cognitive ability. This incremental prediction, coupled with its ease to compute and possibly better tractability, might make PSMD a useful brain biomarker in studies of cognitive aging.
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Affiliation(s)
- Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Stuart J Ritchie
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
| | - Simon R Cox
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
| | - Maria C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Dementia Research Centre, Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Michelle Luciano
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Dementia Research Centre, Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
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Rajani RM, Quick S, Ruigrok SR, Graham D, Harris SE, Verhaaren BFJ, Fornage M, Seshadri S, Atanur SS, Dominiczak AF, Smith C, Wardlaw JM, Williams A. Reversal of endothelial dysfunction reduces white matter vulnerability in cerebral small vessel disease in rats. Sci Transl Med 2018; 10:10/448/eaam9507. [DOI: 10.1126/scitranslmed.aam9507] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 01/31/2018] [Accepted: 06/08/2018] [Indexed: 12/23/2022]
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38
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Stephan Y, Sutin AR, Kornadt A, Caudroit J, Terracciano A. Higher IQ in adolescence is related to a younger subjective age in later life: Findings from the Wisconsin Longitudinal Study. INTELLIGENCE 2018. [DOI: 10.1016/j.intell.2018.06.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Age-related changes in cerebrovascular reactivity and their relationship to cognition: A four-year longitudinal study. Neuroimage 2018; 174:257-262. [PMID: 29567504 DOI: 10.1016/j.neuroimage.2018.03.033] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 02/07/2018] [Accepted: 03/16/2018] [Indexed: 11/20/2022] Open
Abstract
Although cerebrovascular factors are the second leading cause of cognitive impairment and dementia in elderly, the precise spatial and temporal trajectories of vascular decline in aging have not been fully characterized. With an advanced cerebrovascular reactivity (CVR) MRI technique that specifically informs vascular stiffness and dilatory ability of cerebral vessels, we present four-year longitudinal CVR data measured in 116 healthy individuals (20-88 years of age). Our data revealed a spatial heterogeneity in vascular decline in aging (p = 0.003), in that temporal lobe showed the fastest rate of longitudinal CVR decline, followed by parietal and frontal lobes. The rate of CVR decline was also age-dependent. Middle age, not older age, manifested the fastest rate of longitudinal CVR decline (p < 0.05). Longitudinal changes in CVR were associated with changes in processing speed (p = 0.031) and episodic memory (p = 0.022), but not with working memory or reasoning. The rate of longitudinal CVR change was not different between hypertensive and normotensive participants. However, cross-sectionally, individuals with hypertension revealed in a lower CVR compared to normotensive participants (p = 0.016). These findings help elucidate age-related decline in brain hemodynamics and support CVR as a non-invasive biomarker in evaluating cerebrovascular conditions in elderly individuals.
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Abstract
In the face of shifting demographics and an increase in human longevity, it is important to examine carefully what is known about cognitive ageing, and to identify and promote possibly malleable lifestyle and health-related factors that might mitigate age-associated cognitive decline. The Lothian Birth Cohorts of 1921 (LBC1921, n = 550) and 1936 (LBC1936, n = 1091) are longitudinal studies of cognitive and brain ageing based in Scotland. Childhood IQ data are available for these participants, who were recruited in later life and then followed up regularly. This overview summarises some of the main LBC findings to date, illustrating the possible genetic and environmental contributions to cognitive function (level and change) and brain imaging biomarkers in later life. Key associations include genetic variation, health and fitness, psychosocial and lifestyle factors, and aspects of the brain's structure. It addresses some key methodological issues such as confounding by early-life intelligence and social factors and emphasises areas requiring further investigation. Overall, the findings that have emerged from the LBC studies highlight that there are multiple correlates of cognitive ability level in later life, many of which have small effects, that there are as yet few reliable predictors of cognitive change, and that not all of the correlates have independent additive associations. The concept of marginal gains, whereby there might be a cumulative effect of small incremental improvements across a wide range of lifestyle and health-related factors, may offer a useful way to think about and promote a multivariate recipe for healthy cognitive and brain ageing.
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Affiliation(s)
- J Corley
- Department of Psychology,The University of Edinburgh,Edinburgh,UK
| | - S R Cox
- Department of Psychology,The University of Edinburgh,Edinburgh,UK
| | - I J Deary
- Department of Psychology,The University of Edinburgh,Edinburgh,UK
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Liu Y, Zhang M, Chen Y, Gao P, Yun W, Zhou X. The degree of leukoaraiosis predicts clinical outcomes and prognosis in patients with middle cerebral artery occlusion after intravenous thrombolysis. Brain Res 2017; 1681:28-33. [PMID: 29288062 DOI: 10.1016/j.brainres.2017.12.033] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 12/19/2017] [Accepted: 12/21/2017] [Indexed: 10/18/2022]
Abstract
Leukoaraiosis (LA) is common in elderly patients with ischemic stroke on magnetic resonance imaging. In this study, we investigate whether the degree of LA is associated with clinical outcomes and prognosis of patients with middle cerebral artery occlusion following intravenous thrombolytic. Ninety-seven patients were recruited and divided into three groups based on the degree of LA (no, mild and moderate to severe LA) by the Fazekas scale. Clinical outcomes, recurrent stroke, Fugl-Meyer rating scale (FMS) and complications of intravenous thrombolysis were assessed. The association between the degree of LA and functional outcomes was analyzed by multivariable logistic regression model. Patients enrolled were divided into three groups: 26 patients with no LA, 43 patients with mild LA and 28 patients with moderate to severe LA. Impressively, the patients with mild LA were better in early neurological recovery and 90-day FMS score than patients in the other two groups. Multivariate logistic analysis revealed that moderate to severe LA was an independent predictor of poor functional outcome (OR: 10.482; 95% CI: 1.442-76.181; P = .020). Moreover, the patients with moderate to severe LA have a higher rate of hemorrhagic transformation and recurrent stroke as compared with two other groups during 90-day follow-up. Different degrees of LA differentially affect clinical outcome and prognosis in patients with middle cerebral artery occlusion following intravenous thrombolytic. Moderate to severe LA is a risk factor of poor prognosis. Mild LA is associated with early neurological recovery and good motor functional outcome.
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Affiliation(s)
- Yanyan Liu
- Department of Neurology, Laboratory of Neurological Diseases, Changzhou No.2 People's Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Min Zhang
- Department of Neurology, Laboratory of Neurological Diseases, Changzhou No.2 People's Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Yuan Chen
- Department of Neurology, Laboratory of Neurological Diseases, Changzhou No.2 People's Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Ping Gao
- Department of Neurology, Laboratory of Neurological Diseases, Changzhou No.2 People's Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Wenwei Yun
- Department of Neurology, Laboratory of Neurological Diseases, Changzhou No.2 People's Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China.
| | - Xianju Zhou
- Department of Neurology, Laboratory of Neurological Diseases, Changzhou No.2 People's Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China.
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Early life characteristics and late life burden of cerebral small vessel disease in the Lothian Birth Cohort 1936. Aging (Albany NY) 2017; 8:2039-2061. [PMID: 27652981 PMCID: PMC5076451 DOI: 10.18632/aging.101043] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 09/04/2016] [Indexed: 11/25/2022]
Abstract
It is unknown whether relations between early-life factors and overall health in later life apply to burden of cerebral small vessel disease (cSVD), a major cause of stroke and dementia. We explored relations between early-life factors and cSVD in the Lothian Birth Cohort, a healthy aging cohort. Participants were recruited at age 70 (N = 1091); most had completed a test of cognitive ability at age 11 as part of the Scottish Mental Survey of 1947. Of those, 700 participants had brain MRI that could be rated for cSVD conducted at age 73. Presence of lacunes, white matter hyperintensities, microbleeds, and perivascular spaces were summed in a score of 0-4 representing all MRI cSVD features. We tested associations with early-life factors using multivariate logistic regression. Greater SVD score was significantly associated with lower age-11 IQ (OR higher SVD score per SD age-11 IQ = .78, 95%CI 0.65-.95, p=.01). The associations between SVD score and own job class (OR higher job class, .64 95%CI .43-.95, p=.03), age-11 deprivation index (OR per point deprivation score, 1.08, 95%CI 1.00-1.17, p=.04), and education (OR some qualifying education, .60 95%CI .37-.98, p=.04) trended towards significance (p<.05 for all) but did not meet thresholds for multiple testing. No early-life factor was significantly associated with any one individual score component. Early-life factors may contribute to age-73 burden of cSVD. These relations, and the potential for early social interventions to improve brain health, deserve further study.
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Zhou T, Ahmad TK, Gozda K, Truong J, Kong J, Namaka M. Implications of white matter damage in amyotrophic lateral sclerosis (Review). Mol Med Rep 2017; 16:4379-4392. [PMID: 28791401 PMCID: PMC5646997 DOI: 10.3892/mmr.2017.7186] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2017] [Accepted: 06/09/2017] [Indexed: 12/11/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, which involves the progressive degeneration of motor neurons. ALS has long been considered a disease of the grey matter; however, pathological alterations of the white matter (WM), including axonal loss, axonal demyelination and oligodendrocyte death, have been reported in patients with ALS. The present review examined motor neuron death as the primary cause of ALS and evaluated the associated WM damage that is guided by neuronal‑glial interactions. Previous studies have suggested that WM damage may occur prior to the death of motor neurons, and thus may be considered an early indicator for the diagnosis and prognosis of ALS. However, the exact molecular mechanisms underlying early‑onset WM damage in ALS have yet to be elucidated. The present review explored the detailed anatomy of WM and identified several pathological mechanisms that may be implicated in WM damage in ALS. In addition, it associated the pathophysiological alterations of WM, which may contribute to motor neuron death in ALS, with similar mechanisms of WM damage that are involved in multiple sclerosis (MS). Furthermore, the early detection of WM damage in ALS, using neuroimaging techniques, may lead to earlier therapeutic intervention, using immunomodulatory treatment strategies similar to those used in relapsing‑remitting MS, aimed at delaying WM damage in ALS. Early therapeutic approaches may have the potential to delay motor neuron damage and thus prolong the survival of patients with ALS. The therapeutic interventions that are currently available for ALS are only marginally effective. However, early intervention with immunomodulatory drugs may slow the progression of WM damage in the early stages of ALS, thus delaying motor neuron death and increasing the life expectancy of patients with ALS.
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Affiliation(s)
- Ting Zhou
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada
- Department of Human Anatomy and Cell Science, College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
| | - Tina Khorshid Ahmad
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada
| | - Kiana Gozda
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada
| | - Jessica Truong
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada
| | - Jiming Kong
- Department of Human Anatomy and Cell Science, College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
| | - Michael Namaka
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada
- Department of Human Anatomy and Cell Science, College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
- College of Pharmacy, Third Military Medical University, Chongqing 400038, P.R. China
- Department of Medical Rehabilitation, College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada
- Department of Internal Medicine, College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 1R9, Canada
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Zhuang Y, Zeng X, Wang B, Huang M, Gong H, Zhou F. Cortical Surface Thickness in the Middle-Aged Brain with White Matter Hyperintense Lesions. Front Aging Neurosci 2017; 9:225. [PMID: 28769784 PMCID: PMC5511819 DOI: 10.3389/fnagi.2017.00225] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 06/30/2017] [Indexed: 12/27/2022] Open
Abstract
Background and purpose: Previous voxel-based morphometry (VBM) studies have suggested that cortical atrophy is regionally distributed in middle-aged subjects with white matter hyperintense (WMH) lesions. However, few studies have assessed cortical thickness in middle-aged WMH subjects. In this study, we examined cortical thickness as well as cortical morphometry associated with the presence of WMH lesion load in middle-aged subjects. Participants and methods: Thirty-six middle-aged subjects with WMH lesions (WMH group) and without clinical cognitive impairment, and 34 demographically matched healthy control subjects (HCS group) participated in the study. Cortical thickness was estimated using an automated Computational Anatomy Toolbox (CAT12) as the distance between the gray-white matter border and the pial surface. Individual WMH lesions were manually segmented, and WMH loads were measured. Statistical cortical maps were created to estimate differences in cortical thickness between groups based on this cortex-wide analysis. The relationship between WMH lesion loads and cerebral cortical thickness was also analyzed in CAT12. Results: Cortical thickness was significantly lower in the WMH group than in the controls in multimodal integration regions, including the right and left dorsal anterior cingulate cortex (dACC), right and left frontal operculum (fO), right and left operculum parietale (OP), right and left middle temporal gyrus (MTG), and left superior temporal gyrus (STG; P < 0.01, family-wise error (FWE)-corrected). Additionally, cortical thickness was also lower in the recognition regions that contained the right temporal pole (TP), the right and left fusiform gyrus, and the left rolandic operculum (RO; P < 0.01, FWE-corrected). The results revealed that in the left superior parietal lobule (SPL), cortical thickness was higher in the WMH group than in the HCS group (P < 0.01, FWE-corrected). A voxel-wise negative correlation was found between cortical thickness and WMH lesion loads in the right orbitofrontal cortex (OFC), right dorsolateral prefrontal cortex (DLPFC), and right subcallosal cortex (P < 0.01, FWE-corrected). Conclusion: The main findings of this study suggest that middle-aged WMH subjects are more likely to exhibit cortical thinning, especially in multimodal integration and recognition- and motor-related regions. The current morphometry data provide further evidence for WMH-associated structural plasticity.
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Affiliation(s)
- Ying Zhuang
- Department of Oncology, The Second Hospital of Nanchang CityNanchang, China
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital, Nanchang UniversityNanchang, China
| | - Bo Wang
- Department of Radiology, The First Affiliated Hospital, Nanchang UniversityNanchang, China
| | - Muhua Huang
- Department of Radiology, The First Affiliated Hospital, Nanchang UniversityNanchang, China
| | - Honghan Gong
- Department of Radiology, The First Affiliated Hospital, Nanchang UniversityNanchang, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang UniversityNanchang, China
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Backhouse EV, McHutchison CA, Cvoro V, Shenkin SD, Wardlaw JM. Early life risk factors for cerebrovascular disease: A systematic review and meta-analysis. Neurology 2017; 88:976-984. [PMID: 28188307 DOI: 10.1212/wnl.0000000000003687] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 12/14/2016] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Cerebrovascular disease (CVD) causes subclinical brain vascular lesions detected using neuroimaging and childhood factors may increase later CVD risk. METHODS We searched MEDLINE, PsycINFO, and EMBASE, and meta-analyzed all available evidence on childhood (premorbid) IQ, socioeconomic status (SES), education, and subclinical CVD in later life. Overall odds ratios (OR), mean difference or correlation, and 95% confidence intervals (CIs) were calculated using random effects methods. RESULTS We identified 30 relevant studies (n = 22,890). Lower childhood IQ and lower childhood SES were associated with more white matter hyperintensities (WMH) (IQ: n = 1,512, r = -0.07, 95% CI -0.12 to -0.02, p = 0.007; SES: n = 243, deep WMH r = -0.18, periventricular WMH r = -0.146). Fewer years of education were associated with several CVD markers (n = 15,439, OR = 1.17, 95% CI 1.05 to 1.31, p = 0.003). No studies assessed early life factors combined. CONCLUSIONS Childhood IQ, SES, and education are associated with increased risk of CVD on neuroimaging in later life. Further studies are required to provide further evidence and thereby inform policy.
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Affiliation(s)
- Ellen V Backhouse
- From the Centre for Clinical Brain Sciences (E.V.B., C.A.M., V.C., J.M.W.), Centre for Cognitive Ageing and Cognitive Epidemiology (C.A.M., V.C., S.D.S., J.M.W.), and Geriatric Medicine, Department of Clinical and Surgical Sciences (S.D.S.), University of Edinburgh; and Scottish Imaging Network (V.C., S.D.S., J.M.W.), A Platform for Scientific Excellence (SINAPSE), UK
| | - Caroline A McHutchison
- From the Centre for Clinical Brain Sciences (E.V.B., C.A.M., V.C., J.M.W.), Centre for Cognitive Ageing and Cognitive Epidemiology (C.A.M., V.C., S.D.S., J.M.W.), and Geriatric Medicine, Department of Clinical and Surgical Sciences (S.D.S.), University of Edinburgh; and Scottish Imaging Network (V.C., S.D.S., J.M.W.), A Platform for Scientific Excellence (SINAPSE), UK
| | - Vera Cvoro
- From the Centre for Clinical Brain Sciences (E.V.B., C.A.M., V.C., J.M.W.), Centre for Cognitive Ageing and Cognitive Epidemiology (C.A.M., V.C., S.D.S., J.M.W.), and Geriatric Medicine, Department of Clinical and Surgical Sciences (S.D.S.), University of Edinburgh; and Scottish Imaging Network (V.C., S.D.S., J.M.W.), A Platform for Scientific Excellence (SINAPSE), UK
| | - Susan D Shenkin
- From the Centre for Clinical Brain Sciences (E.V.B., C.A.M., V.C., J.M.W.), Centre for Cognitive Ageing and Cognitive Epidemiology (C.A.M., V.C., S.D.S., J.M.W.), and Geriatric Medicine, Department of Clinical and Surgical Sciences (S.D.S.), University of Edinburgh; and Scottish Imaging Network (V.C., S.D.S., J.M.W.), A Platform for Scientific Excellence (SINAPSE), UK
| | - Joanna M Wardlaw
- From the Centre for Clinical Brain Sciences (E.V.B., C.A.M., V.C., J.M.W.), Centre for Cognitive Ageing and Cognitive Epidemiology (C.A.M., V.C., S.D.S., J.M.W.), and Geriatric Medicine, Department of Clinical and Surgical Sciences (S.D.S.), University of Edinburgh; and Scottish Imaging Network (V.C., S.D.S., J.M.W.), A Platform for Scientific Excellence (SINAPSE), UK.
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Heussinger N, Saake M, Mennecke A, Dörr HG, Trollmann R. Variable White Matter Atrophy and Intellectual Development in a Family With X-linked Creatine Transporter Deficiency Despite Genotypic Homogeneity. Pediatr Neurol 2017; 67:45-52. [PMID: 28065824 DOI: 10.1016/j.pediatrneurol.2016.10.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 10/02/2016] [Accepted: 10/08/2016] [Indexed: 11/29/2022]
Abstract
BACKGROUND The X-linked creatine transporter deficiency (CRTD) caused by an SLC6A8 mutation represents the second most common cause of X-linked intellectual disability. The clinical phenotype ranges from mild to severe intellectual disability, epilepsy, short stature, poor language skills, and autism spectrum disorders. The objective of this study was to investigate phenotypic variability in the context of genotype, cerebral creatine concentration, and volumetric analysis in a family with CRTD. PATIENTS AND METHODS The clinical phenotype and manifestations of epilepsy were assessed in a Caucasian family with CRTD. DNA sequencing and creatine metabolism analysis confirmed the diagnosis. Cerebral magnetic resonance imaging (cMRI) with voxel-based morphometry and magnetic resonance spectroscopy was performed in all family members. RESULTS An SLC6A8 missense mutation (c.1169C>T; p.Pro390Leu, exon 8) was detected in four of five individuals. Both male siblings were hemizygous, the mother and the affected sister heterozygous for the mutation. Structural cMRI was normal, whereas voxel-based morphometry analysis showed reduced white matter volume below the first percentile of the reference population of 290 subjects in the more severely affected boy compared with family members and controls. Normalized creatine concentration differed significantly between the individuals (P < 0.005). CONCLUSIONS There is a broad phenotypic variability in CRTD even in family members with the same mutation. Differences in mental development could be related to atrophy of the subcortical white matter.
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Affiliation(s)
- Nicole Heussinger
- Department of Pediatrics, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany.
| | - Marc Saake
- Department of Radiology, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Angelika Mennecke
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Helmuth-Günther Dörr
- Department of Pediatrics, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Regina Trollmann
- Department of Pediatrics, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
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Ryu WS, Woo SH, Schellingerhout D, Jang MU, Park KJ, Hong KS, Jeong SW, Na JY, Cho KH, Kim JT, Kim BJ, Han MK, Lee J, Cha JK, Kim DH, Lee SJ, Ko Y, Cho YJ, Lee BC, Yu KH, Oh MS, Park JM, Kang K, Lee KB, Park TH, Lee J, Choi HK, Lee K, Bae HJ, Kim DE. Stroke outcomes are worse with larger leukoaraiosis volumes. Brain 2016; 140:158-170. [PMID: 28008000 DOI: 10.1093/brain/aww259] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 08/16/2016] [Accepted: 09/02/2016] [Indexed: 12/11/2022] Open
Abstract
Leukoaraiosis or white matter hyperintensities are frequently observed on magnetic resonance imaging of stroke patients. We investigated how white matter hyperintensity volumes affect stroke outcomes, generally and by subtype. In total, 5035 acute ischaemic stroke patients were enrolled. Strokes were classified as large artery atherosclerosis, small vessel occlusion, or cardioembolism. White matter hyperintensity volumes were stratified into quintiles. Mean age (± standard deviation) was 66.3 ± 12.8, 59.6% male. Median (interquartile range) modified Rankin Scale score was 2 (1-3) at discharge and 1 (0-3) at 3 months; 16.5% experienced early neurological deterioration, and 3.3% recurrent stroke. The Cochran-Mantel-Haenszel test with adjustment for age, stroke severity, sex, and thrombolysis status showed that the distributions of 3-month modified Rankin Scale scores differed across white matter hyperintensity quintiles (P < 0.001). Multiple ordinal logistic regression analysis showed that higher white matter hyperintensity quintiles were independently associated with worse 3-month modified Rankin Scale scores; adjusted odds ratios (95% confidence interval) for the second to fifth quintiles versus the first quintile were 1.29 (1.10-1.52), 1.40 (1.18-1.66), 1.69 (1.42-2.02) and 2.03 (1.69-2.43), respectively. For large artery atherosclerosis (39.0%), outcomes varied by white matter hyperintensity volume (P = 0.01, Cochran-Mantel-Haenszel test), and the upper three white matter hyperintensity quintiles (versus the first quintile) had worse 3-month modified Rankin Scale scores; adjusted odds ratios were 1.45 (1.10-1.90), 1.86 (1.41-2.47), and 1.89 (1.41-2.54), respectively. Patients with large artery atherosclerosis were vulnerable to early neurological deterioration (19.4%), and the top two white matter hyperintensity quintiles were more vulnerable still: 23.5% and 22.3%. Moreover, higher white matter hyperintensities were associated with poor modified Rankin Scale improvement: adjusted odds ratios for the upper two quintiles versus the first quintile were 0.66 (0.47-0.94) and 0.62 (0.43-0.89), respectively. For small vessel occlusion (17.8%), outcomes tended to vary by white matter hyperintensitiy volume (P = 0.10, Cochran-Mantel-Haenszel test), and the highest quintile was associated with worse 3-month modified Rankin Scale scores: adjusted odds ratio for the fifth quintile versus first quintile, 1.98 (1.23-3.18). In this subtype, worse white matter hyperintensities were associated with worse National Institute of Health Stroke Scale scores at presentation. For cardioembolism (20.6%), outcomes did not vary significantly by white matter hyperintensity volume (P = 0.19, Cochran-Mantel-Haenszel test); however, the adjusted odds ratio for the highest versus lowest quintiles was 1.62 (1.09-2.40). Regardless of stroke subtype, white matter hyperintensities were not associated with stroke recurrence within 3 months of follow-up. In conclusion, white matter hyperintensity volume independently correlates with stroke outcomes in acute ischaemic stroke. There are some suggestions that stroke outcomes may be affected by leukoaraiosis differentially depending on stroke subtypes, to be confirmed in future investigations.
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Affiliation(s)
- Wi-Sun Ryu
- 1 Stroke Centre and Korean Brain MRI Data Centre, Dongguk University Ilsan Hospital, Korea
| | - Sung-Ho Woo
- 1 Stroke Centre and Korean Brain MRI Data Centre, Dongguk University Ilsan Hospital, Korea
| | - Dawid Schellingerhout
- 2 Departments of Radiology and Cancer Systems Imaging, University of Texas M. D. Anderson Cancer Centre, USA
| | - Min Uk Jang
- 3 Department of Neurology, Chuncheon Sacred Heart Hospital, Korea
| | | | - Keun-Sik Hong
- 5 Department of Neurology, Ilsan Paik Hospital, Korea
| | - Sang-Wuk Jeong
- 1 Stroke Centre and Korean Brain MRI Data Centre, Dongguk University Ilsan Hospital, Korea
| | - Jeong-Yong Na
- 1 Stroke Centre and Korean Brain MRI Data Centre, Dongguk University Ilsan Hospital, Korea
| | - Ki-Hyun Cho
- 6 Department of Neurology, Chonnam National University Hospital, Korea
| | - Joon-Tae Kim
- 6 Department of Neurology, Chonnam National University Hospital, Korea
| | - Beom Joon Kim
- 7 Department of Neurology, Seoul National University Bundang Hospital, Korea
| | - Moon-Ku Han
- 7 Department of Neurology, Seoul National University Bundang Hospital, Korea
| | - Jun Lee
- 8 Department of Neurology, Yeungnam University Hospital, Korea
| | - Jae-Kwan Cha
- 9 Department of Neurology, Dong-A University Hospital, Korea
| | - Dae-Hyun Kim
- 9 Department of Neurology, Dong-A University Hospital, Korea
| | - Soo Joo Lee
- 10 Department of Neurology, Eulji University Hospital, Korea
| | - Youngchai Ko
- 10 Department of Neurology, Eulji University Hospital, Korea
| | - Yong-Jin Cho
- 5 Department of Neurology, Ilsan Paik Hospital, Korea
| | - Byung-Chul Lee
- 11 Department of Neurology, Hallym University Sacred Heart Hospital, Korea
| | - Kyung-Ho Yu
- 11 Department of Neurology, Hallym University Sacred Heart Hospital, Korea
| | - Mi Sun Oh
- 11 Department of Neurology, Hallym University Sacred Heart Hospital, Korea
| | - Jong-Moo Park
- 12 Department of Neurology, Eulji General Hospital, Korea
| | - Kyusik Kang
- 12 Department of Neurology, Eulji General Hospital, Korea
| | - Kyung Bok Lee
- 13 Department of Neurology, Soonchunhyang University Hospital, Korea
| | - Tai Hwan Park
- 14 Department of Neurology, Seoul Medical Centre, Korea
| | - Juneyoung Lee
- 15 Department of Biostatistics, Korea University College of Medicine, Korea
| | | | - Kiwon Lee
- 17 Departments of Neurology and Neurosurgery, The University of Texas Health Science Centre, USA
| | - Hee-Joon Bae
- 7 Department of Neurology, Seoul National University Bundang Hospital, Korea
| | - Dong-Eog Kim
- 1 Stroke Centre and Korean Brain MRI Data Centre, Dongguk University Ilsan Hospital, Korea
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Cox SR, Dickie DA, Ritchie SJ, Karama S, Pattie A, Royle NA, Corley J, Aribisala BS, Valdés Hernández M, Muñoz Maniega S, Starr JM, Bastin ME, Evans AC, Wardlaw JM, Deary IJ. Associations between education and brain structure at age 73 years, adjusted for age 11 IQ. Neurology 2016; 87:1820-1826. [PMID: 27664981 PMCID: PMC5089529 DOI: 10.1212/wnl.0000000000003247] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Accepted: 07/07/2016] [Indexed: 11/20/2022] Open
Abstract
Objective: To investigate how associations between education and brain structure in older age were affected by adjusting for IQ measured at age 11. Methods: We analyzed years of full-time education and measures from an MRI brain scan at age 73 in 617 community-dwelling adults born in 1936. In addition to average and vertex-wise cortical thickness, we measured total brain atrophy and white matter tract fractional anisotropy. Associations between brain structure and education were tested, covarying for sex and vascular health; a second model also covaried for age 11 IQ. Results: The significant relationship between education and average cortical thickness (β = 0.124, p = 0.004) was reduced by 23% when age 11 IQ was included (β = 0.096, p = 0.041). Initial associations between longer education and greater vertex-wise cortical thickness were significant in bilateral temporal, medial-frontal, parietal, sensory, and motor cortices. Accounting for childhood intelligence reduced the number of significant vertices by >90%; only bilateral anterior temporal associations remained. Neither education nor age 11 IQ was significantly associated with total brain atrophy or tract-averaged fractional anisotropy. Conclusions: The association between years of education and brain structure ≈60 years later was restricted to cortical thickness in this sample; however, the previously reported associations between longer education and a thicker cortex are likely to be overestimates in terms of both magnitude and distribution. This finding has implications for understanding, and possibly ameliorating, life-course brain health.
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Affiliation(s)
- Simon R Cox
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria.
| | - David Alexander Dickie
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Stuart J Ritchie
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Sherif Karama
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Alison Pattie
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Natalie A Royle
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Janie Corley
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Benjamin S Aribisala
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Maria Valdés Hernández
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Susana Muñoz Maniega
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - John M Starr
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Mark E Bastin
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Alan C Evans
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Joanna M Wardlaw
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
| | - Ian J Deary
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (S.R.C., S.J.R., A.P., N.A.R., B.S.A., M.V.H., S.M.M., J.M.S., M.E.B., J.M.W., I.J.D.), Department of Psychology (S.R.C., S.J.R., A.P., J.C., I.J.D.), Brain Research Imaging Centre (D.A.D.,N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), Neuroimaging Sciences, Centre for Clinical Brain Sciences, and Alzheimer Scotland Dementia Research Centre (J.M.S.), University of Edinburgh; Scottish Imaging Network (S.R.C., D.A.D., N.A.R., B.S.A., M.V.H., S.M.M., M.E.B., J.M.W.), a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Neurology and Neurosurgery (S.K., A.C.E.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Psychiatry (S.K.), Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada; and Department of Computer Science (B.S.A.), Lagos State University, Lagos, Nigeria
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49
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Henstridge CM, Pickett E, Spires-Jones TL. Synaptic pathology: A shared mechanism in neurological disease. Ageing Res Rev 2016; 28:72-84. [PMID: 27108053 DOI: 10.1016/j.arr.2016.04.005] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 04/18/2016] [Accepted: 04/19/2016] [Indexed: 12/18/2022]
Abstract
Synaptic proteomes have evolved a rich and complex diversity to allow the exquisite control of neuronal communication and information transfer. It is therefore not surprising that many neurological disorders are associated with alterations in synaptic function. As technology has advanced, our ability to study the anatomical and physiological function of synapses in greater detail has revealed a critical role for both central and peripheral synapses in neurodegenerative disease. Synapse loss has a devastating effect on cellular communication, leading to wide ranging effects such as network disruption within central neural systems and muscle wastage in the periphery. These devastating effects link synaptic pathology to a diverse range of neurological disorders, spanning Alzheimer's disease to multiple sclerosis. This review will highlight some of the current literature on synaptic integrity in animal models of disease and human post-mortem studies. Synaptic changes in normal brain ageing will also be discussed and finally the current and prospective treatments for neurodegenerative disorders will be summarised.
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Affiliation(s)
| | - Eleanor Pickett
- Centre for Cognitive and Neural Systems, 1 George Square, University of Edinburgh, EH8 9JZ, UK
| | - Tara L Spires-Jones
- Centre for Cognitive and Neural Systems, 1 George Square, University of Edinburgh, EH8 9JZ, UK; Euan MacDonald Centre for Motor Neurone Disease Research, Chancellor's Building, 49 Little France Crescent, University of Edinburgh, EH16 4SB, UK; Centre for Dementia Prevention, University of Edinburgh Kennedy Tower, Royal Edinburgh Hospital, EH10 5HF, UK.
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50
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Peng Y, Li S, Zhuang Y, Liu X, Wu L, Gong H, Liu D, Zhou F. Density abnormalities in normal-appearing gray matter in the middle-aged brain with white matter hyperintense lesions: a DARTEL-enhanced voxel-based morphometry study. Clin Interv Aging 2016; 11:615-22. [PMID: 27274211 PMCID: PMC4869624 DOI: 10.2147/cia.s98409] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Background and purpose Little is known about the structural alterations within gray matter (GM) in middle-aged subjects with white matter hyperintense (WMH) lesions. Here, we aimed to examine the anatomical changes within the GM and their relationship to WMH lesion loads in middle-aged subjects. Participants and methods Twenty-three middle-aged subjects with WMH lesions (WMH group) and 23 demographically matched healthy control subjects participated in the study. A Diffeomorphic Anatomical Registration Through Exponentiated Liealgebra-enhanced voxel-based morphometry was used to measure the GM density, and the correlations between WMH lesion volume and extracted GM values in abnormal regions were identified by voxel-based morphometry analysis. Results Compared with the healthy control subjects, the WMH group had a significantly decreased GM density in the left middle frontal gyrus, bilateral anterior cingulate cortex, left and right premotor cortex, and left and right middle cingulate cortex and an increased GM density in the bilateral cerebellum anterior lobe, left middle temporal gyrus, right temporoparietal junction, left and right prefrontal cortex (PFC), and left inferior parietal lobule. A relationship was observed between the normalized WMH lesion volume and the decreased GM density, including the left middle frontal gyrus (ρ=−0.629, P=0.002), bilateral anterior cingulate cortex (ρ=−0.507, P=0.019), right middle cingulate cortex (ρ=−0.484, P=0.026), and right premotor cortex (ρ=−0.438, P=0.047). The WMH lesion loads also negatively correlated with increased GM density in the right temporoparietal junction (ρ=−0.484, P=0.026), left PFC (ρ=−0.469, P=0.032), and right PFC (ρ=−0.438, P=0.047). Conclusion We observed that lesion load-associated structural plasticity corresponds to bidirectional changes in regional GM density in the WMH group.
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Affiliation(s)
- Yan Peng
- Burn Center, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi Province, People's Republic of China
| | - Shenhong Li
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi Province, People's Republic of China
| | - Ying Zhuang
- Department of Oncology, The Second Hospital of Nanchang, Nanchang, Jiangxi Province, People's Republic of China
| | - Xiaojia Liu
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, People's Republic of China
| | - Lin Wu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi Province, People's Republic of China
| | - Honghan Gong
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi Province, People's Republic of China
| | - Dewu Liu
- Burn Center, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi Province, People's Republic of China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi Province, People's Republic of China
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