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Biesbroek JM, Weaver NA, Aben HP, Kuijf HJ, Abrigo J, Bae HJ, Barbay M, Best JG, Bordet R, Chappell FM, Chen CPLH, Dondaine T, van der Giessen RS, Godefroy O, Gyanwali B, Hamilton OKL, Hilal S, Huenges Wajer IMC, Kang Y, Kappelle LJ, Kim BJ, Köhler S, de Kort PLM, Koudstaal PJ, Kuchcinski G, Lam BYK, Lee BC, Lee KJ, Lim JS, Lopes R, Makin SDJ, Mendyk AM, Mok VCT, Oh MS, van Oostenbrugge RJ, Roussel M, Shi L, Staals J, Valdés-Hernández MDC, Venketasubramanian N, Verhey FRJ, Wardlaw JM, Werring DJ, Xin X, Yu KH, van Zandvoort MJE, Zhao L, Biessels GJ. Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke. Neuroimage Clin 2022; 34:103018. [PMID: 35504223 PMCID: PMC9079101 DOI: 10.1016/j.nicl.2022.103018] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/14/2022] [Accepted: 04/22/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. AIMS To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. METHODS We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3-12, 12-24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. RESULTS We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3-12 months, 243/853 (28%) at 12-24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. CONCLUSIONS The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.
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Affiliation(s)
- J Matthijs Biesbroek
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, the Netherlands.
| | - Nick A Weaver
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, the Netherlands
| | - Hugo P Aben
- Department of Neurology, Elisabeth Tweesteden Hospital, Tilburg, the Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jill Abrigo
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Mélanie Barbay
- Department of Neurology, Amiens University Hospital, Laboratory of Functional Neurosciences (UR UPJV 4559), Jules Verne Picardy University, 80054 Amiens Cedex, France
| | - Jonathan G Best
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Russell Square House, 10 - 12 Russell Square, London WC1B 5EH, UK
| | - Régis Bordet
- Université Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Francesca M Chappell
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Christopher P L H Chen
- Department of Pharmacology, National University of Singapore, Singapore, Singapore; Memory, Aging and Cognition Center, National University Health System, Singapore, Singapore
| | - Thibaut Dondaine
- Université Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | | | - Olivier Godefroy
- Department of Neurology, Amiens University Hospital, Laboratory of Functional Neurosciences (UR UPJV 4559), Jules Verne Picardy University, 80054 Amiens Cedex, France
| | - Bibek Gyanwali
- Department of Pharmacology, National University of Singapore, Singapore, Singapore; Memory, Aging and Cognition Center, National University Health System, Singapore, Singapore
| | - Olivia K L Hamilton
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore, Singapore; Memory, Aging and Cognition Center, National University Health System, Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Irene M C Huenges Wajer
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, the Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, the Netherlands
| | - Yeonwook Kang
- Department of Psychology, Hallym University, Chuncheon, South Korea
| | - L Jaap Kappelle
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, the Netherlands
| | - Beom Joon Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Sebastian Köhler
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Paul L M de Kort
- Department of Neurology, Elisabeth Tweesteden Hospital, Tilburg, the Netherlands
| | - Peter J Koudstaal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Gregory Kuchcinski
- Université Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Bonnie Y K Lam
- Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Byung-Chul Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Keon-Joo Lee
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Renaud Lopes
- Université Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | | | - Anne-Marie Mendyk
- Université Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Mi Sun Oh
- Department of Neurology, Hallym University Sacred Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, South Korea
| | | | - Martine Roussel
- Department of Neurology, Amiens University Hospital, Laboratory of Functional Neurosciences (UR UPJV 4559), Jules Verne Picardy University, 80054 Amiens Cedex, France
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China; BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Julie Staals
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Maria Del C Valdés-Hernández
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | | | - Frans R J Verhey
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Joanna M Wardlaw
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - David J Werring
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Russell Square House, 10 - 12 Russell Square, London WC1B 5EH, UK
| | - Xu Xin
- Department of Pharmacology, National University of Singapore, Singapore, Singapore; Memory, Aging and Cognition Center, National University Health System, Singapore, Singapore
| | - Kyung-Ho Yu
- Department of Neurology, Hallym University Sacred Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, South Korea
| | - Martine J E van Zandvoort
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, the Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, the Netherlands
| | - Lei Zhao
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, the Netherlands
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Weaver NA, Kuijf HJ, Aben HP, Abrigo J, Bae HJ, Barbay M, Best JG, Bordet R, Chappell FM, Chen CPLH, Dondaine T, van der Giessen RS, Godefroy O, Gyanwali B, Hamilton OKL, Hilal S, Huenges Wajer IMC, Kang Y, Kappelle LJ, Kim BJ, Köhler S, de Kort PLM, Koudstaal PJ, Kuchcinski G, Lam BYK, Lee BC, Lee KJ, Lim JS, Lopes R, Makin SDJ, Mendyk AM, Mok VCT, Oh MS, van Oostenbrugge RJ, Roussel M, Shi L, Staals J, Del C Valdés-Hernández M, Venketasubramanian N, Verhey FRJ, Wardlaw JM, Werring DJ, Xin X, Yu KH, van Zandvoort MJE, Zhao L, Biesbroek JM, Biessels GJ. Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts. Lancet Neurol 2021; 20:448-459. [PMID: 33901427 DOI: 10.1016/s1474-4422(21)00060-0] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 01/24/2021] [Accepted: 02/12/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND Post-stroke cognitive impairment (PSCI) occurs in approximately half of people in the first year after stroke. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations predictive of PSCI is unavailable. We aimed to identify infarct locations most strongly predictive of PSCI after acute ischaemic stroke and use this information to develop a prediction model. METHODS In this large-scale multicohort lesion-symptom mapping study, we pooled and harmonised individual patient data from 12 cohorts through the Meta-analyses on Strategic Lesion Locations for Vascular Cognitive Impairment using Lesion-Symptom Mapping (Meta VCI Map) consortium. The identified cohorts (as of Jan 1, 2019) comprised patients with acute symptomatic infarcts on CT or MRI (with available infarct segmentations) and a cognitive assessment up to 15 months after acute ischaemic stroke onset. PSCI was defined as performance lower than the fifth percentile of local normative data, on at least one cognitive domain on a multidomain neuropsychological assessment or on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios (ORs) for PSCI that were mapped onto a three-dimensional brain template to visualise PSCI risk per location. For the prediction model of PSCI risk, a location impact score on a 5-point scale was derived from the VLSM results on the basis of the mean voxel-wise coefficient (ln[OR]) within each patient's infarct. We did combined internal-external validation by leave-one-cohort-out cross-validation for all 12 cohorts using logistic regression. Predictive performance of a univariable model with only the location impact score was compared with a multivariable model with addition of other clinical PSCI predictors (age, sex, education, time interval between stroke onset and cognitive assessment, history of stroke, and total infarct volume). Testing of visual ratings was done by three clinicians, and accuracy, inter-rater reliability, and intra-rater reliability were assessed with Cohen's weighted kappa. FINDINGS In our sample of 2950 patients (mean age 66·8 years [SD 11·6]; 1157 [39·2%] women), 1286 (43·6%) had PSCI. We achieved high lesion coverage of the brain in our analyses (86·9%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (after false discovery rate correction, q<0·01; voxel-wise ORs >20). On cross-validation, the location impact score showed good correspondence, based on visual assessment of goodness of fit, between predicted and observed risk of PSCI across cohorts after adjusting for cohort-specific PSCI occurrence. Cross-validations showed that the location impact score by itself had similar performance to the combined model with other PSCI predictors, while allowing for easy visual assessment. Therefore the univariable model with only the location impact score was selected as the final model. Correspondence between visual ratings and actual location impact score (Cohen's weighted kappa: range 0·88-0·92), inter-rater agreement (0·85-0·87), and intra-rater agreement (for a single rater, 0·95) were all high. INTERPRETATION To the best of our knowledge, this study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts. Furthermore, we developed a quick and reliable visual rating scale that might in the future be applied by clinicians to identify individual patients at risk of PSCI. FUNDING The Netherlands Organisation for Health Research and Development.
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Affiliation(s)
- Nick A Weaver
- Department of Neurology and Neurosurgery, University Medical Centre (UMC) Utrecht Brain Center, Utrecht, Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, UMC Utrecht, Utrecht, Netherlands
| | - Hugo P Aben
- Department of Neurology, Elisabeth Tweesteden Hospital, Tilburg, Netherlands
| | - Jill Abrigo
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Mélanie Barbay
- Department of Neurology, Amiens University Hospital, Laboratory of Functional Neurosciences, Jules Verne Picardy University, Amiens, France
| | - Jonathan G Best
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, University College London Queen Square Institute of Neurology, London, UK
| | - Régis Bordet
- Université Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience and Cognition, Lille, France
| | - Francesca M Chappell
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Christopher P L H Chen
- Department of Pharmacology, National University of Singapore, Singapore; Memory, Aging and Cognition Center, National University Health System, Singapore
| | - Thibaut Dondaine
- Université Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience and Cognition, Lille, France
| | | | - Olivier Godefroy
- Department of Neurology, Amiens University Hospital, Laboratory of Functional Neurosciences, Jules Verne Picardy University, Amiens, France
| | - Bibek Gyanwali
- Department of Pharmacology, National University of Singapore, Singapore; Memory, Aging and Cognition Center, National University Health System, Singapore
| | - Olivia K L Hamilton
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore; Memory, Aging and Cognition Center, National University Health System, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Irene M C Huenges Wajer
- Department of Neurology and Neurosurgery, University Medical Centre (UMC) Utrecht Brain Center, Utrecht, Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands
| | - Yeonwook Kang
- Department of Psychology, Hallym University, Chuncheon, South Korea
| | - L Jaap Kappelle
- Department of Neurology and Neurosurgery, University Medical Centre (UMC) Utrecht Brain Center, Utrecht, Netherlands
| | - Beom Joon Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Sebastian Köhler
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Paul L M de Kort
- Department of Neurology, Elisabeth Tweesteden Hospital, Tilburg, Netherlands
| | - Peter J Koudstaal
- Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Gregory Kuchcinski
- Université Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience and Cognition, Lille, France
| | - Bonnie Y K Lam
- Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Byung-Chul Lee
- Department of Neurology, Hallym University Sacred Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, South Korea
| | - Keon-Joo Lee
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, Seoul, South Korea
| | - Renaud Lopes
- Université Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience and Cognition, Lille, France
| | - Stephen D J Makin
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Anne-Marie Mendyk
- Université Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience and Cognition, Lille, France
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mi Sun Oh
- Department of Neurology, Hallym University Sacred Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, South Korea
| | | | - Martine Roussel
- Department of Neurology, Amiens University Hospital, Laboratory of Functional Neurosciences, Jules Verne Picardy University, Amiens, France
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; BrainNow Research Institute, Shenzhen, China
| | - Julie Staals
- Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Maria Del C Valdés-Hernández
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | | | - Frans R J Verhey
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Joanna M Wardlaw
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - David J Werring
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, University College London Queen Square Institute of Neurology, London, UK
| | - Xu Xin
- Department of Pharmacology, National University of Singapore, Singapore; Memory, Aging and Cognition Center, National University Health System, Singapore
| | - Kyung-Ho Yu
- Department of Neurology, Hallym University Sacred Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, South Korea
| | - Martine J E van Zandvoort
- Department of Neurology and Neurosurgery, University Medical Centre (UMC) Utrecht Brain Center, Utrecht, Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands
| | - Lei Zhao
- BrainNow Research Institute, Shenzhen, China
| | - J Matthijs Biesbroek
- Department of Neurology and Neurosurgery, University Medical Centre (UMC) Utrecht Brain Center, Utrecht, Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, University Medical Centre (UMC) Utrecht Brain Center, Utrecht, Netherlands.
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Rachmadi MF, Valdés-Hernández MDC, Makin S, Wardlaw J, Komura T. Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks. Med Image Anal 2020; 63:101712. [PMID: 32428823 PMCID: PMC7294240 DOI: 10.1016/j.media.2020.101712] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 04/10/2020] [Accepted: 04/20/2020] [Indexed: 11/24/2022]
Abstract
Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the model's architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.
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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.
| | | | - Stephen Makin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Rural Health, University of Aberdeen, 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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Jian X, Satizabal CL, Smith AV, Wittfeld K, Bis JC, Smith JA, Hsu FC, Nho K, Hofer E, Hagenaars SP, Nyquist PA, Mishra A, Adams HHH, Li S, Teumer A, Zhao W, Freedman BI, Saba Y, Yanek LR, Chauhan G, van Buchem MA, Cushman M, Royle NA, Bryan RN, Niessen WJ, Windham BG, DeStefano AL, Habes M, Heckbert SR, Palmer ND, Lewis CE, Eiriksdottir G, Maillard P, Mathias RA, Homuth G, Valdés-Hernández MDC, Divers J, Beiser AS, Langner S, Rice KM, Bastin ME, Yang Q, Maldjian JA, Starr JM, Sidney S, Risacher SL, Uitterlinden AG, Gudnason VG, Nauck M, Rotter JI, Schreiner PJ, Boerwinkle E, van Duijn CM, Mazoyer B, von Sarnowski B, Gottesman RF, Levy D, Sigurdsson S, Vernooij MW, Turner ST, Schmidt R, Wardlaw JM, Psaty BM, Mosley TH, DeCarli CS, Saykin AJ, Bowden DW, Becker DM, Deary IJ, Schmidt H, Kardia SLR, Ikram MA, Debette S, Grabe HJ, Longstreth WT, Seshadri S, Launer LJ, Fornage M. Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging. Stroke 2019; 49:1812-1819. [PMID: 30002152 DOI: 10.1161/strokeaha.118.020689] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10-7). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; PEA=4.5×10-8) partially independent of known common signal ( PEA(conditional)=1.4×10-3). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; Pall=1.9×10-10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( Prs34136221=2.8×10-8). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.
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Affiliation(s)
- Xueqiu Jian
- From the Institute of Molecular Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston (M.F., X.J.)
| | - Claudia L Satizabal
- Department of Neurology, Boston University School of Medicine, MA (C.L.S., S. Seshadri)
| | - Albert V Smith
- Icelandic Heart Association, Kópavogur, Iceland (A.V.S., G.E., S. Sigurdsson, V.G.G.)
| | - Katharina Wittfeld
- German Center for Neurodegenerative Diseases, Site Rostock/Greifswald, Germany (K.W.)
| | - Joshua C Bis
- Cardiovascular Health Research Unit (B.M.P., J.C.B., S.R.H.)
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (J.A.S., S.L.R.K., W.Z.)
| | - Fang-Chi Hsu
- Division of Public Health Sciences (F.-C.H., J.D.)
| | - Kwangsik Nho
- Center for Neuroimaging, Indiana University School of Medicine, Indianapolis (K.N., S.L.R.)
| | | | - Saskia P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, United Kingdom (I.J.D., J.M.W., J.M.S., M.d.C.V.-H., M.E.B., N.A.R., S.P.H.)
| | - Paul A Nyquist
- Department of Neurology and Neurosurgery (P.A.N., R.F.G.)
| | - Aniket Mishra
- Bordeaux Population Health Research Centre U1219, Inserm, France (A.M., G.C., S.D.)
| | | | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, MA (A.S.B., A.L.D., Q.Y., S.L.)
| | | | - Wei Zhao
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (J.A.S., S.L.R.K., W.Z.)
| | | | - Yasaman Saba
- Institute of Molecular Biology and Biochemistry (H.S., Y.S.), Medical University of Graz, Austria
| | - Lisa R Yanek
- Department of Medicine (D.M.B., L.R.Y., R.A.M.), Johns Hopkins School of Medicine, Baltimore, MD
| | - Ganesh Chauhan
- Bordeaux Population Health Research Centre U1219, Inserm, France (A.M., G.C., S.D.)
| | - Mark A van Buchem
- Department of Radiology, Leiden University Medical Center, the Netherlands (M.A.v.B.)
| | - Mary Cushman
- Department of Medicine, The University of Vermont Larner College of Medicine, Burlington (M.C.)
| | - Natalie A Royle
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, United Kingdom (I.J.D., J.M.W., J.M.S., M.d.C.V.-H., M.E.B., N.A.R., S.P.H.)
| | - R Nick Bryan
- Department of Diagnostic Medicine, Dell Medical School at The University of Texas at Austin (R.N.B.)
| | - Wiro J Niessen
- Departments of Radiology and Medical Informatics (W.J.N.).,Department of Medicine, The University of Mississippi School of Medicine, Jackson (W.J.N.)
| | | | - Anita L DeStefano
- Department of Biostatistics, Boston University School of Public Health, MA (A.S.B., A.L.D., Q.Y., S.L.)
| | - Mohamad Habes
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia (M.H.)
| | | | - Nicholette D Palmer
- Department of Biochemistry (D.W.B., N.D.P.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Cora E Lewis
- Department of Epidemiology, The University of Alabama at Birmingham School of Public Health (C.E.L.)
| | - Gudny Eiriksdottir
- Icelandic Heart Association, Kópavogur, Iceland (A.V.S., G.E., S. Sigurdsson, V.G.G.)
| | - Pauline Maillard
- Department of Neurology, UC Davis School of Medicine (C.S.D., P.M.), CA
| | - Rasika A Mathias
- Department of Medicine (D.M.B., L.R.Y., R.A.M.), Johns Hopkins School of Medicine, Baltimore, MD
| | - Georg Homuth
- Institute of Genetics and Functional Genomics, University of Greifswald, Germany (G.H.)
| | - Maria Del C Valdés-Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, United Kingdom (I.J.D., J.M.W., J.M.S., M.d.C.V.-H., M.E.B., N.A.R., S.P.H.)
| | | | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, MA (A.S.B., A.L.D., Q.Y., S.L.)
| | - Sönke Langner
- Institute for Diagnostic Radiology and Neuroradiology (S.L.)
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington School of Public Health, Seattle (K.M.R.)
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, United Kingdom (I.J.D., J.M.W., J.M.S., M.d.C.V.-H., M.E.B., N.A.R., S.P.H.)
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, MA (A.S.B., A.L.D., Q.Y., S.L.)
| | - Joseph A Maldjian
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas (J.A.M.)
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, United Kingdom (I.J.D., J.M.W., J.M.S., M.d.C.V.-H., M.E.B., N.A.R., S.P.H.)
| | - Stephen Sidney
- Division of Research, Kaiser Permanente Northern California, Oakland (S. Sidney)
| | - Shannon L Risacher
- Center for Neuroimaging, Indiana University School of Medicine, Indianapolis (K.N., S.L.R.)
| | | | - Vilmundur G Gudnason
- Icelandic Heart Association, Kópavogur, Iceland (A.V.S., G.E., S. Sigurdsson, V.G.G.)
| | - Matthias Nauck
- Institute for Clinical Chemistry and Laboratory Medicine (M.N.)
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Harbor-UCLA Medical Center, Torrance, CA (J.I.R.)
| | - Pamela J Schreiner
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis (P.J.S.)
| | - Eric Boerwinkle
- Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (E.B.)
| | | | - Bernard Mazoyer
- Neurodegeneratives Diseases Institute-CNRS UMR 5293 (B.M.), University of Bordeaux, France
| | | | | | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD (D.L.)
| | - Sigurdur Sigurdsson
- Icelandic Heart Association, Kópavogur, Iceland (A.V.S., G.E., S. Sigurdsson, V.G.G.)
| | | | - Stephen T Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN (S.T.T.)
| | | | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, United Kingdom (I.J.D., J.M.W., J.M.S., M.d.C.V.-H., M.E.B., N.A.R., S.P.H.)
| | - Bruce M Psaty
- Cardiovascular Health Research Unit (B.M.P., J.C.B., S.R.H.)
| | | | - Charles S DeCarli
- Department of Neurology, UC Davis School of Medicine (C.S.D., P.M.), CA
| | | | - Donald W Bowden
- Department of Biochemistry (D.W.B., N.D.P.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Diane M Becker
- Department of Medicine (D.M.B., L.R.Y., R.A.M.), Johns Hopkins School of Medicine, Baltimore, MD
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, United Kingdom (I.J.D., J.M.W., J.M.S., M.d.C.V.-H., M.E.B., N.A.R., S.P.H.)
| | - Helena Schmidt
- Institute of Molecular Biology and Biochemistry (H.S., Y.S.), Medical University of Graz, Austria
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (J.A.S., S.L.R.K., W.Z.)
| | - M Arfan Ikram
- Departments of Epidemiology, Radiology and Neurology (M.A.I.), Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Stéphanie Debette
- Bordeaux Population Health Research Centre U1219, Inserm, France (A.M., G.C., S.D.)
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy (H.J.G.), University Medicine Greifswald, Germany
| | - W T Longstreth
- Departments of Neurology and Epidemiology (W.T.L.), University of Washington, Seattle, WA
| | - Sudha Seshadri
- Department of Neurology, Boston University School of Medicine, MA (C.L.S., S. Seshadri)
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, MD (L.J.L.)
| | - Myriam Fornage
- From the Institute of Molecular Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston (M.F., X.J.)
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